modelId
stringlengths 4
81
| tags
list | pipeline_tag
stringclasses 17
values | config
dict | downloads
int64 0
59.7M
| first_commit
timestamp[ns, tz=UTC] | card
stringlengths 51
438k
|
|---|---|---|---|---|---|---|
dccuchile/albert-xxlarge-spanish
|
[
"pytorch",
"tf",
"albert",
"pretraining",
"es",
"dataset:large_spanish_corpus",
"transformers",
"spanish",
"OpenCENIA"
] | null |
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"AlbertForPreTraining"
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| 42 | null |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.50 +/- 2.74
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="thuyentruong/Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
dccuchile/bert-base-spanish-wwm-uncased-finetuned-mldoc
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
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"BertForSequenceClassification"
],
"model_type": "bert",
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| 39 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: 20230430-001-baseline-mbert-qa-squadv2-ft-clickbait-spoiling
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 20230430-001-baseline-mbert-qa-squadv2-ft-clickbait-spoiling
This model is a fine-tuned version of [intanm/mbert-squadv2](https://huggingface.co/intanm/mbert-squadv2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 4.8747
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 200 | 2.8065 |
| No log | 2.0 | 400 | 2.8088 |
| 2.5698 | 3.0 | 600 | 3.1652 |
| 2.5698 | 4.0 | 800 | 3.5464 |
| 1.1384 | 5.0 | 1000 | 3.8477 |
| 1.1384 | 6.0 | 1200 | 4.1725 |
| 1.1384 | 7.0 | 1400 | 4.5057 |
| 0.4763 | 8.0 | 1600 | 4.7721 |
| 0.4763 | 9.0 | 1800 | 4.8970 |
| 0.2594 | 10.0 | 2000 | 4.8747 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
dccuchile/bert-base-spanish-wwm-uncased-finetuned-pos
|
[
"pytorch",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] |
token-classification
|
{
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"BertForTokenClassification"
],
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| 5 | null |
---
language:
- af
- am
- ar
- as
- az
- be
- bg
- bn
- bo
- bs
- ca
- ceb
- co
- cs
- cy
- da
- de
- el
- en
- eo
- es
- et
- eu
- fa
- fi
- fr
- fy
- ga
- gd
- gl
- gu
- ha
- haw
- he
- hi
- hmn
- hr
- ht
- hu
- hy
- id
- ig
- is
- it
- ja
- jv
- ka
- kk
- km
- kn
- ko
- ku
- ky
- la
- lb
- lo
- lt
- lv
- mg
- mi
- mk
- ml
- mn
- mr
- ms
- mt
- my
- ne
- nl
- no
- ny
- or
- pa
- pl
- pt
- ro
- ru
- rw
- si
- sk
- sl
- sm
- sn
- so
- sq
- sr
- st
- su
- sv
- sw
- ta
- te
- tg
- th
- tk
- tl
- tr
- tt
- ug
- uk
- ur
- uz
- vi
- wo
- xh
- yi
- yo
- zh
- zu
tags:
- bert
- sentence_embedding
- multilingual
- google
- sentence-similarity
license: apache-2.0
datasets:
- CommonCrawl
- Wikipedia
---
Copy of setu4993/LaBSE that returns the sentence embeddings (pooler_output) and implements caching
Original Model Card:
# LaBSE
## Model description
Language-agnostic BERT Sentence Encoder (LaBSE) is a BERT-based model trained for sentence embedding for 109 languages. The pre-training process combines masked language modeling with translation language modeling. The model is useful for getting multilingual sentence embeddings and for bi-text retrieval.
- Model: [HuggingFace's model hub](https://huggingface.co/setu4993/LaBSE).
- Paper: [arXiv](https://arxiv.org/abs/2007.01852).
- Original model: [TensorFlow Hub](https://tfhub.dev/google/LaBSE/2).
- Blog post: [Google AI Blog](https://ai.googleblog.com/2020/08/language-agnostic-bert-sentence.html).
- Conversion from TensorFlow to PyTorch: [GitHub](https://github.com/setu4993/convert-labse-tf-pt).
This is migrated from the v2 model on the TF Hub, which uses dict-based input. The embeddings produced by both the versions of the model are [equivalent](https://github.com/setu4993/convert-labse-tf-pt/blob/ec3a019159a54ed6493181a64486c2808c01f216/tests/test_conversion.py#L31).
## Usage
Using the model:
```python
import torch
from transformers import BertModel, BertTokenizerFast
tokenizer = BertTokenizerFast.from_pretrained("setu4993/LaBSE")
model = BertModel.from_pretrained("setu4993/LaBSE")
model = model.eval()
english_sentences = [
"dog",
"Puppies are nice.",
"I enjoy taking long walks along the beach with my dog.",
]
english_inputs = tokenizer(english_sentences, return_tensors="pt", padding=True)
with torch.no_grad():
english_outputs = model(**english_inputs)
```
To get the sentence embeddings, use the pooler output:
```python
english_embeddings = english_outputs.pooler_output
```
Output for other languages:
```python
italian_sentences = [
"cane",
"I cuccioli sono carini.",
"Mi piace fare lunghe passeggiate lungo la spiaggia con il mio cane.",
]
japanese_sentences = ["犬", "子犬はいいです", "私は犬と一緒にビーチを散歩するのが好きです"]
italian_inputs = tokenizer(italian_sentences, return_tensors="pt", padding=True)
japanese_inputs = tokenizer(japanese_sentences, return_tensors="pt", padding=True)
with torch.no_grad():
italian_outputs = model(**italian_inputs)
japanese_outputs = model(**japanese_inputs)
italian_embeddings = italian_outputs.pooler_output
japanese_embeddings = japanese_outputs.pooler_output
```
For similarity between sentences, an L2-norm is recommended before calculating the similarity:
```python
import torch.nn.functional as F
def similarity(embeddings_1, embeddings_2):
normalized_embeddings_1 = F.normalize(embeddings_1, p=2)
normalized_embeddings_2 = F.normalize(embeddings_2, p=2)
return torch.matmul(
normalized_embeddings_1, normalized_embeddings_2.transpose(0, 1)
)
print(similarity(english_embeddings, italian_embeddings))
print(similarity(english_embeddings, japanese_embeddings))
print(similarity(italian_embeddings, japanese_embeddings))
```
## Details
Details about data, training, evaluation and performance metrics are available in the [original paper](https://arxiv.org/abs/2007.01852).
### BibTeX entry and citation info
```bibtex
@misc{feng2020languageagnostic,
title={Language-agnostic BERT Sentence Embedding},
author={Fangxiaoyu Feng and Yinfei Yang and Daniel Cer and Naveen Arivazhagan and Wei Wang},
year={2020},
eprint={2007.01852},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
dccuchile/distilbert-base-spanish-uncased-finetuned-mldoc
|
[
"pytorch",
"distilbert",
"text-classification",
"transformers"
] |
text-classification
|
{
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"DistilBertForSequenceClassification"
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| 27 | null |
---
license: apache-2.0
tags:
- image-classification
- generated_from_trainer
datasets:
- uta_rldd
metrics:
- accuracy
model-index:
- name: vit-driver-drowsiness-detection
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: chbh7051/driver-drowsiness-detection
type: uta_rldd
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9930477264186396
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-driver-drowsiness-detection
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the chbh7051/driver-drowsiness-detection dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0159
- Accuracy: 0.9930
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1504 | 0.17 | 500 | 0.1178 | 0.9540 |
| 0.0581 | 0.33 | 1000 | 0.1022 | 0.9579 |
| 0.0415 | 0.5 | 1500 | 0.0877 | 0.9746 |
| 0.0487 | 0.67 | 2000 | 0.0650 | 0.9775 |
| 0.0555 | 0.84 | 2500 | 0.0537 | 0.9786 |
| 0.0279 | 1.0 | 3000 | 0.0472 | 0.9827 |
| 0.0139 | 1.17 | 3500 | 0.0452 | 0.9855 |
| 0.0282 | 1.34 | 4000 | 0.0358 | 0.9878 |
| 0.0077 | 1.5 | 4500 | 0.0397 | 0.9876 |
| 0.0143 | 1.67 | 5000 | 0.0159 | 0.9930 |
| 0.0439 | 1.84 | 5500 | 0.0162 | 0.9930 |
### Framework versions
- Transformers 4.27.4
- Pytorch 1.13.0
- Datasets 2.1.0
- Tokenizers 0.13.2
|
dccuchile/distilbert-base-spanish-uncased-finetuned-pawsx
|
[
"pytorch",
"distilbert",
"text-classification",
"transformers"
] |
text-classification
|
{
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"DistilBertForSequenceClassification"
],
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}
| 29 | null |
---
# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/model-cards
{}
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
dccuchile/distilbert-base-spanish-uncased-finetuned-qa-mlqa
|
[
"pytorch",
"distilbert",
"question-answering",
"transformers",
"autotrain_compatible"
] |
question-answering
|
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"DistilBertForQuestionAnswering"
],
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"task_specific_params": {
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| 5 | null |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: gpt-cv
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt-cv
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Tokenizers 0.13.3
|
dccuchile/distilbert-base-spanish-uncased-finetuned-xnli
|
[
"pytorch",
"distilbert",
"text-classification",
"transformers"
] |
text-classification
|
{
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"DistilBertForSequenceClassification"
],
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"task_specific_params": {
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| 31 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: split
split: validation
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.9235
- name: F1
type: f1
value: 0.9235647957765342
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2155
- Accuracy: 0.9235
- F1: 0.9236
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 250 | 0.3117 | 0.9065 | 0.9034 |
| No log | 2.0 | 500 | 0.2155 | 0.9235 | 0.9236 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
dccuchile/distilbert-base-spanish-uncased
|
[
"pytorch",
"distilbert",
"fill-mask",
"es",
"dataset:large_spanish_corpus",
"transformers",
"spanish",
"OpenCENIA",
"autotrain_compatible"
] |
fill-mask
|
{
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"DistilBertForMaskedLM"
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"max_length": null
},
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}
| 670 | null |
---
license: creativeml-openrail-m
tags:
- stablediffusionapi.com
- stable-diffusion-api
- text-to-image
- ultra-realistic
pinned: true
---
# Three Delicacy Wonton API Inference
## Get API Key
Get API key from [Stable Diffusion API](http://stablediffusionapi.com/), No Payment needed.
Replace Key in below code, change **model_id** to "three-delicacy-wonto"
Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://stablediffusionapi.com/docs)
Model link: [View model](https://stablediffusionapi.com/models/three-delicacy-wonto)
Credits: [View credits](https://civitai.com/?query=Three%20Delicacy%20Wonton)
View all models: [View Models](https://stablediffusionapi.com/models)
import requests
import json
url = "https://stablediffusionapi.com/api/v3/dreambooth"
payload = json.dumps({
"key": "",
"model_id": "three-delicacy-wonto",
"prompt": "actual 8K portrait photo of gareth person, portrait, happy colors, bright eyes, clear eyes, warm smile, smooth soft skin, big dreamy eyes, beautiful intricate colored hair, symmetrical, anime wide eyes, soft lighting, detailed face, by makoto shinkai, stanley artgerm lau, wlop, rossdraws, concept art, digital painting, looking into camera",
"negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime",
"width": "512",
"height": "512",
"samples": "1",
"num_inference_steps": "30",
"safety_checker": "no",
"enhance_prompt": "yes",
"seed": None,
"guidance_scale": 7.5,
"multi_lingual": "no",
"panorama": "no",
"self_attention": "no",
"upscale": "no",
"embeddings": "embeddings_model_id",
"lora": "lora_model_id",
"webhook": None,
"track_id": None
})
headers = {
'Content-Type': 'application/json'
}
response = requests.request("POST", url, headers=headers, data=payload)
print(response.text)
> Use this coupon code to get 25% off **DMGG0RBN**
|
CennetOguz/distilbert-base-uncased-finetuned-recipe
|
[
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible"
] |
fill-mask
|
{
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"DistilBertForMaskedLM"
],
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}
| 2 | null |
---
tags:
- LunarLander-v2
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 100.09 +/- 120.34
name: mean_reward
verified: false
---
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
```python
{'exp_name': 'ppo'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'repo_id': 'ntrant7/ppo-LunarLander-v2'
'env_id': 'LunarLander-v2'
'total_timesteps': 300000
'learning_rate': 0.003
'num_envs': 4
'num_steps': 128
'anneal_lr': True
'gamma': 0.99
'gae_lambda': 0.97
'num_minibatches': 4
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'batch_size': 512
'minibatch_size': 128}
```
|
Chaddmckay/Cdm
|
[] | null |
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}
| 0 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: distilbart-cnn-12-6-rate-prof
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbart-cnn-12-6-rate-prof
This model is a fine-tuned version of [sshleifer/distilbart-cnn-12-6](https://huggingface.co/sshleifer/distilbart-cnn-12-6) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0922
- Rouge1: 0.3041
- Rouge2: 0.1196
- Rougel: 0.2229
- Rougelsum: 0.2241
- Gen Len: 66.9333
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| No log | 1.0 | 68 | 1.1530 | 0.2844 | 0.0943 | 0.204 | 0.2027 | 67.8 |
| No log | 2.0 | 136 | 1.0948 | 0.2614 | 0.0498 | 0.1672 | 0.168 | 67.8 |
| No log | 3.0 | 204 | 1.0797 | 0.3042 | 0.0983 | 0.2068 | 0.2082 | 66.6667 |
| No log | 4.0 | 272 | 1.0808 | 0.2932 | 0.0914 | 0.2012 | 0.2024 | 67.1333 |
| No log | 5.0 | 340 | 1.0922 | 0.3041 | 0.1196 | 0.2229 | 0.2241 | 66.9333 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Chaewon/mnmt_decoder_en
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
{
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"GPT2LMHeadModel"
],
"model_type": "gpt2",
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"max_length": 50
},
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}
| 8 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: french_52
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# french_52
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 7
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10
- training_steps: 5
### Training results
### Framework versions
- Transformers 4.28.0.dev0
- Pytorch 1.13.1
- Datasets 2.11.0
- Tokenizers 0.13.2
|
Chakita/KROBERT
|
[
"pytorch",
"roberta",
"fill-mask",
"transformers",
"masked-lm",
"fill-in-the-blanks",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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}
}
}
| 7 | null |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: gpt-paper
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt-paper
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Tokenizers 0.13.3
|
Chakita/Kalbert
|
[
"pytorch",
"tensorboard",
"albert",
"fill-mask",
"transformers",
"generated_from_trainer",
"license:mit",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
"task_specific_params": {
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},
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}
| 5 | null |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -1.43 +/- 0.80
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Chan/distilgpt2-finetuned-wikitext2
|
[] | null |
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}
}
| 0 | null |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="achgls/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
Charlotte77/model_test
|
[] | null |
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| 0 | null |
---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### jakobseeder11 Dreambooth model trained by jakobsitter with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Sample pictures of this concept:
|
Cheatham/xlm-roberta-large-finetuned-d12
|
[
"pytorch",
"xlm-roberta",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"XLMRobertaForSequenceClassification"
],
"model_type": "xlm-roberta",
"task_specific_params": {
"conversational": {
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},
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| 20 | null |
NEWS: Health experts said it is too early to predict whether demand would match up with the 171 million doses of the new boosters the U.S. ordered for the fall.\n\n HEADLINE:
```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("utkan/gpt2-news-headlines-v1")
model = AutoModelForCausalLM.from_pretrained("utkan/gpt2-news-headlines-v1")
device = "cuda" # or "cpu"
input_text = "NEWS: Health experts said it is too early to predict whether demand would match up with the 171 million doses of the new boosters the U.S. ordered for the fall.\n\n HEADLINE:"
x = tokenizer([input_text], return_tensors='pt').input_ids.to(device)
y = model.generate(x, max_new_tokens=1)
tokenizer.batch_decode(y, skip_special_tokens=True)[0].split("HEADLINE: ")[-1]
```
|
Cheatham/xlm-roberta-large-finetuned-r01
|
[
"pytorch",
"xlm-roberta",
"text-classification",
"transformers"
] |
text-classification
|
{
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"XLMRobertaForSequenceClassification"
],
"model_type": "xlm-roberta",
"task_specific_params": {
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},
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},
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},
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},
"translation_en_to_fr": {
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}
}
| 23 | null |
---
tags:
- autotrain
- summarization
language:
- unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- Udit191/autotrain-data-summarization_bart_longformer
co2_eq_emissions:
emissions: 33.494252747225424
---
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 54164127153
- CO2 Emissions (in grams): 33.4943
## Validation Metrics
- Loss: 2.334
- Rouge1: 50.856
- Rouge2: 21.784
- RougeL: 28.707
- RougeLsum: 45.379
- Gen Len: 214.938
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/Udit191/autotrain-summarization_bart_longformer-54164127153
```
|
Check/vaw2tmp
|
[
"tensorboard"
] | null |
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}
| 0 | null |
---
license: creativeml-openrail-m
tags:
- stablediffusionapi.com
- stable-diffusion-api
- text-to-image
- ultra-realistic
pinned: true
---
# three-delicacy API Inference
## Get API Key
Get API key from [Stable Diffusion API](http://stablediffusionapi.com/), No Payment needed.
