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
|
|---|---|---|---|---|---|---|
DeskDown/MarianMixFT_en-vi
|
[
"pytorch",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
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| 5 | null |
---
tags:
- generated_from_trainer
model-index:
- name: results
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. -->
# results
This model is a fine-tuned version of [Rostlab/prot_bert_bfd](https://huggingface.co/Rostlab/prot_bert_bfd) 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: 16
- eval_batch_size: 8
- seed: 0
- gradient_accumulation_steps: 128
- total_train_batch_size: 2048
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Tokenizers 0.13.3
|
DeskDown/MarianMix_en-ja-10
|
[
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
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| 1 | 2023-05-01T04:10:07Z |
---
datasets:
- gozfarb/ShareGPT_Vicuna_unfiltered
---
# Converter
https://github.com/practicaldreamer/fastchat-conversation-converter
# Trainer
https://github.com/oobabooga/text-generation-webui
LoRA is totally WIP consider it pre-alpha
|
DeskDown/MarianMix_en-zh-10
|
[
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
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| 3 | null |
---
license: other
---
Mirror of LLaMA 7B and 13B's quantization from Sparsebit.
The author assert NO RIGHT WHATSOEVER. All rights belong to their respective owners. I do not own any of this content.
|
Despin89/test
|
[] | null |
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| 0 | 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/1559522455382855681/SBC0WX5Z_400x400.png')">
</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">Nansen Portfolio</div>
<div style="text-align: center; font-size: 14px;">@nansenportfolio</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 Nansen Portfolio.
| Data | Nansen Portfolio |
| --- | --- |
| Tweets downloaded | 2117 |
| Retweets | 98 |
| Short tweets | 226 |
| Tweets kept | 1793 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/lp1s6lkg/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 @nansenportfolio's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/uc47wqfz) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/uc47wqfz/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/nansenportfolio')
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)
|
Dev-DGT/food-dbert-multiling
|
[
"pytorch",
"distilbert",
"token-classification",
"transformers",
"autotrain_compatible"
] |
token-classification
|
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| 17 | 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: 7.52 +/- 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="cyrodw/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"])
```
|
Devid/DialoGPT-small-Miku
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
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"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
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| 10 | null |
---
tags:
- generated_from_trainer
model-index:
- name: outputs
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. -->
# outputs
This model is a fine-tuned version of [jl2010/outputs](https://huggingface.co/jl2010/outputs) 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: 0.003
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Author | Date | Image | Other | Paragraph | Title | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:----------------------------------------------------------:|:----------------------------------------------------------:|:----------------------------------------------------------:|:------------------------------------------------------------:|:-----------------------------------------------------------------------------------------:|:----------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| No log | 1.0 | 125 | nan | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 21} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 88} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 96} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1839} | {'precision': 0.2006172839506173, 'recall': 1.0, 'f1': 0.3341902313624679, 'number': 520} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 28} | 0.2006 | 0.2006 | 0.2006 | 0.2006 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.1+cu117
- Datasets 2.8.0
- Tokenizers 0.13.2
|
DiegoAlysson/opus-mt-en-ro-finetuned-en-to-ro
|
[
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"dataset:wmt16",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] |
text2text-generation
|
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| 1 | 2023-05-01T04:37:59Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-3000-samples
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
split: test
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.8533333333333334
- name: F1
type: f1
value: 0.8552631578947368
---
<!-- 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. -->
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3081
- Accuracy: 0.8533
- F1: 0.8553
## 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: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Dimedrolza/DialoGPT-small-cyberpunk
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
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"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
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}
| 9 | 2023-05-01T04:54:16Z |
---
license: creativeml-openrail-m
base_model: CompVis/stable-diffusion-v1-4
instance_prompt: a photo of sks dog
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA DreamBooth - sayakpaul/dreambooth-text-encoder-test
These are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks dog using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
LoRA for the text encoder was enabled: True.