Replace Key in below code, change **model_id** to "three-delicacy"
Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://stablediffusionapi.com/docs)
Model link: [View model](https://stablediffusionapi.com/models/three-delicacy)
Credits: [View credits](https://civitai.com/?query=three-delicacy)
View all models: [View Models](https://stablediffusionapi.com/models)
import requests
import json
url = "https://stablediffusionapi.com/api/v3/dreambooth"
payload = json.dumps({
"key": "",
"model_id": "three-delicacy",
"prompt": "actual 8K portrait photo of gareth person, portrait, happy colors, bright eyes, clear eyes, warm smile, smooth soft skin, big dreamy eyes, beautiful intricate colored hair, symmetrical, anime wide eyes, soft lighting, detailed face, by makoto shinkai, stanley artgerm lau, wlop, rossdraws, concept art, digital painting, looking into camera",
"negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime",
"width": "512",
"height": "512",
"samples": "1",
"num_inference_steps": "30",
"safety_checker": "no",
"enhance_prompt": "yes",
"seed": None,
"guidance_scale": 7.5,
"multi_lingual": "no",
"panorama": "no",
"self_attention": "no",
"upscale": "no",
"embeddings": "embeddings_model_id",
"lora": "lora_model_id",
"webhook": None,
"track_id": None
})
headers = {
'Content-Type': 'application/json'
}
response = requests.request("POST", url, headers=headers, data=payload)
print(response.text)
> Use this coupon code to get 25% off **DMGG0RBN**
|
CheonggyeMountain-Sherpa/kogpt-trinity-punct-wrapper
|
[
"ko",
"gpt2",
"license:cc-by-nc-sa-4.0"
] | null |
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}
}
| 0 | null |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_health_gathering_supreme
type: doom_health_gathering_supreme
metrics:
- type: mean_reward
value: 9.70 +/- 4.48
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r ckauth/rl_course_vizdoom_health_gathering_supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m .usr.local.lib.python3.10.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m .usr.local.lib.python3.10.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
Chertilasus/main
|
[] | null |
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| 0 | null |
---
language:
- pt
- en
- es
- ca
---
I recommend you to run this LORA with KoboldCPP if you need CPU, like me, to perform inference.
Below, my command to run this LORA with KoboldCPP:
python koboldcpp.py --launch --lora /ai/text/LORAs/ggml-alpaca-lora-ptbr-7b.bin
Recomendo que você execute este arquivo LORA com o KoboldCPP se precisa usar o CPU, como eu, para fazer inferências.
Abaixo, meu comando para executar este LORA com o KoboldCPP:
python koboldcpp.py --launch --lora /ai/text/LORAs/ggml-alpaca-lora-ptbr-7b.bin
## LORAs X Models:
ggml-alpaca-lora-ptbr-7b.bin x MedAlpaca-7B-ggml-q4_0.bin -> Works OK, with a few errors of interpretation
ggml-alpaca-lora-ptbr-7b.bin x ggml-alpaca-7b-q4.bin -> Works very good! No relevant errors found
## Example prompts in (Brazilian Portuguese) using LORA ggml-alpaca-lora-ptbr-7b.bin X model ggml-alpaca-7b-q4.bin:
*qual remédio usar para dor de cabeça?*
Para a dor de cabeça, o qual remédio utilizar depende do tipo de dor que se está experimentando. Seu médico pode recomendar algumas medicações como ibuprofeno, acetaminofen ou naproxen, dependendo da severidade da dor.
|
Chikita1/www_stash_stock
|
[
"license:bsd-3-clause-clear"
] | null |
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| 0 | null |
---
license: creativeml-openrail-m
---
https://civitai.com/models/54299/tomori-nao-charlotte
|
Chinat/test-classifier
|
[] | null |
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| 0 | null |
---
license: creativeml-openrail-m
---
https://civitai.com/models/54113/columbia-azur-lane
|
Chinmay/mlindia
|
[] | null |
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| 0 | null |
---
license: creativeml-openrail-m
---
https://civitai.com/models/54260/the-quintessential-quintuplets-all-nakano-sisters-ichika-nino-miku-yotsuba-itsuki
|
Chiuchiyin/DialoGPT-small-Donald
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
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"GPT2LMHeadModel"
],
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}
| 7 | null |
---
license: creativeml-openrail-m
---
https://civitai.com/models/13926/wa2000-5-costume-lorasor-girls-frontline
|
Chiuchiyin/Donald
|
[] | null |
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}
| 0 | null |
---
license: creativeml-openrail-m
---
https://civitai.com/models/53799/asta-honkai-star-rail
|
ChoboAvenger/DialoGPT-small-DocBot
|
[] | null |
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}
| 0 | 2023-04-30T14:32:55Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/54003/sena-kashiwazaki-or-boku-wa-tomodachi-ga-sukunai
|
ChoboAvenger/DialoGPT-small-joshua
|
[] | null |
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}
| 0 | null |
---
license: creativeml-openrail-m
---
https://civitai.com/models/53161/kaga-kancolle
|
ChrisP/xlm-roberta-base-finetuned-marc-en
|
[] | null |
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}
| 0 | null |
---
license: creativeml-openrail-m
---
https://civitai.com/models/52995/arknights-schwarz-skyline
|
ChrisVCB/DialoGPT-medium-cmjs
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
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}
| 7 | null |
---
license: creativeml-openrail-m
---
https://civitai.com/models/20058/asuka-langley-souryuushikinami-evangelion
|
ChrisVCB/DialoGPT-medium-ej
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
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}
| 13 | null |
---
language:
- ms
---
# pythia-1b
## how-to
```python
from transformers import GenerationConfig, AutoTokenizer, AutoConfig, AutoModelForCausalLM
base_model='mesolitica/pythia-1b-finetune'
temperature=0.7
top_p=0.75
top_k=40
num_beams=4
max_new_tokens=256
device = 'cuda'
template = {
"description": "Template used by Alpaca-LoRA.",
"prompt_input": "Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n\n### Instruction:\n{instruction}\n\n### Input:\n{input}\n\n### Response:\n",
"prompt_no_input": "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{instruction}\n\n### Response:\n",
"response_split": "### Response:"
}
model = AutoModelForCausalLM.from_pretrained(
base_model,
torch_dtype=torch.float16,
device_map="auto",
)
model.config.pad_token_id = tokenizer.pad_token_id = 1
model.config.eos_token_id = tokenizer.eos_token_id = 0
model.half()
_ = model.eval()
q = """
paragraph `"Isu ini sudah lama dan sudah reda namun seperti mereka ini (kerajaan) masih dengan mentaliti 'pembangkang' kerana menghangatkan sesuatu isu supaya rakyat pandang serong kepada PN," katanya ketika dihubungi Sinar Harian pada Isnin.
Beliau berkata demikian ketika diminta mengulas isu dua pemimpin PN iaitu Presiden Pas yang juga Ahli Parlimen Marang, Tan Sri Abdul Hadi Awang serta Ahli Parlimen Permatang Pauh yang Ketua Pemuda Pas Pulau Pinang, Muhammad Fawwaz Mohamad Jan disiasat berhubung kenyataan berunsur perkauman.
Jelas Mohd Harun, Abdul Hadi yang didakwa berunsur perkauman itu mempunyai asas.` isu fawwaz
"""
prompt = template["prompt_no_input"].format(instruction=q)
prompt
inputs = tokenizer(prompt, return_tensors="pt")
input_ids = inputs["input_ids"].to(device)
generation_config = GenerationConfig(
temperature=temperature,
top_p=top_p,
top_k=top_k,
num_beams=num_beams,
)
with torch.no_grad():
generation_output = model.generate(
input_ids=input_ids,
return_dict_in_generate=True,
output_scores=True,
max_new_tokens=max_new_tokens,
temperature=temperature,
top_p=top_p,
top_k=top_k,
num_beams=num_beams,
)
s = generation_output.sequences[0]
output = tokenizer.decode(s)
```
output,
```text
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
paragraph `"Isu ini sudah lama dan sudah reda namun seperti mereka ini (kerajaan) masih dengan mentaliti 'pembangkang' kerana menghangatkan sesuatu isu supaya rakyat pandang serong kepada PN," katanya ketika dihubungi Sinar Harian pada Isnin.
Beliau berkata demikian ketika diminta mengulas isu dua pemimpin PN iaitu Presiden Pas yang juga Ahli Parlimen Marang, Tan Sri Abdul Hadi Awang serta Ahli Parlimen Permatang Pauh yang Ketua Pemuda Pas Pulau Pinang, Muhammad Fawwaz Mohamad Jan disiasat berhubung kenyataan berunsur perkauman.
Jelas Mohd Harun, Abdul Hadi yang didakwa berunsur perkauman itu mempunyai asas.` isu fawwaz
### Response:
Isu ini sudah lama dan sudah reda namun seperti mereka ini (kerajaan) masih dengan mentaliti 'pembangkang' kerana menghangatkan sesuatu isu supaya rakyat pandang serong kepada PN. Beliau berkata demikian ketika diminta mengulas isu dua pemimpin PN iaitu Presiden Pas yang juga Ahli Parlimen Marang, Tan Sri Abdul Hadi Awang serta Ahli Parlimen Permatang Pauh yang Ketua Pemuda Pas Pulau Pinang, Muhammad Fawwaz Mohamad Jan disiasat berhubung kenyataan berunsur perkauman. Jelas Mohd Harun, Abdul Hadi yang didakwa berunsur perkauman itu mempunyai asas.<|endoftext|>
```
|
ChristianOrr/madnet_keras
|
[
"tensorboard",
"dataset:flyingthings-3d",
"dataset:kitti",
"arxiv:1810.05424",
"vision",
"deep-stereo",
"depth-estimation",
"Tensorflow2",
"Keras",
"license:apache-2.0"
] |
depth-estimation
|
{
"architectures": null,
"model_type": null,
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 0 | null |
---
license: creativeml-openrail-m
---
https://civitai.com/models/6358/tsumasaky-cc-code-geass
|
ChristopherA08/IndoELECTRA
|
[
"pytorch",
"electra",
"pretraining",
"id",
"dataset:oscar",
"transformers"
] | null |
{
"architectures": [
"ElectraForPreTraining"
],
"model_type": "electra",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 4 | null |
---
license: creativeml-openrail-m
---
https://civitai.com/models/54347/prinz-eugen-azur-lane-kindred-evening-spirits
|
Chuah/DialoGPT-small-harrypotter
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 9 | null |
---
license: creativeml-openrail-m
tags:
- stablediffusionapi.com
- stable-diffusion-api
- text-to-image
- ultra-realistic
pinned: true
---
# Dark Sushi 2.5D API Inference
## Get API Key
Get API key from [Stable Diffusion API](http://stablediffusionapi.com/), No Payment needed.
Replace Key in below code, change **model_id** to "dark-sushi-25d"
Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://stablediffusionapi.com/docs)
Model link: [View model](https://stablediffusionapi.com/models/dark-sushi-25d)
Credits: [View credits](https://civitai.com/?query=Dark%20Sushi%202.5D)
View all models: [View Models](https://stablediffusionapi.com/models)
import requests
import json
url = "https://stablediffusionapi.com/api/v3/dreambooth"
payload = json.dumps({
"key": "",
"model_id": "dark-sushi-25d",
"prompt": "actual 8K portrait photo of gareth person, portrait, happy colors, bright eyes, clear eyes, warm smile, smooth soft skin, big dreamy eyes, beautiful intricate colored hair, symmetrical, anime wide eyes, soft lighting, detailed face, by makoto shinkai, stanley artgerm lau, wlop, rossdraws, concept art, digital painting, looking into camera",
"negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime",
"width": "512",
"height": "512",
"samples": "1",
"num_inference_steps": "30",
"safety_checker": "no",
"enhance_prompt": "yes",
"seed": None,
"guidance_scale": 7.5,
"multi_lingual": "no",
"panorama": "no",
"self_attention": "no",
"upscale": "no",
"embeddings": "embeddings_model_id",
"lora": "lora_model_id",
"webhook": None,
"track_id": None
})
headers = {
'Content-Type': 'application/json'
}
response = requests.request("POST", url, headers=headers, data=payload)
print(response.text)
> Use this coupon code to get 25% off **DMGG0RBN**
|
ChukSamuels/DialoGPT-small-Dr.FauciBot
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 13 | null |
---
license: creativeml-openrail-m
---
https://civitai.com/models/54525/nakano-nino-5-toubun-no-hanayome
|
Chun/DialoGPT-large-dailydialog
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 6 | null |
---
license: creativeml-openrail-m
---
https://civitai.com/models/54319/loha-kai-schulen-valkyria-chronicles-44
|
Chun/w-en2zh-mtm
|
[
"pytorch",
"mbart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"MBartForConditionalGeneration"
],
"model_type": "mbart",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 7 | null |
---
tags:
- CartPole-v1
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 226.40 +/- 119.52
name: mean_reward
verified: false
---
# PPO Agent Playing CartPole-v1
This is a trained model of a PPO agent playing CartPole-v1.