|
Dmitry12/sber
|
[] | null |
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| 0 | null |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Cartpole-v1-r2
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
|
DongHyoungLee/distilbert-base-uncased-finetuned-cola
|
[
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] |
text-classification
|
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| 27 | null |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: jikkyjohn/qa_model_distill_bert_squad
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. -->
# jikkyjohn/qa_model_distill_bert_squad
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:
- Train Loss: 3.4480
- Validation Loss: 2.2621
- Epoch: 0
## 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', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 500, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 3.4480 | 2.2621 | 0 |
### Framework versions
- Transformers 4.28.1
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Waynehillsdev/Wayne_NLP_mT5
|
[
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] |
text2text-generation
|
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"MT5ForConditionalGeneration"
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| 11 | 2023-05-01T07:29:06Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: opus-mt-finetuned-id-to-su
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. -->
# opus-mt-finetuned-id-to-su
This model is a fine-tuned version of [hermanshid/opus-mt-finetuned-id-to-su](https://huggingface.co/hermanshid/opus-mt-finetuned-id-to-su) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2138
- Bleu: 30.2906
- Gen Len: 61.8136
## 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
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|
| 1.6062 | 1.0 | 2500 | 1.4921 | 25.6912 | 62.7002 |
| 1.4351 | 2.0 | 5000 | 1.3702 | 27.6125 | 62.2532 |
| 1.3364 | 3.0 | 7500 | 1.3116 | 28.515 | 62.4072 |
| 1.2696 | 4.0 | 10000 | 1.2821 | 29.279 | 61.8128 |
| 1.2245 | 5.0 | 12500 | 1.2532 | 29.6096 | 61.631 |
| 1.1812 | 6.0 | 15000 | 1.2373 | 29.8749 | 61.9136 |
| 1.1589 | 7.0 | 17500 | 1.2309 | 29.8342 | 62.038 |
| 1.1268 | 8.0 | 20000 | 1.2186 | 30.0483 | 61.9544 |
| 1.1055 | 9.0 | 22500 | 1.2158 | 30.3904 | 61.5692 |
| 1.1035 | 10.0 | 25000 | 1.2138 | 30.2906 | 61.8136 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Doogie/Waynehills-KE-T5-doogie
|
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| 0 | null |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: EinsZwo/en-to-ar_coref_words
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. -->
# EinsZwo/en-to-ar_coref_words
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ar](https://huggingface.co/Helsinki-NLP/opus-mt-en-ar) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.7615
- Validation Loss: 2.0233
- 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': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 19626, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 2.1489 | 2.0516 | 0 |
| 1.9031 | 2.0309 | 1 |
| 1.7615 | 2.0233 | 2 |
### Framework versions
- Transformers 4.28.1
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Doohae/q_encoder
|
[
"pytorch"
] | null |
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| 3 | null |
There are several ways to convert MSG files to the EML file format, but the DataVare MSG to EML conversion application is an effective choice that can swiftly convert single MSG file at once. Numerous email clients are supported, including Thunderbird, Apple mail, eM Client, Incredimail, etc. DataVare MSG to EML Converter Software allows you to convert an endless amount of data error-free and uninterrupted while providing accurate results for your MSG to EML files. All MS Outlook versions, including 2019, 2016, 2013, 2010, 2007, 2003, and 2000, can access this. Users are free to select the destination path according to their needs, allowing them to quickly and efficiently export MSG to a variety of formats as necessary without having to scout around the system. On Windows 10 and earlier, MSG to EML converters easily run it. Simply download the trial version to explore all the features and capabilities. Technical assistance is not required for this utility to function. The best method for converting MSG to EML files is this one. Purchase the licensed version at reasonable prices after trying the free demo. Even vast quantities of files can be managed with only one click thanks to this time-saving program.