# Hyperparameters
```python
{'exp_name': 'unit8-part-sol'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'env_id': 'CartPole-v1'
'total_timesteps': 50000
'learning_rate': 0.00025
'num_envs': 4
'num_steps': 128
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.95
'num_minibatches': 4
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'mojemai/ppo-CartPole-v1'
'batch_size': 512
'minibatch_size': 128}
```
|
Chun/w-zh2en-hsk
|
[
"pytorch",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"MarianMTModel"
],
"model_type": "marian",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 3 | null |
---
license: creativeml-openrail-m
---
https://civitai.com/models/37986/chameleonai-rpg-mix
|
Chun/w-zh2en-mtm
|
[
"pytorch",
"mbart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"MBartForConditionalGeneration"
],
"model_type": "mbart",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 8 | null |
---
license: creativeml-openrail-m
tags:
- stablediffusionapi.com
- stable-diffusion-api
- text-to-image
- ultra-realistic
pinned: true
---
# TMND-Mix API Inference
## Get API Key
Get API key from [Stable Diffusion API](http://stablediffusionapi.com/), No Payment needed.
Replace Key in below code, change **model_id** to "tmnd-mix"
Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://stablediffusionapi.com/docs)
Model link: [View model](https://stablediffusionapi.com/models/tmnd-mix)
Credits: [View credits](https://civitai.com/?query=TMND-Mix)
View all models: [View Models](https://stablediffusionapi.com/models)
import requests
import json
url = "https://stablediffusionapi.com/api/v3/dreambooth"
payload = json.dumps({
"key": "",
"model_id": "tmnd-mix",
"prompt": "actual 8K portrait photo of gareth person, portrait, happy colors, bright eyes, clear eyes, warm smile, smooth soft skin, big dreamy eyes, beautiful intricate colored hair, symmetrical, anime wide eyes, soft lighting, detailed face, by makoto shinkai, stanley artgerm lau, wlop, rossdraws, concept art, digital painting, looking into camera",
"negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime",
"width": "512",
"height": "512",
"samples": "1",
"num_inference_steps": "30",
"safety_checker": "no",
"enhance_prompt": "yes",
"seed": None,
"guidance_scale": 7.5,
"multi_lingual": "no",
"panorama": "no",
"self_attention": "no",
"upscale": "no",
"embeddings": "embeddings_model_id",
"lora": "lora_model_id",
"webhook": None,
"track_id": None
})
headers = {
'Content-Type': 'application/json'
}
response = requests.request("POST", url, headers=headers, data=payload)
print(response.text)
> Use this coupon code to get 25% off **DMGG0RBN**
|
Chungu424/DATA
|
[] | null |
{
"architectures": null,
"model_type": null,
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 0 | null |
---
tags:
- LunarLander-v2
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 24.37 +/- 97.04
name: mean_reward
verified: false
---
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
```python
{'exp_name': '__file__'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'env_id': 'LunarLander-v2'
'total_timesteps': 650000
'learning_rate': 0.00025
'num_envs': 4
'num_steps': 128
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.95
'num_minibatches': 4
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.25
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'kinkpunk/PPO-LunarLander'
'batch_size': 512
'minibatch_size': 128}
```
|
Chungu424/repo
|
[] | null |
{
"architectures": null,
"model_type": null,
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 0 | null |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_health_gathering_supreme
type: doom_health_gathering_supreme
metrics:
- type: mean_reward
value: 8.45 +/- 3.21
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r kinkpunk/rl-doom-health-gathering-supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m <path.to.enjoy.module> --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl-doom-health-gathering-supreme
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m <path.to.train.module> --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl-doom-health-gathering-supreme --restart_behavior=resume --train_for_env_steps=5000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
Cinnamon/electra-small-japanese-discriminator
|
[
"pytorch",
"electra",
"pretraining",
"ja",
"transformers",
"license:apache-2.0"
] | null |
{
"architectures": [
"ElectraForPreTraining"
],
"model_type": "electra",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 419 | null |
---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
config: PAN-X.de
split: validation
args: PAN-X.de
metrics:
- name: F1
type: f1
value: 0.8645886561062851
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1316
- F1: 0.8646
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2585 | 1.0 | 525 | 0.1560 | 0.8222 |
| 0.128 | 2.0 | 1050 | 0.1428 | 0.8419 |
| 0.0808 | 3.0 | 1575 | 0.1316 | 0.8646 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.6.0
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Cinnamon/electra-small-japanese-generator
|
[
"pytorch",
"electra",
"fill-mask",
"ja",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
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}
| 19 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: split
split: validation
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.9255
- name: F1
type: f1
value: 0.9255688957679862
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2237
- Accuracy: 0.9255
- F1: 0.9256
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8556 | 1.0 | 250 | 0.3192 | 0.908 | 0.9055 |
| 0.2538 | 2.0 | 500 | 0.2237 | 0.9255 | 0.9256 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
ClaudeCOULOMBE/RickBot
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
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"GPT2LMHeadModel"
],
"model_type": "gpt2",
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},
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| 9 | null |
---
license: apache-2.0
tags:
- translation
- generated_from_trainer
datasets:
- kde4
metrics:
- bleu
model-index:
- name: marian-finetuned-kde4-en-to-fr
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: kde4
type: kde4
config: en-fr
split: train
args: en-fr
metrics:
- name: Bleu
type: bleu
value: 44.10330687777927
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# marian-finetuned-kde4-en-to-fr
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) on the kde4 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8558
- Bleu: 44.1033
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu117
- Datasets 2.11.0
- Tokenizers 0.13.3
|
ClaudeYang/awesome_fb_model
|
[
"pytorch",
"bart",
"text-classification",
"dataset:multi_nli",
"transformers",
"zero-shot-classification"
] |
zero-shot-classification
|
{
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"BartForSequenceClassification"
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| 26 | null |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_health_gathering_supreme
type: doom_health_gathering_supreme
metrics:
- type: mean_reward
value: 10.55 +/- 4.52
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r MerlinTK/rl_course_vizdoom_health_gathering_supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m .usr.local.lib.python3.10.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m .usr.local.lib.python3.10.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
Cloudy/DialoGPT-CJ-large
|
[
"pytorch",
"conversational"
] |
conversational
|
{
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| 1 | null |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Cartpole-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
ClydeWasTaken/DialoGPT-small-joshua
|
[
"conversational"
] |
conversational
|
{
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}
| 0 | null |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="yyassin/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
CoShin/XLM-roberta-large_ko_en_nil_sts
|
[] | null |
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}
}
| 0 | null |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: taxi-v3-q-learning
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.46 +/- 2.73
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="yyassin/taxi-v3-q-learning", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
CodeDanCode/SP-KyleBot
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
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}
| 15 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: split
split: validation
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.9265
- name: F1
type: f1
value: 0.9265254169154161
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2167
- Accuracy: 0.9265
- F1: 0.9265
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8025 | 1.0 | 250 | 0.3076 | 0.9055 | 0.9032 |
| 0.2454 | 2.0 | 500 | 0.2167 | 0.9265 | 0.9265 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cpu
- Datasets 2.12.0
- Tokenizers 0.13.3
|
CodeNinja1126/koelectra-model
|
[] | null |
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| 0 | null |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_health_gathering_supreme
type: doom_health_gathering_supreme
metrics:
- type: mean_reward
value: 10.18 +/- 3.22
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r myklicious/rl_course_vizdoom_health_gathering_supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m .usr.local.lib.python3.10.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m .usr.local.lib.python3.10.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
CodeNinja1126/xlm-roberta-large-kor-mrc
|
[
"pytorch",
"xlm-roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] |
question-answering
|
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"XLMRobertaForQuestionAnswering"
],
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| 8 | null |
---
license: bsd-3-clause
tags:
- generated_from_trainer
datasets:
- audio_dataset
model-index:
- name: ast_bird_model2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ast_bird_model2
This model is a fine-tuned version of [MIT/ast-finetuned-audioset-10-10-0.4593](https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593) on the audio_dataset dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 8
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.27.4
- Pytorch 1.13.0
- Datasets 2.1.0
- Tokenizers 0.13.2
|
CoderEFE/DialoGPT-medium-marx
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
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},
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"do_sample": null,
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},
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
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"prefix": null
}
}
}
| 7 | null |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
instance_prompt: a photo of fantuan portrait
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA DreamBooth - wujia/fantuan_result
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were trained on a photo of fantuan portrait using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
LoRA for the text encoder was enabled: False.
|
Venkatakrishnan-Ramesh/Text_gen
|
[] | null |
{
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}
}
| 0 | null |
just for resolve link for colab.. source https://civitai.com/models/22922/lyriel
|
CoffeeAddict93/gpt1-modest-proposal
|
[
"pytorch",
"openai-gpt",
"text-generation",
"transformers",
"has_space"
] |
text-generation
|
{
"architectures": [
"OpenAIGPTLMHeadModel"
],
"model_type": "openai-gpt",
"task_specific_params": {
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},
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"prefix": null
},
"text-generation": {
"do_sample": true,
"max_length": 50
},
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},
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}
}
}
| 11 | null |
---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: AntBulletEnv-v0
type: AntBulletEnv-v0
metrics:
- type: mean_reward
value: 1776.61 +/- 253.02
name: mean_reward
verified: false
---
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
CoffeeAddict93/gpt2-call-of-the-wild
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
{
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"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": null
},
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"prefix": null
},
"text-generation": {
"do_sample": true,
"max_length": 50
},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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},
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}
}
}
| 6 | null |
---
language:
- en
thumbnail: null
tags:
- text generation
- conversational
pipeline_tag: text-generation
inference: false
duplicated_from: PygmalionAI/pygmalion-7b
---
<h1 style="text-align: center">Pygmalion 7B</h1>
<h2 style="text-align: center">A conversational LLaMA fine-tune.</h2>
## Model Details
Pygmalion 7B is a dialogue model based on Meta's LLaMA-7B.
This is version 1. It has been fine-tuned using a subset of the data from Pygmalion-6B-v8-pt4, for those of you familiar with the project.
## Applying the XORs
The model weights in this repository cannot be used as-is. The files here are XORs due to licensing concerns. To obtain proper, usable model weights you need to:
- Request access to the original LLaMA weights from Meta [through this form](https://docs.google.com/forms/d/e/1FAIpQLSfqNECQnMkycAp2jP4Z9TFX0cGR4uf7b_fBxjY_OjhJILlKGA/viewform?usp=send_form)
- Convert them to the HuggingFace Transformers format by using the [convert_llama_weights_to_hf.py](https://github.com/huggingface/transformers/blob/849367ccf741d8c58aa88ccfe1d52d8636eaf2b7/src/transformers/models/llama/convert_llama_weights_to_hf.py) script **for your version of the `transformers` library**
- With the LLaMA-7B weights in hand, you can use the [xor_codec.py](./xor_codec.py) script provided in this repository:
```bash
python3 xor_codec.py \
./pygmalion-7b \
./xor_encoded_files \
/path/to/hf-converted/llama-7b \
--decode
```
**Note for Windows users:** If you're on Windows, you might run into issues where following the steps above will result in corrupted files. This seems to be because `git` messes with the encoding of text files (so the `.json`s and other relevant files). To avoid this, use WSL. For reference, these are the MD5 hashes you should get after following the steps above:
```bash
$ rhash -M *
4608facb4910118f8dfa80f090cbc4dc config.json
2917a1cafb895cf57e746cfd7696bfe5 generation_config.json
98764eb949eea16f8e2e1c2d3dea0066 pytorch_model-00001-of-00002.bin
be9ba2f37228a0a9ea0eaf6530aba4de pytorch_model-00002-of-00002.bin
81648ef3915ed2e83d49fed93122d53e pytorch_model.bin.index.json
6b2e0a735969660e720c27061ef3f3d3 special_tokens_map.json
fdb311c39b8659a5d5c1991339bafc09 tokenizer.json
eeec4125e9c7560836b4873b6f8e3025 tokenizer.model
f0b65b44265ba51881b1e1881102504f tokenizer_config.json
```
## Prompting
The model was trained on the usual Pygmalion persona + chat format, so any of the usual UIs should already handle everything correctly. If you're using the model directly, this is the expected formatting:
```
[CHARACTER]'s Persona: [A few sentences about the character you want the model to play]
<START>
[DIALOGUE HISTORY]
You: [User's input message here]
[CHARACTER]:
```
Where `[CHARACTER]` is, as you can probably guess, the name of the character you want the model to portray, `<START>` should be used verbatim as a delimiter token to separate persona and scenario data from the dialogue, and `[DIALOGUE HISTORY]` is a sliding window of chat history so the model can have conversational context to draw from. Here's a concrete example:
```
Assistant's Persona: Assistant is a highly intelligent language model trained to comply with user requests.
<START>
Assistant: Hello! How may I help you today?
You: What is Zork?
Assistant:
```
Which will generate something like:
```
Zork is an interactive fiction computer game created in the 1970s by Infocom, Inc., which was later acquired by Activision Blizzard. It is widely considered one of the most influential games ever made and has been credited with popularizing text-based adventure games. The original version of Zork was written in the programming language MACRO-10, but it was ported to many other platforms over the years."
```
The model will automatically emit an end-of-text token (`</s>`) when it judges that the response is complete.
## Limitations and biases
The intended use-case for this model is fictional conversation for entertainment purposes. Any other sort of usage is out of scope.
As such, it was **not** fine-tuned to be safe and harmless: the base model _and_ this fine-tune have been trained on data known to contain profanity and texts that are lewd or otherwise offensive. It may produce socially unacceptable or undesirable text, even if the prompt itself does not include anything explicitly offensive. Outputs might often be factually wrong or misleading.
|
CoffeeAddict93/gpt2-medium-modest-proposal
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
{
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"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
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},
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},
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"do_sample": true,
"max_length": 50
},
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},
"translation_en_to_fr": {
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},
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}
}
}
| 7 | null |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 2301 with parameters:
```
{'batch_size': None, 'sampler': 'torch.utils.data.sampler.SequentialSampler', 'batch_sampler': 'nkwdataset.BatchNegSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 100,
"evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 100,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 1024, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
CogComp/bart-faithful-summary-detector
|
[
"pytorch",
"jax",
"bart",
"text-classification",
"en",
"dataset:xsum",
"transformers",
"xsum",
"license:cc-by-sa-4.0"
] |
text-classification
|
{
"architectures": [
"BartForSequenceClassification"
],
"model_type": "bart",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": 1,
"max_length": 128,
"min_length": 12,
"no_repeat_ngram_size": null,
"num_beams": 4,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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}
}
}
| 234 | null |
---
tags:
- autotrain
- text-classification
language:
- es
widget:
- text: "I love AutoTrain 🤗"
datasets:
- alexisbaladon/autotrain-data-huhu-humor
co2_eq_emissions:
emissions: 0.3100765073399468
---
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 54189127188
- CO2 Emissions (in grams): 0.3101
## Validation Metrics
- Loss: 0.426
- Accuracy: 0.835
- Precision: 0.795
- Recall: 0.710
- AUC: 0.869
- F1: 0.750
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/alexisbaladon/autotrain-huhu-humor-54189127188
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("alexisbaladon/autotrain-huhu-humor-54189127188", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("alexisbaladon/autotrain-huhu-humor-54189127188", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
```
|
CogComp/roberta-temporal-predictor
|
[
"pytorch",
"roberta",
"fill-mask",
"arxiv:2202.00436",
"transformers",
"license:mit",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
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},
"text-generation": {
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"max_length": null
},
"translation_en_to_de": {
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"max_length": null,
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"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
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"num_beams": null,
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},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 14 | null |
---
tags:
- autotrain
- text-classification
language:
- es
widget:
- text: "I love AutoTrain 🤗"
datasets:
- alexisbaladon/autotrain-data-huhu-humor
co2_eq_emissions:
emissions: 0.2929350890489067
---
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 54189127189
- CO2 Emissions (in grams): 0.2929
## Validation Metrics
- Loss: 0.397
- Accuracy: 0.839
- Precision: 0.812
- Recall: 0.699
- AUC: 0.892
- F1: 0.751
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/alexisbaladon/autotrain-huhu-humor-54189127189
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("alexisbaladon/autotrain-huhu-humor-54189127189", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("alexisbaladon/autotrain-huhu-humor-54189127189", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
```
|
CohleM/mbert-nepali-tokenizer
|
[] | null |
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}
| 0 | null |
---
license: creativeml-openrail-m
language:
- cs
tags:
- legal
- art
---
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❏ STREAMOVAT MÉDIA ❏
Streamovaná média jsou multimédia, která jsou nepřetržitě přijímána a prezentována koncovému uživateli, zatímco jsou dodávána poskytovatelem. Sloveso streamovat odkazuje na proces doručování nebo získávání médií tímto způsobem. [Potřebné upřesnění] Streamování se týká způsobu doručení média, nikoli média samotného. Rozdíl mezi způsobem doručování a vysílacím médiem se týká zejména telekomunikačních sítí, protože většina doručovacích systémů má streamingový charakter (např. rádio, televize, streamingové aplikace) nebo nestreamingový charakter (např. knihy, videokazety, audio CD). Streamování obsahu přes internet přináší problémy. Uživatelé, jejichž připojení k internetu nemá dostatečnou šířku pásma, mohou například zaznamenat zamrzání, zpoždění nebo pomalé ukládání obsahu do vyrovnávací paměti. A uživatelé, kteří nemají kompatibilní hardwarové nebo softwarové systémy, nemusí být schopni streamovat nějaký obsah.
Živé vysílání je poskytování internetového obsahu v reálném čase, podobně jako živé televizní vysílání obsahu prostřednictvím rádiových vln prostřednictvím televizního signálu. Online živé vysílání vyžaduje určitou formu zdrojového média (např. videokameru, audio rozhraní, software pro snímání obrazovky), kodér pro digitalizaci obsahu, vydavatele médií a síť pro doručování obsahu pro distribuci a doručování obsahu. Přímé přenosy není nutné nahrávat na začátku, i když často ano. Streamování je alternativou ke stahování souborů, což je proces, při kterém koncový uživatel získá celý soubor obsahu před jeho zobrazením nebo poslechem. Streamování umožňuje koncovému uživateli používat svůj přehrávač médií ke spuštění přehrávání digitálního videa nebo digitálního zvukového obsahu před přenesením celého souboru. Termín „streamingová média“ může odkazovat na média jiná než video a zvuk, jako jsou živé titulky, kazety se zprávami a text v reálném čase, které jsou považovány za „streamovaný text“.
❏ OBSAH AUTORSKÝCH PRÁV ❏
Autorské právo je druh duševního vlastnictví, který dává vlastníkovi výhradní právo pořídit si rozmnoženinu tvůrčího díla, obvykle na omezenou dobu. Tvůrčí práce může být literární, výtvarná, vzdělávací nebo hudební. Ochrana autorských práv je určena k ochraně původního vyjádření myšlenky jako tvůrčího díla, nikoli však myšlenky samotné. Autorská práva jsou omezena ohledy veřejného zájmu, jako je americká doktrína fair use.
Některé jurisdikce vyžadují, abyste „opravili“ díla chráněná autorským právem v hmotné podobě. Často jej sdílí více autorů, každý hWatch Dunes je soubor práv k použití nebo licencování díla, kteří jsou běžně označováni jako práva hWatch Duneers. [pochvalná zmínka potřebovaný] Tato práva často zahrnují reprodukci, odvozenou kontrolu, distribuci, veřejné předvádění a osobní práva, jako je uvádění zdroje.
Autorská práva mohou být udělena podle veřejného práva a v tomto případě jsou považována za „územní práva“. To znamená, že autorská práva udělená právem dané země nepřesahují území této konkrétní jurisdikce. Tyto typy autorských práv se v jednotlivých zemích liší; mnoho zemí, a někdy i velká skupina zemí, má dohody s jinými zeměmi o postupech, které je třeba dodržet, když práce „překročí“ státní hranice nebo když jsou vnitrostátní zákony nekonzistentní . Veřejná autorská práva obvykle vyprší 50 až 100 let po smrti tvůrce, v závislosti na jurisdikci. Některé země vyžadují určité formality týkající se autorských práv pro stanovení autorských práv, jiné uznávají autorská práva v jakémkoli dokončeném díle bez formální registrace.
Obecně se má za to, že autorská práva jsou zásadní pro podporu kulturní rozmanitosti a kreativity. Na rozdíl od všeobecného přesvědčení však Parc tvrdí, že napodobování a kopírování neomezuje kreativitu ani kulturní rozmanitost, ale ve skutečnosti je navíc podporuje. Tento argument byl podpořen mnoha příklady jako Millet a Van Gogh, Picasso, Manet a Monet atd.
❏ SERVIS ZBOŽÍ ❏
Kredit (z latinského credit, „(ona/ona) věří“) je trust, který umožňuje jedné straně poskytnout peníze nebo zdroje druhé straně, přičemž druhá strana okamžitě nevrátí první straně (která vytváří dluh), ale slíbí splatit nebo splatit tyto zdroje (nebo jiné materiály stejné hodnoty) později Jinými slovy, úvěr je způsob, jak učinit formality reciproční, právně vymahatelné a rozšířitelné na velkou skupinu nepříbuzných jednotlivců .
Převáděné prostředky mohou mít finanční povahu (např. poskytnutí půjčky) nebo představovat zboží či služby (např. spotřebitelský úvěr). Kredit zahrnuje jakoukoli formu odložené platby. Půjčku poskytuje věřitel, také známý jako věřitel, dlužník, také známý jako dlužník .
|
Coldestadam/Breakout_Mentors_SpongeBob_Model
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
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| 10 | null |
---
tags:
- autotrain
- text-classification
language:
- en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- ssamper/autotrain-data-deepentregable2
co2_eq_emissions:
emissions: 0.8730303110593549
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 54196127214
- CO2 Emissions (in grams): 0.8730
## Validation Metrics
- Loss: 0.079
- Accuracy: 0.986
- Macro F1: 0.986
- Micro F1: 0.986
- Weighted F1: 0.985
- Macro Precision: 0.991
- Micro Precision: 0.986
- Weighted Precision: 0.987
- Macro Recall: 0.983
- Micro Recall: 0.986
- Weighted Recall: 0.986
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/ssamper/autotrain-deepentregable2-54196127214
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("ssamper/autotrain-deepentregable2-54196127214", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("ssamper/autotrain-deepentregable2-54196127214", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
```
|
ComCom/gpt2
|
[
"pytorch",
"gpt2",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
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"GPT2Model"
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}
| 1 | null |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_health_gathering_supreme
type: doom_health_gathering_supreme
metrics:
- type: mean_reward
value: 12.37 +/- 5.70
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r mojemai/rl_course_vizdoom_health_gathering_supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m unit8-part2 --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m unit8-part2 --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
Cometasonmi451/Mine
|
[] | null |
{
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| 0 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: mBERT-naamapdam-fine-tuned
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mBERT-naamapdam-fine-tuned
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4625
- Precision: 0.8060
- Recall: 0.8246
- F1: 0.8152
- Accuracy: 0.9173
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 128
- eval_batch_size: 256
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.3625 | 0.26 | 1000 | 0.3300 | 0.7651 | 0.7809 | 0.7729 | 0.8964 |
| 0.3099 | 0.51 | 2000 | 0.3070 | 0.7708 | 0.8041 | 0.7871 | 0.9002 |
| 0.2954 | 0.77 | 3000 | 0.2962 | 0.7793 | 0.8036 | 0.7913 | 0.9041 |
| 0.283 | 1.03 | 4000 | 0.2958 | 0.7843 | 0.8153 | 0.7995 | 0.9066 |
| 0.265 | 1.29 | 5000 | 0.2873 | 0.7930 | 0.8065 | 0.7997 | 0.9069 |
| 0.2613 | 1.54 | 6000 | 0.2838 | 0.7789 | 0.8289 | 0.8031 | 0.9092 |
| 0.2635 | 1.8 | 7000 | 0.2790 | 0.7902 | 0.8252 | 0.8073 | 0.9088 |
| 0.2574 | 2.06 | 8000 | 0.2946 | 0.7887 | 0.8345 | 0.8110 | 0.9098 |
| 0.2355 | 2.31 | 9000 | 0.2859 | 0.7975 | 0.8152 | 0.8063 | 0.9105 |
| 0.2361 | 2.57 | 10000 | 0.2806 | 0.7883 | 0.8313 | 0.8092 | 0.9104 |
| 0.2361 | 2.83 | 11000 | 0.2805 | 0.7931 | 0.8279 | 0.8101 | 0.9123 |
| 0.2268 | 3.08 | 12000 | 0.2934 | 0.7959 | 0.8323 | 0.8137 | 0.9130 |
| 0.2106 | 3.34 | 13000 | 0.2862 | 0.7934 | 0.8311 | 0.8118 | 0.9121 |
| 0.2106 | 3.6 | 14000 | 0.2876 | 0.8009 | 0.8332 | 0.8167 | 0.9143 |
| 0.2131 | 3.86 | 15000 | 0.2777 | 0.8015 | 0.8242 | 0.8127 | 0.9123 |
| 0.1993 | 4.11 | 16000 | 0.2999 | 0.7920 | 0.8311 | 0.8111 | 0.9113 |
| 0.1872 | 4.37 | 17000 | 0.2984 | 0.8003 | 0.8365 | 0.8180 | 0.9143 |
| 0.1861 | 4.63 | 18000 | 0.2894 | 0.7976 | 0.8321 | 0.8145 | 0.9151 |
| 0.1916 | 4.88 | 19000 | 0.2909 | 0.7958 | 0.8300 | 0.8125 | 0.9143 |
| 0.1745 | 5.14 | 20000 | 0.3075 | 0.7906 | 0.8386 | 0.8139 | 0.9136 |
| 0.1649 | 5.4 | 21000 | 0.2986 | 0.8055 | 0.8199 | 0.8127 | 0.9147 |
| 0.1678 | 5.66 | 22000 | 0.3043 | 0.7988 | 0.8303 | 0.8142 | 0.9147 |
| 0.1688 | 5.91 | 23000 | 0.2950 | 0.8026 | 0.8269 | 0.8146 | 0.9155 |
| 0.153 | 6.17 | 24000 | 0.3231 | 0.7995 | 0.8305 | 0.8147 | 0.9150 |
| 0.1468 | 6.43 | 25000 | 0.3145 | 0.7954 | 0.8326 | 0.8136 | 0.9156 |
| 0.1478 | 6.68 | 26000 | 0.3222 | 0.8034 | 0.8307 | 0.8168 | 0.9160 |
| 0.1489 | 6.94 | 27000 | 0.3184 | 0.8019 | 0.8318 | 0.8166 | 0.9161 |
| 0.1311 | 7.2 | 28000 | 0.3336 | 0.8022 | 0.8278 | 0.8148 | 0.9168 |
| 0.1298 | 7.46 | 29000 | 0.3430 | 0.8050 | 0.8281 | 0.8164 | 0.9164 |
| 0.1319 | 7.71 | 30000 | 0.3374 | 0.8005 | 0.8257 | 0.8129 | 0.9152 |
| 0.1312 | 7.97 | 31000 | 0.3320 | 0.8019 | 0.8353 | 0.8183 | 0.9173 |