Read more:- https://www.datavare.com/software/msg-to-eml-converter-expert.html
|
Doohae/roberta
|
[
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] |
question-answering
|
{
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"RobertaForQuestionAnswering"
],
"model_type": "roberta",
"task_specific_params": {
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| 3 | null |
---
language:
- mn
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bloom-NER-fr
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. -->
# bloom-NER-fr
This model is a fine-tuned version of [roberta-large-mnli](https://huggingface.co/roberta-large-mnli) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2930
- Precision: 0.5423
- Recall: 0.6361
- F1: 0.5854
- Accuracy: 0.9004
## 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: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.7569 | 1.0 | 47 | 0.4836 | 0.3709 | 0.3924 | 0.3813 | 0.8604 |
| 0.4348 | 2.0 | 94 | 0.3771 | 0.4395 | 0.5443 | 0.4863 | 0.8687 |
| 0.3607 | 3.0 | 141 | 0.3232 | 0.5115 | 0.6086 | 0.5559 | 0.8953 |
| 0.2913 | 4.0 | 188 | 0.2918 | 0.5527 | 0.6255 | 0.5868 | 0.8974 |
| 0.2602 | 5.0 | 235 | 0.2835 | 0.5485 | 0.6445 | 0.5926 | 0.9028 |
| 0.2332 | 6.0 | 282 | 0.2930 | 0.5423 | 0.6361 | 0.5854 | 0.9004 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
DoyyingFace/bert-asian-hate-tweets-asian-unclean-freeze-12
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
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| 29 | 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: 0.8653353814644136
---
<!-- 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: 0.1339
- F1: 0.8653
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2583 | 1.0 | 525 | 0.1596 | 0.8231 |
| 0.1262 | 2.0 | 1050 | 0.1395 | 0.8468 |
| 0.0824 | 3.0 | 1575 | 0.1339 | 0.8653 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
DoyyingFace/bert-asian-hate-tweets-asian-unclean-freeze-4
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
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| 44 | 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- ParityError/ControlNet-Shadows
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 hollow wooden box and ribbed green ball in the air with shadows on the ground, light direction west+32, light elevation 25
prompt: a hollow green cube and silver planet in the sky and metallic wedge with shadows on the ground, light direction east+34, light elevation 25
prompt: a tree house with a rope ladder, light direction nw, light elevation 25
|
DoyyingFace/bert-asian-hate-tweets-asian-unclean-freeze-8
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
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"BertForSequenceClassification"
],
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| 30 | 2023-05-01T05:51:10Z |
---
language:
- mn
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: roberta-base-ner-demo
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. -->
# roberta-base-ner-demo
This model is a fine-tuned version of [bayartsogt/mongolian-roberta-base](https://huggingface.co/bayartsogt/mongolian-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1263
- Precision: 0.9352
- Recall: 0.9416
- F1: 0.9384
- Accuracy: 0.9817
## 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: 32
- 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 | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.161 | 1.0 | 477 | 0.0722 | 0.9132 | 0.9248 | 0.9190 | 0.9786 |
| 0.052 | 2.0 | 954 | 0.0732 | 0.9211 | 0.9353 | 0.9282 | 0.9797 |
| 0.028 | 3.0 | 1431 | 0.0802 | 0.9280 | 0.9354 | 0.9317 | 0.9804 |
| 0.015 | 4.0 | 1908 | 0.0954 | 0.9190 | 0.9324 | 0.9257 | 0.9791 |
| 0.0101 | 5.0 | 2385 | 0.0978 | 0.9312 | 0.9385 | 0.9348 | 0.9809 |
| 0.0055 | 6.0 | 2862 | 0.1072 | 0.9315 | 0.9392 | 0.9353 | 0.9810 |
| 0.0035 | 7.0 | 3339 | 0.1165 | 0.9313 | 0.9392 | 0.9352 | 0.9807 |
| 0.0026 | 8.0 | 3816 | 0.1223 | 0.9338 | 0.9403 | 0.9371 | 0.9812 |
| 0.002 | 9.0 | 4293 | 0.1234 | 0.9341 | 0.9398 | 0.9369 | 0.9813 |
| 0.0009 | 10.0 | 4770 | 0.1263 | 0.9352 | 0.9416 | 0.9384 | 0.9817 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
DoyyingFace/bert-asian-hate-tweets-asian-unclean-slanted
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
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| 29 | null |
Use the WholeClear NSF to MSG Converter Software is a secure approach to convert NSF files into MSG file format. This program offers to converts NSF files of any size of the NSF file into MSG file format. Technical and non-technical users can easily to converts NSF file into MSG file format. The converted MSG file can be saved with this tool in a specific location on your own computer. This application entrely preserves all email properties, including To, CC, BCC, From, Subject, Date, and attachments. This utility is the best part of that allows to you preview facility, so that every users can easily to see all the database before the conversion. If you want to understand more about the conversion the program, you may try the best free demo version, which enables you to convert a few items from each folders. This utility is accessible with all the MS Outlook versions such as 2003, 2007, 2010, 2013, 2016, and 2019. With this utility, you can access all Windows operating system versions, including 7, 8, 8.1, 10, XP, and Vista.