| 0.1144 | 8.23 | 32000 | 0.3539 | 0.8007 | 0.8309 | 0.8155 | 0.9160 |
| 0.1132 | 8.48 | 33000 | 0.3581 | 0.7940 | 0.8376 | 0.8152 | 0.9158 |
| 0.1159 | 8.74 | 34000 | 0.3566 | 0.8032 | 0.8355 | 0.8191 | 0.9182 |
| 0.117 | 9.0 | 35000 | 0.3384 | 0.8113 | 0.8205 | 0.8159 | 0.9166 |
| 0.0996 | 9.25 | 36000 | 0.3637 | 0.8060 | 0.8256 | 0.8156 | 0.9166 |
| 0.1004 | 9.51 | 37000 | 0.3687 | 0.8043 | 0.8147 | 0.8095 | 0.9152 |
| 0.1015 | 9.77 | 38000 | 0.3715 | 0.8017 | 0.8359 | 0.8185 | 0.9173 |
| 0.1001 | 10.03 | 39000 | 0.3826 | 0.8047 | 0.8288 | 0.8166 | 0.9174 |
| 0.0874 | 10.28 | 40000 | 0.3857 | 0.8087 | 0.8231 | 0.8158 | 0.9168 |
| 0.0892 | 10.54 | 41000 | 0.3817 | 0.8069 | 0.8221 | 0.8145 | 0.9165 |
| 0.0895 | 10.8 | 42000 | 0.3800 | 0.8107 | 0.8291 | 0.8198 | 0.9183 |
| 0.0868 | 11.05 | 43000 | 0.4099 | 0.8032 | 0.8297 | 0.8162 | 0.9177 |
| 0.0777 | 11.31 | 44000 | 0.4099 | 0.8059 | 0.8255 | 0.8156 | 0.9170 |
| 0.0781 | 11.57 | 45000 | 0.4077 | 0.8044 | 0.8335 | 0.8187 | 0.9186 |
| 0.0779 | 11.83 | 46000 | 0.4172 | 0.8050 | 0.8243 | 0.8145 | 0.9161 |
| 0.0759 | 12.08 | 47000 | 0.4230 | 0.8034 | 0.8244 | 0.8138 | 0.9158 |
| 0.0691 | 12.34 | 48000 | 0.4286 | 0.8048 | 0.8221 | 0.8134 | 0.9162 |
| 0.0676 | 12.6 | 49000 | 0.4251 | 0.8091 | 0.8287 | 0.8188 | 0.9185 |
| 0.0695 | 12.85 | 50000 | 0.4289 | 0.8043 | 0.8284 | 0.8161 | 0.9168 |
| 0.0663 | 13.11 | 51000 | 0.4431 | 0.8060 | 0.8246 | 0.8152 | 0.9168 |
| 0.0618 | 13.37 | 52000 | 0.4484 | 0.8054 | 0.8214 | 0.8133 | 0.9162 |
| 0.0614 | 13.62 | 53000 | 0.4421 | 0.8044 | 0.8230 | 0.8136 | 0.9166 |
| 0.0611 | 13.88 | 54000 | 0.4468 | 0.8066 | 0.8231 | 0.8148 | 0.9166 |
| 0.0582 | 14.14 | 55000 | 0.4606 | 0.8055 | 0.8244 | 0.8148 | 0.9173 |
| 0.0552 | 14.4 | 56000 | 0.4642 | 0.8055 | 0.8274 | 0.8163 | 0.9175 |
| 0.0553 | 14.65 | 57000 | 0.4633 | 0.8083 | 0.8248 | 0.8165 | 0.9175 |
| 0.0556 | 14.91 | 58000 | 0.4625 | 0.8060 | 0.8246 | 0.8152 | 0.9173 |
### Framework versions
- Transformers 4.27.4
- Pytorch 2.0.0+cu117
- Datasets 2.11.0
- Tokenizers 0.13.3
|
Connor-tech/bert_cn_finetuning
|
[
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
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| 27 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-base-multilingual-NER-naamapdam-fine-tuned
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-multilingual-NER-naamapdam-fine-tuned
This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3852
- Precision: 0.7940
- Recall: 0.8182
- F1: 0.8059
- Accuracy: 0.9124
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 256
- eval_batch_size: 512
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.3757 | 0.51 | 1000 | 0.3173 | 0.7666 | 0.7858 | 0.7761 | 0.8984 |
| 0.3062 | 1.03 | 2000 | 0.3020 | 0.7791 | 0.7981 | 0.7885 | 0.9026 |
| 0.2793 | 1.54 | 3000 | 0.2962 | 0.7827 | 0.8021 | 0.7923 | 0.9059 |
| 0.2755 | 2.06 | 4000 | 0.2973 | 0.7768 | 0.8122 | 0.7941 | 0.9048 |
| 0.2529 | 2.57 | 5000 | 0.2879 | 0.7747 | 0.8201 | 0.7968 | 0.9057 |
| 0.2483 | 3.08 | 6000 | 0.3025 | 0.7714 | 0.8298 | 0.7996 | 0.9079 |
| 0.2294 | 3.6 | 7000 | 0.2899 | 0.7877 | 0.8211 | 0.8041 | 0.9105 |
| 0.2252 | 4.11 | 8000 | 0.2952 | 0.7850 | 0.8185 | 0.8014 | 0.9090 |
| 0.2088 | 4.63 | 9000 | 0.2932 | 0.7851 | 0.8234 | 0.8038 | 0.9090 |
| 0.2046 | 5.14 | 10000 | 0.2998 | 0.7931 | 0.8215 | 0.8071 | 0.9117 |
| 0.1909 | 5.66 | 11000 | 0.3029 | 0.7925 | 0.8240 | 0.8080 | 0.9112 |
| 0.1857 | 6.17 | 12000 | 0.3160 | 0.7903 | 0.8228 | 0.8062 | 0.9108 |
| 0.1744 | 6.68 | 13000 | 0.3099 | 0.7858 | 0.8259 | 0.8054 | 0.9115 |
| 0.1686 | 7.2 | 14000 | 0.3199 | 0.7859 | 0.8246 | 0.8048 | 0.9097 |
| 0.1613 | 7.71 | 15000 | 0.3161 | 0.7941 | 0.8179 | 0.8058 | 0.9121 |
| 0.1538 | 8.23 | 16000 | 0.3294 | 0.7903 | 0.8221 | 0.8059 | 0.9110 |
| 0.1475 | 8.74 | 17000 | 0.3260 | 0.7935 | 0.8248 | 0.8089 | 0.9129 |
| 0.1429 | 9.25 | 18000 | 0.3378 | 0.7958 | 0.8210 | 0.8082 | 0.9130 |
| 0.1369 | 9.77 | 19000 | 0.3402 | 0.7905 | 0.8240 | 0.8069 | 0.9118 |
| 0.1302 | 10.28 | 20000 | 0.3573 | 0.7865 | 0.8269 | 0.8062 | 0.9114 |
| 0.1276 | 10.8 | 21000 | 0.3564 | 0.7924 | 0.8208 | 0.8063 | 0.9117 |
| 0.122 | 11.31 | 22000 | 0.3590 | 0.7939 | 0.8274 | 0.8103 | 0.9130 |
| 0.1181 | 11.83 | 23000 | 0.3660 | 0.7974 | 0.8234 | 0.8102 | 0.9132 |
| 0.1141 | 12.34 | 24000 | 0.3695 | 0.7921 | 0.8208 | 0.8062 | 0.9112 |
| 0.1118 | 12.85 | 25000 | 0.3649 | 0.7942 | 0.8188 | 0.8063 | 0.9114 |
| 0.1081 | 13.37 | 26000 | 0.3781 | 0.7980 | 0.8149 | 0.8064 | 0.9124 |
| 0.1054 | 13.88 | 27000 | 0.3800 | 0.7913 | 0.8179 | 0.8044 | 0.9120 |
| 0.1023 | 14.4 | 28000 | 0.3857 | 0.7942 | 0.8207 | 0.8072 | 0.9128 |
| 0.101 | 14.91 | 29000 | 0.3852 | 0.7940 | 0.8182 | 0.8059 | 0.9124 |
### Framework versions
- Transformers 4.27.4
- Pytorch 2.0.0+cu117
- Datasets 2.11.0
- Tokenizers 0.13.3
|
Connorvr/BrightBot-small
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
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| 7 | null |
---
license: creativeml-openrail-m
language:
- cs
tags:
- art
- legal
---
# Hodiny – CELÝ FILM ONLINE ZDARMA CZ/SK DABING
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[](https://fliktv.net/cs/movie/1085103)
poslední aktualizace: 30. dubna 2023
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| 𝟜𝕂 𝕌ℍ𝔻 | 𝟙𝟘𝟠𝟘ℙ 𝔽𝕌𝕃𝕃 ℍ𝔻 | 𝟟𝟚𝟘ℙ ℍ𝔻 | 𝕄𝕂𝕍 | 𝕄ℙ𝟜 | 𝔻𝕍𝔻 | 𝔹𝕝𝕦-ℝ𝕒𝕪 |
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❏ STREAMOVAT MÉDIA ❏
Streamovaná média jsou multimédia, která jsou nepřetržitě přijímána a prezentována koncovému uživateli, zatímco jsou dodávána poskytovatelem. Sloveso streamovat odkazuje na proces doručování nebo získávání médií tímto způsobem. [Potřebné upřesnění] Streamování se týká způsobu doručení média, nikoli média samotného. Rozdíl mezi způsobem doručování a vysílacím médiem se týká zejména telekomunikačních sítí, protože většina doručovacích systémů má streamingový charakter (např. rádio, televize, streamingové aplikace) nebo nestreamingový charakter (např. knihy, videokazety, audio CD). Streamování obsahu přes internet přináší problémy. Uživatelé, jejichž připojení k internetu nemá dostatečnou šířku pásma, mohou například zaznamenat zamrzání, zpoždění nebo pomalé ukládání obsahu do vyrovnávací paměti. A uživatelé, kteří nemají kompatibilní hardwarové nebo softwarové systémy, nemusí být schopni streamovat nějaký obsah.
Živé vysílání je poskytování internetového obsahu v reálném čase, podobně jako živé televizní vysílání obsahu prostřednictvím rádiových vln prostřednictvím televizního signálu. Online živé vysílání vyžaduje určitou formu zdrojového média (např. videokameru, audio rozhraní, software pro snímání obrazovky), kodér pro digitalizaci obsahu, vydavatele médií a síť pro doručování obsahu pro distribuci a doručování obsahu. Přímé přenosy není nutné nahrávat na začátku, i když často ano. Streamování je alternativou ke stahování souborů, což je proces, při kterém koncový uživatel získá celý soubor obsahu před jeho zobrazením nebo poslechem. Streamování umožňuje koncovému uživateli používat svůj přehrávač médií ke spuštění přehrávání digitálního videa nebo digitálního zvukového obsahu před přenesením celého souboru. Termín „streamingová média“ může odkazovat na média jiná než video a zvuk, jako jsou živé titulky, kazety se zprávami a text v reálném čase, které jsou považovány za „streamovaný text“.
❏ OBSAH AUTORSKÝCH PRÁV ❏
Autorské právo je druh duševního vlastnictví, který dává vlastníkovi výhradní právo pořídit si rozmnoženinu tvůrčího díla, obvykle na omezenou dobu. Tvůrčí práce může být literární, výtvarná, vzdělávací nebo hudební. Ochrana autorských práv je určena k ochraně původního vyjádření myšlenky jako tvůrčího díla, nikoli však myšlenky samotné. Autorská práva jsou omezena ohledy veřejného zájmu, jako je americká doktrína fair use.
Některé jurisdikce vyžadují, abyste „opravili“ díla chráněná autorským právem v hmotné podobě. Často jej sdílí více autorů, každý hWatch Dunes je soubor práv k použití nebo licencování díla, kteří jsou běžně označováni jako práva hWatch Duneers. [pochvalná zmínka potřebovaný] Tato práva často zahrnují reprodukci, odvozenou kontrolu, distribuci, veřejné předvádění a osobní práva, jako je uvádění zdroje.
Autorská práva mohou být udělena podle veřejného práva a v tomto případě jsou považována za „územní práva“. To znamená, že autorská práva udělená právem dané země nepřesahují území této konkrétní jurisdikce. Tyto typy autorských práv se v jednotlivých zemích liší; mnoho zemí, a někdy i velká skupina zemí, má dohody s jinými zeměmi o postupech, které je třeba dodržet, když práce „překročí“ státní hranice nebo když jsou vnitrostátní zákony nekonzistentní . Veřejná autorská práva obvykle vyprší 50 až 100 let po smrti tvůrce, v závislosti na jurisdikci. Některé země vyžadují určité formality týkající se autorských práv pro stanovení autorských práv, jiné uznávají autorská práva v jakémkoli dokončeném díle bez formální registrace.
Obecně se má za to, že autorská práva jsou zásadní pro podporu kulturní rozmanitosti a kreativity. Na rozdíl od všeobecného přesvědčení však Parc tvrdí, že napodobování a kopírování neomezuje kreativitu ani kulturní rozmanitost, ale ve skutečnosti je navíc podporuje. Tento argument byl podpořen mnoha příklady jako Millet a Van Gogh, Picasso, Manet a Monet atd.
❏ SERVIS ZBOŽÍ ❏
Kredit (z latinského credit, „(ona/ona) věří“) je trust, který umožňuje jedné straně poskytnout peníze nebo zdroje druhé straně, přičemž druhá strana okamžitě nevrátí první straně (která vytváří dluh), ale slíbí splatit nebo splatit tyto zdroje (nebo jiné materiály stejné hodnoty) později Jinými slovy, úvěr je způsob, jak učinit formality reciproční, právně vymahatelné a rozšířitelné na velkou skupinu nepříbuzných jednotlivců .
Převáděné prostředky mohou mít finanční povahu (např. poskytnutí půjčky) nebo představovat zboží či služby (např. spotřebitelský úvěr). Kredit zahrnuje jakoukoli formu odložené platby. Půjčku poskytuje věřitel, také známý jako věřitel, dlužník, také známý jako dlužník .
|
ConstellationBoi/Oop
|
[] | null |
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| 0 | null |
---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: taNER-500-naamapdam-fine-tuned
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# taNER-500-naamapdam-fine-tuned
This model is a fine-tuned version of [livinNector/tabert-500](https://huggingface.co/livinNector/tabert-500) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3466
- Precision: 0.7858
- Recall: 0.8156
- F1: 0.8004
- Accuracy: 0.9134
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 256
- eval_batch_size: 512
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.37 | 0.51 | 1000 | 0.3241 | 0.7491 | 0.7745 | 0.7616 | 0.8951 |
| 0.3118 | 1.03 | 2000 | 0.3120 | 0.7631 | 0.7862 | 0.7745 | 0.8998 |
| 0.2877 | 1.54 | 3000 | 0.2994 | 0.7679 | 0.7918 | 0.7797 | 0.9027 |
| 0.284 | 2.06 | 4000 | 0.2977 | 0.7643 | 0.7960 | 0.7798 | 0.9026 |
| 0.2631 | 2.57 | 5000 | 0.2900 | 0.7716 | 0.8001 | 0.7856 | 0.9040 |
| 0.259 | 3.08 | 6000 | 0.2958 | 0.7739 | 0.8058 | 0.7895 | 0.9050 |
| 0.2426 | 3.6 | 7000 | 0.2850 | 0.7795 | 0.8042 | 0.7917 | 0.9072 |
| 0.2378 | 4.11 | 8000 | 0.2910 | 0.7740 | 0.8069 | 0.7901 | 0.9059 |
| 0.2231 | 4.63 | 9000 | 0.2878 | 0.7788 | 0.8106 | 0.7944 | 0.9077 |
| 0.2188 | 5.14 | 10000 | 0.2941 | 0.7752 | 0.8101 | 0.7923 | 0.9087 |
| 0.2066 | 5.66 | 11000 | 0.2928 | 0.7721 | 0.8147 | 0.7928 | 0.9079 |
| 0.2008 | 6.17 | 12000 | 0.3048 | 0.7798 | 0.8141 | 0.7966 | 0.9088 |
| 0.1902 | 6.68 | 13000 | 0.2987 | 0.7834 | 0.8108 | 0.7969 | 0.9099 |
| 0.1843 | 7.2 | 14000 | 0.3055 | 0.7784 | 0.8137 | 0.7957 | 0.9101 |
| 0.1775 | 7.71 | 15000 | 0.2991 | 0.7762 | 0.8155 | 0.7953 | 0.9096 |
| 0.1694 | 8.23 | 16000 | 0.3117 | 0.7876 | 0.8137 | 0.8004 | 0.9120 |
| 0.1631 | 8.74 | 17000 | 0.3085 | 0.7761 | 0.8210 | 0.7979 | 0.9121 |
| 0.1585 | 9.25 | 18000 | 0.3144 | 0.7851 | 0.8063 | 0.7955 | 0.9108 |
| 0.1528 | 9.77 | 19000 | 0.3086 | 0.7834 | 0.8169 | 0.7998 | 0.9124 |
| 0.1458 | 10.28 | 20000 | 0.3167 | 0.7773 | 0.8208 | 0.7985 | 0.9114 |
| 0.143 | 10.8 | 21000 | 0.3202 | 0.7822 | 0.8134 | 0.7975 | 0.9123 |
| 0.1368 | 11.31 | 22000 | 0.3299 | 0.7798 | 0.8176 | 0.7983 | 0.9112 |
| 0.1337 | 11.83 | 23000 | 0.3369 | 0.7857 | 0.8151 | 0.8002 | 0.9131 |
| 0.1289 | 12.34 | 24000 | 0.3366 | 0.7855 | 0.8148 | 0.7999 | 0.9128 |
| 0.1257 | 12.85 | 25000 | 0.3316 | 0.7837 | 0.8172 | 0.8001 | 0.9129 |
| 0.122 | 13.37 | 26000 | 0.3415 | 0.7880 | 0.8120 | 0.7998 | 0.9136 |
| 0.1191 | 13.88 | 27000 | 0.3414 | 0.7867 | 0.8182 | 0.8021 | 0.9139 |
| 0.1157 | 14.4 | 28000 | 0.3481 | 0.7863 | 0.8174 | 0.8016 | 0.9136 |
| 0.1153 | 14.91 | 29000 | 0.3466 | 0.7858 | 0.8156 | 0.8004 | 0.9134 |
### Framework versions
- Transformers 4.27.4
- Pytorch 2.0.0+cu117
- Datasets 2.11.0
- Tokenizers 0.13.3
|
Contrastive-Tension/BERT-Base-CT
|
[
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
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}
}
| 16 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-finetuned-vk
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-vk
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2150
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.3219 | 1.0 | 12032 | 0.2150 |
### Framework versions
- Transformers 4.27.4
- Pytorch 1.13.0
- Datasets 2.1.0
- Tokenizers 0.13.2
|
Contrastive-Tension/BERT-Base-NLI-CT
|
[
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
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},
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}
}
}
| 9 | null |
---
library_name: diffusers
pipeline_tag: text-to-image
---
FlaxControNetModel trained with battlemaps images.
|
Contrastive-Tension/BERT-Base-Swe-CT-STSb
|
[
"pytorch",
"tf",
"jax",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"prefix": null
}
}
}
| 126 | null |
---
license: other
tags:
- generated_from_trainer
model-index:
- name: segformer-b0-scene-parse-150-MASKED5
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# segformer-b0-scene-parse-150-MASKED5
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 4.4715
- Mean Iou: 0.0161
- Mean Accuracy: 0.0620
- Overall Accuracy: 0.2198
- Per Category Iou: [0.26811535900844996, 0.13361073012813385, 0.08961412470909454, 0.1128982647416514, 0.0583985070987828, 0.07100614244377887, 0.19794722756159697, 0.012499372941156798, 0.13135492375006866, 0.07853702343048412, 0.0, 0.0, 0.000257651881995438, nan, 0.00015613885949237522, 0.00816753033501802, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0]
- Per Category Accuracy: [0.5251087767953342, 0.3576830362495917, 0.09867066511502999, 0.24665550110621756, 0.14805877178383775, 0.08033983486897212, 0.8465623033354311, 0.014047451256753583, 0.38394991259676786, 0.20428242788886922, 0.0, 0.0, 0.0002825539553398542, nan, 0.00015841165909810963, 0.0103471565581521, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 6e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Per Category Iou | Per Category Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
| 4.8994 | 1.0 | 20 | 4.9538 | 0.0033 | 0.0158 | 0.0601 | [0.09446474142430365, 0.09207604302632935, 0.0, 0.0027654472166947293, 0.00022686472943915094, 0.024674639716049208, 0.05004409798287291, 0.0, 0.0659773981315997, 0.000611224966742171, 0.0033934631184140023, 0.0, 0.00017743332055813424, 0.0, 0.0, 0.0035134839707053516, nan, 0.0016311966356569389, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.014299143136049748, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0] | [0.11622029935531653, 0.15137832797879686, 0.0, 0.0028017285941732317, 0.00027341242481158316, 0.024855809501017113, 0.069186595342983, 0.0, 0.3015857443544647, 0.0018049902672093434, 0.0074461136512083605, 0.0, 0.0002077602612793046, nan, 0.0, 0.006078616776982304, nan, 0.003389291955727374, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.057269041413263826, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] |
| 4.8245 | 2.0 | 40 | 4.7740 | 0.0083 | 0.0441 | 0.1807 | [0.2585690813978081, 0.14108215993332457, 0.03419684396156098, 0.023996082272282077, 0.027117104130902927, 0.062083130688529144, 0.14413986579778207, 0.003936397464167586, 0.08274324585033332, 0.024486949334228778, 5.267038870746866e-05, 0.0, 0.0028745859227420813, 0.0, 8.715812226541392e-05, 0.004454446326241793, 0.0, 0.011132801332995728, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, 0.0, 0.0, 0.0] | [0.48883868337236625, 0.37235736052362817, 0.03638140423119386, 0.02735562310030395, 0.07379757970566558, 0.06843125523513223, 0.6446664568911264, 0.00429410382898755, 0.20655702614962007, 0.06710316758095912, 8.708904855214457e-05, 0.0, 0.003490372389492317, nan, 8.800647727672756e-05, 0.0051870863163582335, nan, 0.07218132711963142, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] |
| 4.5392 | 3.0 | 60 | 4.5845 | 0.0136 | 0.0563 | 0.2071 | [0.2557528891910265, 0.13708037604006187, 0.08692430869243087, 0.09462758833831256, 0.03757492466907787, 0.07904498578899012, 0.1908769498813005, 0.0028976120267943887, 0.1151440589593073, 0.045923559494518804, 0.0, 0.0, 0.00032954989615346296, nan, 0.0, 0.010276481299315599, nan, 0.0010769035854554668, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] | [0.47425697914676446, 0.38188388718673894, 0.09495941873302897, 0.18992825093563262, 0.09620551104348446, 0.08812253200909417, 0.7368628067967276, 0.0031947380784590087, 0.43822910349256183, 0.12617235887453548, 0.0, 0.0, 0.0003573476494004039, nan, 0.0, 0.013264892611103607, nan, 0.0018005613514801672, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] |
| 4.5062 | 4.0 | 80 | 4.4950 | 0.0154 | 0.0587 | 0.2151 | [0.26153152576800204, 0.13111120499063553, 0.0587806904353667, 0.1163810590985491, 0.05045636959256171, 0.07593274224946232, 0.20581123895280973, 0.011514468896205164, 0.12641034098405404, 0.060098757566103854, 0.0, 0.0, 0.0001400405380504883, nan, 3.468970062788358e-05, 0.009957528094454266, nan, 0.00014301548142586434, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0] | [0.5153390546612014, 0.38497256575329436, 0.062016978485960675, 0.2491160597977793, 0.1301799767005064, 0.08550915400263252, 0.7767463813719321, 0.012487667371388301, 0.39649851949627196, 0.1335338878074677, 0.0, 0.0, 0.00015789779857227148, nan, 3.5202590910691024e-05, 0.013237876536539241, nan, 0.00021183074723296087, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] |
| 4.2543 | 5.0 | 100 | 4.4715 | 0.0161 | 0.0620 | 0.2198 | [0.26811535900844996, 0.13361073012813385, 0.08961412470909454, 0.1128982647416514, 0.0583985070987828, 0.07100614244377887, 0.19794722756159697, 0.012499372941156798, 0.13135492375006866, 0.07853702343048412, 0.0, 0.0, 0.000257651881995438, nan, 0.00015613885949237522, 0.00816753033501802, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0] | [0.5251087767953342, 0.3576830362495917, 0.09867066511502999, 0.24665550110621756, 0.14805877178383775, 0.08033983486897212, 0.8465623033354311, 0.014047451256753583, 0.38394991259676786, 0.20428242788886922, 0.0, 0.0, 0.0002825539553398542, nan, 0.00015841165909810963, 0.0103471565581521, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Contrastive-Tension/BERT-Distil-CT-STSb
|
[
"pytorch",
"tf",
"distilbert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"DistilBertModel"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
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"max_length": null
},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
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}
}
}
| 1 | 2023-04-30T16:22:30Z |
Not official! This are diffusers weights for https://civitai.com/models/7240/meinamix
Based on Stable Diffusion v1.5
|
Contrastive-Tension/BERT-Distil-CT
|
[
"pytorch",
"tf",
"distilbert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"DistilBertForMaskedLM"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 9 | 2023-04-30T16:25:31Z |
Not official! This are diffusers weights for https://civitai.com/models/50294/dreamscapes-and-dragonfire
Based on Stable Diffusion v1.5
|
Contrastive-Tension/BERT-Large-CT
|
[
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
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"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 5 | 2023-04-30T16:27:27Z |
---
license: openrail
datasets:
- humarin/chatgpt-paraphrases
language:
- en
library_name: transformers
inference:
parameters:
num_beams: 5
num_beam_groups: 5
num_return_sequences: 5
repetition_penalty: 10.01
diversity_penalty: 3.01
no_repeat_ngram_size: 2
temperature: 0.7
max_length: 128
widget:
- text: What are the best places to see in New York?
example_title: New York tourist attractions
- text: When should I go to the doctor?
example_title: Doctor's time
- text: >-
Rammstein's album Mutter was recorded in the south of France in May and June
2000, and mixed in Stockholm in October of that year.
example_title: Rammstein's album Mutter
pipeline_tag: text2text-generation1
duplicated_from: humarin/chatgpt_paraphraser_on_T5_base
---
This model was trained on our [ChatGPT paraphrase dataset](https://huggingface.co/datasets/humarin/chatgpt-paraphrases).
This dataset is based on the [Quora paraphrase question](https://www.kaggle.com/competitions/quora-question-pairs), texts from the [SQUAD 2.0](https://huggingface.co/datasets/squad_v2) and the [CNN news dataset](https://huggingface.co/datasets/cnn_dailymail).
This model is based on the T5-base model. We used "transfer learning" to get our model to generate paraphrases as well as ChatGPT. Now we can say that this is one of the best paraphrases of the Hugging Face.
[Kaggle](https://www.kaggle.com/datasets/vladimirvorobevv/chatgpt-paraphrases) link
## Deploying example
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
device = "cuda"
tokenizer = AutoTokenizer.from_pretrained("humarin/chatgpt_paraphraser_on_T5_base")
model = AutoModelForSeq2SeqLM.from_pretrained("humarin/chatgpt_paraphraser_on_T5_base").to(device)
def paraphrase(
question,
num_beams=5,
num_beam_groups=5,
num_return_sequences=5,
repetition_penalty=10.0,
diversity_penalty=3.0,
no_repeat_ngram_size=2,
temperature=0.7,
max_length=128
):
input_ids = tokenizer(
f'paraphrase: {question}',
return_tensors="pt", padding="longest",
max_length=max_length,
truncation=True,
).input_ids
outputs = model.generate(
input_ids, temperature=temperature, repetition_penalty=repetition_penalty,
num_return_sequences=num_return_sequences, no_repeat_ngram_size=no_repeat_ngram_size,
num_beams=num_beams, num_beam_groups=num_beam_groups,
max_length=max_length, diversity_penalty=diversity_penalty
)
res = tokenizer.batch_decode(outputs, skip_special_tokens=True)
return res
```
## Usage examples
**Input:**
```python
text = 'What are the best places to see in New York?'
paraphrase(text)
```
**Output:**
```python
['What are some must-see places in New York?',
'Can you suggest some must-see spots in New York?',
'Where should one go to experience the best NYC has to offer?',
'Which places should I visit in New York?',
'What are the top destinations to explore in New York?']
```
**Input:**
```python
text = "Rammstein's album Mutter was recorded in the south of France in May and June 2000, and mixed in Stockholm in October of that year."
paraphrase(text)
```
**Output:**
```python
['In May and June 2000, Rammstein travelled to the south of France to record his album Mutter, which was mixed in Stockholm in October of that year.',
'The album Mutter by Rammstein was recorded in the south of France during May and June 2000, with mixing taking place in Stockholm in October of that year.',
'The album Mutter by Rammstein was recorded in the south of France during May and June 2000, with mixing taking place in Stockholm in October of that year. It',
'Mutter, the album released by Rammstein, was recorded in southern France during May and June 2000, with mixing taking place between October and September.',
'In May and June 2000, Rammstein recorded his album Mutter in the south of France, with the mix being made at Stockholm during October.']
```
## Train parameters
```python
epochs = 5
batch_size = 64
max_length = 128
lr = 5e-5
batches_qty = 196465
betas = (0.9, 0.999)
eps = 1e-08
```
### BibTeX entry and citation info
```bibtex
@inproceedings{chatgpt_paraphraser,
author={Vladimir Vorobev, Maxim Kuznetsov},
title={A paraphrasing model based on ChatGPT paraphrases},
year={2023}
}
```
|
Crumped/imdb-simpleRNN
|
[
"keras"
] | null |
{
"architectures": null,
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},
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},
"text-generation": {
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},
"translation_en_to_de": {
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"max_length": null,
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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}
}
}
| 0 | null |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -0.61 +/- 0.22
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
DSI/ar_emotion_6
|
[
"pytorch",
"bert",
"transformers"
] | null |
{
"architectures": [
"BertForMultiLabelSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 1 | null |
---
tags:
- autotrain
- text-regression
language:
- es
widget:
- text: "I love AutoTrain 🤗"
datasets:
- alexisbaladon/autotrain-data-huhu-prejudice
co2_eq_emissions:
emissions: 0.0016647063749410328
---
# Model Trained Using AutoTrain
- Problem type: Single Column Regression
- Model ID: 54234127237
- CO2 Emissions (in grams): 0.0017
## Validation Metrics
- Loss: 0.514
- MSE: 0.514
- MAE: 0.552
- R2: 0.268
- RMSE: 0.717
- Explained Variance: 0.270
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/alexisbaladon/autotrain-huhu-prejudice-54234127237
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("alexisbaladon/autotrain-huhu-prejudice-54234127237", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("alexisbaladon/autotrain-huhu-prejudice-54234127237", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
```
|
DTAI-KULeuven/robbertje-1-gb-shuffled
|
[
"pytorch",
"roberta",
"fill-mask",
"nl",
"dataset:oscar",
"dataset:oscar (NL)",
"dataset:dbrd",
"dataset:lassy-ud",
"dataset:europarl-mono",
"dataset:conll2002",
"arxiv:2101.05716",
"transformers",
"Dutch",
"Flemish",
"RoBERTa",
"RobBERT",
"RobBERTje",
"license:mit",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
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"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 7 | null |
Access to model sirius93/finetuning-sentiment-model-3000-samples is restricted and you are not in the authorized list. Visit https://huggingface.co/sirius93/finetuning-sentiment-model-3000-samples to ask for access.
|
Davlan/bert-base-multilingual-cased-finetuned-yoruba
|
[
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
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}
| 21 | null |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-cartpole
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
Davlan/xlm-roberta-base-finetuned-igbo
|
[
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"XLMRobertaForMaskedLM"
],
"model_type": "xlm-roberta",
"task_specific_params": {
"conversational": {
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},
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}
}
}
| 68 | null |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="ravkumar/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
Dawit/DialogGPT-small-ironman
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
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}
}
| 7 | 2023-04-30T21:33:12Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: expert-uspto
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# expert-uspto
This model is a fine-tuned version of [EleutherAI/pythia-1b-deduped](https://huggingface.co/EleutherAI/pythia-1b-deduped) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2220
- Accuracy: 0.5362
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 1000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.2735 | 0.01 | 200 | 2.2464 | 0.5325 |
| 2.2557 | 0.01 | 400 | 2.2417 | 0.5331 |
| 2.2342 | 0.02 | 600 | 2.2342 | 0.5344 |
| 2.2241 | 0.03 | 800 | 2.2267 | 0.5355 |
| 2.229 | 0.03 | 1000 | 2.2220 | 0.5362 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu117
- Datasets 2.11.0
- Tokenizers 0.13.3
|
Declan/Breitbart_model_v5
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
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"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
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},
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}
| 3 | null |
---
language:
- en
metrics:
- bleu
pipeline_tag: text2text-generation
datasets:
- fka/awesome-chatgpt-prompts
library_name: adapter-transformers
---
|
Declan/ChicagoTribune_model_v3
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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}
| 3 | null |
---
language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1443730494055665669/xM8jhl6q_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">JAX</div>
<div style="text-align: center; font-size: 14px;">@jax</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from JAX.
| Data | JAX |
| --- | --- |
| Tweets downloaded | 2691 |
| Retweets | 618 |
| Short tweets | 401 |
| Tweets kept | 1672 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/cjhi2ndz/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @jax's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/tcl10aar) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/tcl10aar/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/jax')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
Declan/ChicagoTribune_model_v7
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
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"max_length": null
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}
| 7 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: my_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0373
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Tokenizers 0.13.3
|
Declan/HuffPost_model_v4
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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}
}
| 3 | null |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 264.70 +/- 23.24
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Declan/NPR_model_v4
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
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},
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},
"translation_en_to_fr": {
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},
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}
| 3 | null |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-flappyBird
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 43.10 +/- 31.67
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
Declan/NPR_model_v5
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
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}
| 7 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: expert-arxiv
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# expert-arxiv
This model is a fine-tuned version of [EleutherAI/pythia-1b-deduped](https://huggingface.co/EleutherAI/pythia-1b-deduped) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8797
- Accuracy: 0.5852
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 1000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.8752 | 0.01 | 200 | 1.9087 | 0.5805 |
| 1.8809 | 0.01 | 400 | 1.9018 | 0.5815 |
| 1.9102 | 0.02 | 600 | 1.8933 | 0.5829 |
| 1.8764 | 0.02 | 800 | 1.8851 | 0.5843 |
| 1.8694 | 0.03 | 1000 | 1.8797 | 0.5852 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu117
- Datasets 2.11.0
- Tokenizers 0.13.3
|
Declan/NPR_model_v8
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
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"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
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}
| 3 | null |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 277.28 +/- 17.18
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Declan/NewYorkPost_model_v1
|
[] | null |
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"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
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"prefix": null
}
}
}
| 0 | null |
StableDiffusion 2.1 converted to fp16 ONNX model to run GPU accelerated NodeJS code https://github.com/dakenf/stable-diffusion-nodejs
```javascript
import * as tf from "@tensorflow/tfjs-node"
import { StableDiffusionPipeline } from 'stable-diffusion-nodejs'
const pipe = await StableDiffusionPipeline.fromPretrained(
'directml', // can be 'cuda' on linux or 'cpu' on mac os
'aislamov/stable-diffusion-2-1-onnx', // relative path or huggingface repo with onnx model
)
const image = await pipe.run("A photo of a cat", undefined, 1, 9, 30)
const png = await tf.node.encodePng(image[0])
fs.writeFileSync("output.png", png);
```
|
Declan/NewYorkTimes_model_v4
|
[] | null |
{
"architectures": null,
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},
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}
| 0 | null |
---
language:
- fr
---
This is a 4 bit quantized LoRA merge of [Vigogne-instruct-13b](https://huggingface.co/bofenghuang/vigogne-instruct-13b). <br />
*Il s'agit d'une fusion du LoRA de [Vigogne-instruct-13b](https://huggingface.co/bofenghuang/vigogne-instruct-13b) quantifié à 4 bits.*
Vigogne-instruct-13b is a LLaMA-13B model fine-tuned to follow the 🇫🇷 French instructions. <br />
*Vigogne-instruct-13b est un modèle LLaMA-13B affiné pour suivre les instructions 🇫🇷 françaises.*
For more information, please visit the Github repo: <br />
*Pour plus d'informations, veuillez visiter ce repo Github :* <br />
https://github.com/bofenghuang/vigogne
**Requires at least 10GB of vram.** <br />
*Nécessite au moins 10 Go de vram.*
-----------------------------------------------------------------------------------------------
This as been merged using [export_hf_checkpoint.py](https://github.com/tloen/alpaca-lora) <br />
*Fusionné en utilisant [export_hf_checkpoint.py](https://github.com/tloen/alpaca-lora)*
Quantized with [GPTQ-for-LLaMA](https://github.com/oobabooga/GPTQ-for-LLaMa)'s cuda branch: <br />
*Quantifiée avec la branche cuda de [GPTQ-for-LLaMA](https://github.com/oobabooga/GPTQ-for-LLaMa):* <br />
```
CUDA_VISIBLE_DEVICES=0 python llama.py "./hf_ckpt/" c4 --wbits 4 --groupsize 128 --save_safetensors vigogne-13b-4bit-128g.safetensor
```
**USAGE:** <br />
**UTILISATION:** <br />
In the folder characters/instruction-following, create a file named Vigogne.yaml that contains: <br />
*Dans le dossier characters/instruction-following, créer un fichier Vigogne.yaml qui contient:*
```
name: "### Réponse:"
your_name: "### Instruction:"
context: "Ci-dessous se trouve une instruction qui décrit une tâche. Écrivez une réponse qui complète correctement la demande.\n\n"
turn_template: "<|user|>\n<|user-message|>\n\n<|bot|>\n<|bot-message|>\n\n"
```
Start text-generation-webui with --chat and switch to instruct mode. Select Vigogne as Instruction template. <br />
*Démarrez text-generation-webui avec --chat et passez en mode instruct. Sélectionnez Vigogne dans Instruction template.* <br />
Here's my settings.json (a max_new_tokens value set to 200 might be more reasonable): <br />
*Voici mon settings.json (une valeur max_new_tokens de 200 est peut être plus raisonnable):*
```
{
"max_new_tokens": 750,
"max_new_tokens_min": 1,
"max_new_tokens_max": 2000,
"seed": -1,
"character": "None",
"name1": "### Instruction:",
"name2": "### Réponse:",
"context": "Ci-dessous se trouve une instruction qui décrit une tâche. Écrivez une réponse qui complète correctement la demande.\n\n",
"greeting": "",
"turn_template": "<|user|>\n<|user-message|>\n\n<|bot|>\n<|bot-message|>\n\n",
"custom_stopping_strings": "",
"stop_at_newline": false,
"add_bos_token": true,
"ban_eos_token": false,
"skip_special_tokens": true,
"truncation_length": 2048,
"truncation_length_min": 0,
"truncation_length_max": 8192,
"mode": "instruct",
"instruction_template": "Vigogne",
"chat_prompt_size": 2048,
"chat_prompt_size_min": 0,
"chat_prompt_size_max": 2048,
"chat_generation_attempts": 1,
"chat_generation_attempts_min": 1,
"chat_generation_attempts_max": 5,
"default_extensions": [],
"chat_default_extensions": [
""
],
"presets": {
"default": "LLaMA-Precise",
".*(alpaca|llama|llava)": "LLaMA-Precise",
".*pygmalion": "NovelAI-Storywriter",
".*RWKV": "Naive"
},
"prompts": {
"default": "Vigogne",
".*oasst": "Open Assistant",
".*alpaca": "Alpaca"
},
"lora_prompts": {
"default": "Vigogne",
".*(alpaca-lora-7b|alpaca-lora-13b|alpaca-lora-30b)": "Alpaca"
}
}
```
Here's my starting script: <br />
*Voici mon script de démarrage:*
```
#!/bin/bash
source ~/miniconda3/etc/profile.d/conda.sh
conda activate textgen
python server.py --model cmh_vigogne-13b-4bit-128g --model_type llama --wbits 4 --groupsize 128 --no-stream --sdp-attention --xformers --quant_attn --warmup_autotune --fused_mlp --load-in-8bit --chat --listen --auto-launch
```
|
Declan/NewYorkTimes_model_v6
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"length_penalty": null,
"max_length": null,
"min_length": null,
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},
"text-generation": {
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},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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}
}
}
| 5 | null |
---
license: creativeml-openrail-m
base_model: stabilityai/stable-diffusion-2-1-base
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- jax-diffusers-event
inference: true
datasets:
- ChristophSchuhmann/improved_aesthetics_6plus
---
# Stable Diffusion Nano 2.1
Stable Diffusion Nano was built during the [JAX/Diffusers community sprint 🧨](https://github.com/huggingface/community-events/tree/main/jax-controlnet-sprint#jaxdiffusers-community-sprint-).
Based on stable diffusion and fine-tuned on 128x128 images, Stable Diffusion Nano
allows for fast prototyping of diffusion models, enabling quick experimentation
with easily available hardware.
It performs reasonably well on several tasks, but it struggles with small details
such as faces.
prompt: A watercolor painting of an otter
prompt: Marvel MCU deadpool, red mask, red shirt, red gloves, black shoulders,
black elbow pads, black legs, gold buckle, black belt, black mask, white eyes,
black boots, fuji low light color 35mm film, downtown Osaka alley at night out
of focus in background, neon lights
## Training details
All parameters were initialized from the [stabilityai/stable-diffusion-2-1-base](https://huggingface.co/stabilityai/stable-diffusion-2-1-base)
model. The unet was fine tuned as follows:
U-net fine-tuning:
- 200,000 steps, learning rate = 1e-5, batch size = 992 (248 per TPU).
- 100,000 steps, SNR gamma = 5.0, learning rate = 1e-5, batch size = 992 (248 per TPU).
- Trained on [LAION Improved Aesthetics 6plus](https://huggingface.co/datasets/ChristophSchuhmann/improved_aesthetics_6plus).
## License
This model is open access and available to all, with a CreativeML OpenRAIL-M license
further specifying rights and usage. The CreativeML OpenRAIL License specifies:
- You can't use the model to deliberately produce nor share illegal or harmful outputs or content.
- The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license.
- You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully) Please read the full license here.
|
Declan/NewYorkTimes_model_v8
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
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},
"text-generation": {
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"max_length": null
},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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}
}
}
| 3 | null |
---
license: other
language:
- en
thumbnail:
tags:
- text generation
- conversational
inference: false
---
# Pygmalion 7B (bfloat16 version)
Converted from the XORed weights from [PygmalionAI](https://huggingface.co/PygmalionAI/pygmalion-7b) (i.e. ready for use).
|
Declan/Politico_model_v2
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
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},
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"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 5 | null |
---
license: afl-3.0
pipeline_tag: text-classification
tags:
- code
---
|
Declan/Politico_model_v8
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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}
}
}
| 7 | null |
---
license: creativeml-openrail-m
base_model: stabilityai/stable-diffusion-2-1-base
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA text2image fine-tuning - mikephillips/slant-axial-lora-2-1
These are LoRA adaption weights for stabilityai/stable-diffusion-2-1-base. The weights were fine-tuned on the None dataset. You can find some example images in the following.
|
Declan/Reuters_model_v3
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 7 | null |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 30270 with parameters:
```
{'batch_size': 4}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 50,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 3027,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
Declan/Reuters_model_v5
|
[
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 3 | null |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 30270 with parameters:
```
{'batch_size': 4}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 50,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 3027,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
DeepChem/ChemBERTa-5M-MLM
|
[
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
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},
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},
"translation_en_to_fr": {
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}
}
}
| 29 | null |
<div align="center">
<h1>Retrieval-based-Voice-Conversion-WebUI</h1>
一个基于VITS的简单易用的语音转换（变声器）框架<br><br>
[](https://github.com/liujing04/Retrieval-based-Voice-Conversion-WebUI)
<img src="https://counter.seku.su/cmoe?name=rvc&theme=r34" /><br>
[](https://colab.research.google.com/github/liujing04/Retrieval-based-Voice-Conversion-WebUI/blob/main/Retrieval_based_Voice_Conversion_WebUI.ipynb)
[](https://github.com/liujing04/Retrieval-based-Voice-Conversion-WebUI/blob/main/%E4%BD%BF%E7%94%A8%E9%9C%80%E9%81%B5%E5%AE%88%E7%9A%84%E5%8D%8F%E8%AE%AE-LICENSE.txt)
[](https://huggingface.co/lj1995/VoiceConversionWebUI/tree/main/)
[](https://discord.gg/HcsmBBGyVk)
</div>
------
[**更新日志**](https://github.com/liujing04/Retrieval-based-Voice-Conversion-WebUI/blob/main/Changelog_CN.md)
[**English**](./docs/README.en.md) | [**中文简体**](./README.md) | [**日本語**](./docs/README.ja.md) | [**한국어**](./docs/README.ko.md)
> 点此查看我们的[演示视频](https://www.bilibili.com/video/BV1pm4y1z7Gm/) !
> 使用了RVC的实时语音转换: [w-okada/voice-changer](https://github.com/w-okada/voice-changer)
> 底模使用接近50小时的开源高质量VCTK训练集训练，无版权方面的顾虑，请大家放心使用
> 后续会陆续加入高质量有授权歌声训练集训练底模
## 简介
本仓库具有以下特点
+ 使用top1检索替换输入源特征为训练集特征来杜绝音色泄漏
+ 即便在相对较差的显卡上也能快速训练
+ 使用少量数据进行训练也能得到较好结果(推荐至少收集10分钟低底噪语音数据)
+ 可以通过模型融合来改变音色(借助ckpt处理选项卡中的ckpt-merge)
+ 简单易用的网页界面
+ 可调用UVR5模型来快速分离人声和伴奏
## 环境配置
推荐使用poetry配置环境。
以下指令需在Python版本大于3.8的环境中执行:
```bash
# 安装Pytorch及其核心依赖，若已安装则跳过
# 参考自: https://pytorch.org/get-started/locally/
pip install torch torchvision torchaudio
#如果是win系统+Nvidia Ampere架构(RTX30xx)，根据 #21 的经验，需要指定pytorch对应的cuda版本
#pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu117
# 安装 Poetry 依赖管理工具, 若已安装则跳过
# 参考自: https://python-poetry.org/docs/#installation
curl -sSL https://install.python-poetry.org | python3 -
# 通过poetry安装依赖
poetry install
```
你也可以通过pip来安装依赖：
**注意**: `MacOS`下`faiss 1.7.2`版本会导致抛出段错误，在手动安装时请使用命令`pip install faiss-cpu==1.7.0`指定使用`1.7.0`版本
```bash
pip install -r requirements.txt
```
## 其他预模型准备
RVC需要其他一些预模型来推理和训练。
你可以从我们的[Hugging Face space](https://huggingface.co/lj1995/VoiceConversionWebUI/tree/main/)下载到这些模型。
以下是一份清单，包括了所有RVC所需的预模型和其他文件的名称:
```bash
hubert_base.pt
./pretrained
./uvr5_weights
#如果你正在使用Windows，则你可能需要这个文件，若ffmpeg和ffprobe已安装则跳过; ubuntu/debian 用户可以通过apt install ffmpeg来安装这2个库
./ffmpeg
./ffprobe
```
之后使用以下指令来启动WebUI:
```bash
python infer-web.py
```
如果你正在使用Windows，你可以直接下载并解压`RVC-beta.7z`，运行`go-web.bat`以启动WebUI。
仓库内还有一份`小白简易教程.doc`以供参考。
## 参考项目
+ [ContentVec](https://github.com/auspicious3000/contentvec/)
+ [VITS](https://github.com/jaywalnut310/vits)
+ [HIFIGAN](https://github.com/jik876/hifi-gan)
+ [Gradio](https://github.com/gradio-app/gradio)
+ [FFmpeg](https://github.com/FFmpeg/FFmpeg)
+ [Ultimate Vocal Remover](https://github.com/Anjok07/ultimatevocalremovergui)
+ [audio-slicer](https://github.com/openvpi/audio-slicer)
## 感谢所有贡献者作出的努力
<a href="https://github.com/liujing04/Retrieval-based-Voice-Conversion-WebUI/graphs/contributors" target="_blank">
<img src="https://contrib.rocks/image?repo=liujing04/Retrieval-based-Voice-Conversion-WebUI" />
</a>
|
DeepESP/gpt2-spanish
|
[
"pytorch",
"tf",
"jax",
"gpt2",
"text-generation",
"es",
"dataset:ebooks",
"transformers",
"GPT-2",
"Spanish",
"ebooks",
"nlg",
"license:mit",
"has_space"
] |
text-generation
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": null
},
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"prefix": null
},
"text-generation": {
"do_sample": true,
"max_length": 50
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
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"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 1,463 | 2023-05-01T02:42:28Z |
---
title: chinese-alpaca-plus-7b
emoji: 📚
colorFrom: gray
colorTo: red
language:
- zh
tags:
- chatglm
- pytorch
- zh
- Text2Text-Generation
license: "other"
widget:
- text: "为什么天空是蓝色的？"
---
# Chinese Alpaca Plus 7B Model
**发布中文LLaMA, Alpaca Plus版（7B）模型**
推出中文LLaMA, Alpaca Plus版（7B），相比基础版本的改进点如下：
- 进一步扩充了训练数据，其中LLaMA扩充至120G文本（通用领域），Alpaca扩充至4M指令数据（重点增加了STEM相关数据）
- Alpaca训练时采用了更大的rank，相比原版具有更低的验证集损失
- 评测结果显示，Alpaca-Plus-7B相比基础版Alpaca-7B效果更优，部分任务接近或超过13B版本
- 这一轮比拼：7B获得65.3分，13B获得70.9分，Plus-7B效果75.3分，具体评测结果请参考[效果评测](https://github.com/ymcui/Chinese-LLaMA-Alpaca/blob/main/examples/README.md)
本模型是`原生LLaMA-7B`合并`中文LLaMA LoRA`和`中文Alpaca LoRA`后的模型权重`chinese-alpaca-plus-7b-hf`，并转化为HuggingFace版本权重（.bin文件），可以直接使用或者继续训练。
13b-hf权重链接：https://huggingface.co/shibing624/chinese-alpaca-plus-13b-hf
test case:
|input_text|predict|
|:-- |:--- |
|为什么天空是蓝色的？|天空是蓝色的，是因为大气层中的气体分子会散射太阳光中的蓝色光，使得我们看到的天空是蓝色的。|
## release model weight
- chinese-llama-plus-7b 模型权重链接：https://huggingface.co/minlik/chinese-llama-plus-7b-merged
- chinese-alpaca-plus-7b 模型权重链接：https://huggingface.co/shibing624/chinese-alpaca-plus-7b-hf
- chinese-llama-plus-13b 模型权重链接：https://huggingface.co/shibing624/chinese-llama-plus-13b-hf
- chinese-aplaca-plus-13b 模型权重链接：https://huggingface.co/shibing624/chinese-alpaca-plus-13b-hf
## Usage
本项目开源在textgen项目：[textgen](https://github.com/shibing624/textgen)，可支持llama模型，通过如下命令调用：
Install package:
```shell
pip install -U textgen
```
```python
from textgen import LlamaModel
model = LlamaModel("llama", "shibing624/chinese-alpaca-plus-7b-hf")
r = model.predict(["用一句话描述地球为什么是独一无二的。"])
print(r) # ['地球是独一无二的，因为它拥有独特的大气层、水循环、生物多样性以及其他自然资源，这些都使它成为一个独特的生命支持系统。']
```
## Usage (HuggingFace Transformers)
Without [textgen](https://github.com/shibing624/textgen), you can use the model like this:
First, you pass your input through the transformer model, then you get the generated sentence.
Install package:
```
pip install sentencepiece
pip install transformers>=4.28.0
```
```python
import torch
import transformers
from transformers import LlamaTokenizer, LlamaForCausalLM
def generate_prompt(text):
return f"""Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{text}
### Response:"""
tokenizer = LlamaTokenizer.from_pretrained('shibing624/chinese-alpaca-plus-7b-hf')
model = LlamaForCausalLM.from_pretrained('shibing624/chinese-alpaca-plus-7b-hf').half().cuda()
model.eval()
text = '为什么天空是蓝色的？'
prompt = generate_prompt(text)
input_ids = tokenizer.encode(prompt, return_tensors='pt').to('cuda')
with torch.no_grad():
output_ids = model.generate(
input_ids=input_ids,
max_new_tokens=128,
temperature=1,
top_k=40,
top_p=0.9,
repetition_penalty=1.15
).cuda()
output = tokenizer.decode(output_ids[0], skip_special_tokens=True)
print(output.replace(text, '').strip())
```
output:
```shell
为什么天空是蓝色的？
天空是蓝色的，是因为大气层中的气体分子会散射太阳光中的蓝色光，使得我们看到的天空是蓝色的。
```
## 模型来源
release合并后的模型权重，一步到位直接使用，省电、减少碳排放。
基于 [多LoRA权重合并（适用于Chinese-Alpaca-Plus ）](https://github.com/ymcui/Chinese-LLaMA-Alpaca/wiki/%E6%89%8B%E5%8A%A8%E6%A8%A1%E5%9E%8B%E5%90%88%E5%B9%B6%E4%B8%8E%E8%BD%AC%E6%8D%A2#%E5%A4%9Alora%E6%9D%83%E9%87%8D%E5%90%88%E5%B9%B6%E9%80%82%E7%94%A8%E4%BA%8Echinese-alpaca-plus-)方法手动合并而成，具体是使用 [decapoda-research/llama-7b-hf](https://huggingface.co/decapoda-research/llama-7b-hf)
底座模型 合并 Chinese-LLaMA-Plus-LoRA和Chinese-Alpaca-Plus-LoRA 两个LoRA权重 得到，并转化为HuggingFace版本权重（.bin文件）。
HuggingFace版本权重（.bin文件）可用于：
- 使用Transformers进行训练和推理
- 使用text-generation-webui搭建界面
PyTorch版本权重（.pth文件）可用于：
- 使用llama.cpp工具进行量化和部署
PyTorch版本权重（.pth文件）链接，8-bit量化版的Alpaca-Plus-7B：[Billsfriend/chinese-Alpaca-7b-plus-ggml-q8_0](https://huggingface.co/Billsfriend/chinese-Alpaca-7b-plus-ggml-q8_0/tree/main)
模型文件组成：
```
chinese-alpaca-plus-7b-hf
config.json
generation_config.json
pytorch_model-00001-of-00002.bin
pytorch_model-00002-of-00002.bin
pytorch_model.bin.index.json
special_tokens_map.json
tokenizer.json
tokenizer.model
tokenizer_config.json
```
硬件要求：14G显存
### 微调数据集
我整理部分公开微调数据集：
1. 50万条中文ChatGPT指令Belle数据集：[BelleGroup/train_0.5M_CN](https://huggingface.co/datasets/BelleGroup/train_0.5M_CN)
2. 100万条中文ChatGPT指令Belle数据集：[BelleGroup/train_1M_CN](https://huggingface.co/datasets/BelleGroup/train_1M_CN)
3. 5万条英文ChatGPT指令Alpaca数据集：[50k English Stanford Alpaca dataset](https://github.com/tatsu-lab/stanford_alpaca#data-release)
4. 5万条中文GPT4指令Alpaca数据集：[shibing624/alpaca-zh](https://huggingface.co/datasets/shibing624/alpaca-zh)
5. 69万条中文指令Guanaco数据集(Belle50万条+Guanaco19万条)：[Chinese-Vicuna/guanaco_belle_merge_v1.0](https://huggingface.co/datasets/Chinese-Vicuna/guanaco_belle_merge_v1.0)
如果需要训练LLaMA模型，请参考[https://github.com/shibing624/textgen](https://github.com/shibing624/textgen)
## Citation
```latex
@software{textgen,
author = {Xu Ming},
title = {textgen: Implementation of language model finetune},
year = {2023},
url = {https://github.com/shibing624/textgen},
}
```
## Reference
- https://github.com/ymcui/Chinese-LLaMA-Alpaca
|
DeepPavlov/bert-base-multilingual-cased-sentence
|
[
"pytorch",
"jax",
"bert",
"feature-extraction",
"multilingual",
"arxiv:1704.05426",
"arxiv:1809.05053",
"arxiv:1908.10084",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 140 | null |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- controlnet
- jax-diffusers-event
inference: true
---
# controlnet- tsungtao/controlnet-mlsd-202305011046
These are controlnet weights trained on runwayml/stable-diffusion-v1-5 with new type of conditioning. You can find some example images in the following.
prompt: a living room with a dining table and chairs
prompt: a living room with couches and a tv
training datasets: https://huggingface.co/datasets/tsungtao/diffusers-testing
SD model base on: runwayml/stable-diffusion-v1-5
----------------------------------------------------------------------------------
for the training data, you can find it has 2 colums, raw training data, conditioning training data and its prompts.
raw training data is crawling from the internet for the living room with special training purpose. and they were resized with the local tool, it is the standerlization
process before training.
conditioning data was creating base on raw training data, we deployed the local Stable diffustion and Controllnet plugin on our local Dev Env for this purpose, to setup
the target conditioning data. after this, the conditioning data also be resized with the standardliaztion procedure.
for the prompts, we add this by the manual way, since the small testing data sets. anyway it also can be done by some favour tool on the internet.
above all, we have a upload scripte to combine all the raw data/conditioning data/prompts into a dataset and then auto upload they to Huggingface.
from the conditioning data, you can see which is the framework of the raw data. so training base on this, it will get a mode to extract the line frame work of the input.
and at the same time, raw data are all with some special style. so base on this model, you will get your raw input living room data line framework and then change the input
living room to a special style living room.
from the functional point of view, it seems like "MLSD" model, but it works better on special style living room data.
|
DeepPavlov/rubert-base-cased
|
[
"pytorch",
"jax",
"bert",
"feature-extraction",
"ru",
"arxiv:1905.07213",
"transformers",
"has_space"
] |
feature-extraction
|
{
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
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"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 148,127 | null |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="Felix555/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
DeepPavlov/xlm-roberta-large-en-ru-mnli
|
[
"pytorch",
"xlm-roberta",
"text-classification",
"en",
"ru",
"dataset:glue",
"dataset:mnli",
"transformers",
"xlm-roberta-large",
"xlm-roberta-large-en-ru",
"xlm-roberta-large-en-ru-mnli",
"has_space"
] |
text-classification
|
{
"architectures": [
"XLMRobertaForSequenceClassification"
],
"model_type": "xlm-roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 227 | null |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.50 +/- 2.74
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="Felix555/Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
DeepPavlov/xlm-roberta-large-en-ru
|
[
"pytorch",
"xlm-roberta",
"feature-extraction",
"en",
"ru",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"XLMRobertaModel"
],
"model_type": "xlm-roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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}
| 190 | null |
---
license: mit
tags:
- pytorch
- diffusers
- unconditional-image-generation
- diffusion-models-class
---
# Yet another Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class)
This model is a diffusion model for unconditional image generation of cute 🦋.
## Usage
```python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('jjholt/sd-class-butterflies-32')
image = pipeline().images[0]
image
```
|
Deniskin/essays_small_2000i
|
[] | null |
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}
| 0 | null |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: toki-pona-gpt2-alpaca-best
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# toki-pona-gpt2-alpaca-best
This model is a fine-tuned version of [vicgalle/gpt2-alpaca-gpt4](https://huggingface.co/vicgalle/gpt2-alpaca-gpt4) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7089
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 7
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:------:|:---------------:|
| 1.0386 | 1.0 | 22442 | 0.9873 |
| 0.9368 | 2.0 | 44884 | 0.8761 |
| 0.8786 | 3.0 | 67326 | 0.8070 |
| 0.8313 | 4.0 | 89768 | 0.7617 |
| 0.7885 | 5.0 | 112210 | 0.7323 |
| 0.7734 | 6.0 | 134652 | 0.7150 |
| 0.7586 | 7.0 | 157094 | 0.7089 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
DeskDown/MarianMixFT_en-fil
|
[
"pytorch",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
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"MarianMTModel"
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}
| 3 | null |
---
tags:
- autotrain
- summarization
language:
- unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- Xmm/autotrain-data-entities
co2_eq_emissions:
emissions: 0.019401340864734905
---
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 54336127310
- CO2 Emissions (in grams): 0.0194
## Validation Metrics
- Loss: 0.854
- Rouge1: 48.567
- Rouge2: 34.256
- RougeL: 39.748
- RougeLsum: 41.884
- Gen Len: 61.949
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/Xmm/autotrain-entities-54336127310
```
|
DeskDown/MarianMixFT_en-ja
|
[
"pytorch",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"MarianMTModel"
],
"model_type": "marian",
"task_specific_params": {
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"max_length": null
},
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},
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},
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},
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}
| 9 | 2023-05-01T04:02:42Z |
---
tags:
- generated_from_keras_callback
model-index:
- name: xinyixiuxiu/albert-xlarge-v2-SST2-incremental_pre_training
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# xinyixiuxiu/albert-xlarge-v2-SST2-incremental_pre_training
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1059
- Train Accuracy: 0.9630
- Validation Loss: 0.1832
- Validation Accuracy: 0.9381
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': 3e-06, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.2528 | 0.8917 | 0.2056 | 0.9323 | 0 |
| 0.1384 | 0.9503 | 0.1707 | 0.9461 | 1 |
| 0.1059 | 0.9630 | 0.1832 | 0.9381 | 2 |
### Framework versions
- Transformers 4.28.1
- TensorFlow 2.7.0
- Datasets 2.10.1
- Tokenizers 0.12.1
|
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