Read More:- https://www.wholeclear.com/nsf/msg/
|
DoyyingFace/bert-asian-hate-tweets-asian-unclean-warmup-100
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
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"BertForSequenceClassification"
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| 28 | 2023-05-01T05:55:31Z |
---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: whatracket
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.4157303273677826
---
# whatracket
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### badminton racket
#### squash racket
#### table tennis racket
#### tennis racket
|
DoyyingFace/bert-asian-hate-tweets-asian-unclean-warmup-25
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
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"BertForSequenceClassification"
],
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}
| 30 | null |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- textual_inversion
inference: true
---
# Textual inversion text2image fine-tuning - anic87/textual_inversion_well
These are textual inversion adaption weights for runwayml/stable-diffusion-v1-5. You can find some example images in the following.
|
DoyyingFace/bert-asian-hate-tweets-asian-unclean-warmup-50
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
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}
}
| 28 | 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 playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="Fillemon/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"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
DoyyingFace/bert-asian-hate-tweets-asian-unclean-warmup-75
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
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"BertForSequenceClassification"
],
"model_type": "bert",
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},
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}
| 37 | 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: 7.52 +/- 2.77
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="Fillemon/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"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
DoyyingFace/bert-asian-hate-tweets-asian-unclean-with-clean-valid
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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}
| 33 | null |
---
license: apache-2.0
tags:
- summarization
- generated_from_trainer
datasets:
- wiki_lingua
metrics:
- rouge
model-index:
- name: wiki_lingua-es-8-3-5.6e-05-mt5-small-finetuned
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: wiki_lingua
type: wiki_lingua
config: es
split: test
args: es
metrics:
- name: Rouge1
type: rouge
value: 22.4103
---
<!-- 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. -->
# wiki_lingua-es-8-3-5.6e-05-mt5-small-finetuned
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the wiki_lingua dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0242
- Rouge1: 22.4103
- Rouge2: 9.2461
- Rougel: 19.4105
- Rougelsum: 21.758
# Baseline LEAD-64
- Rouge1: 25.16
- Rouge2: 7.28
- Rougel: 16.23
- Rougelsum: 16.23
## 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: 5.6e-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
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|
| 2.8922 | 1.0 | 9537 | 2.1216 | 21.4907 | 8.3405 | 18.2727 | 20.7713 |
| 2.4024 | 2.0 | 19074 | 2.0520 | 22.2765 | 9.1257 | 19.2788 | 21.608 |
| 2.3131 | 3.0 | 28611 | 2.0242 | 22.4103 | 9.2461 | 19.4105 | 21.758 |
### Framework versions
- Transformers 4.27.4
- Pytorch 1.13.0
- Datasets 2.1.0
- Tokenizers 0.13.2
|
DoyyingFace/bert-asian-hate-tweets-asonam-clean
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
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},
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}
}
| 27 | 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="ngyewkong/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"])
```
|
DoyyingFace/bert-asian-hate-tweets-asonam-unclean
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"BertForSequenceClassification"
],
"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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}
| 30 | null |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: unit2-Taxi3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.48 +/- 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="barry0121/unit2-Taxi3", 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"])
```
|
DoyyingFace/bert-asian-hate-tweets-concat-clean-with-unclean-valid
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"BertForSequenceClassification"
],
"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,
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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": {
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}
| 25 | 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: 7.48 +/- 2.81
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="ngyewkong/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"])
```
|
DoyyingFace/bert-asian-hate-tweets-concat-clean
|
[
"pytorch",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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}
| 25 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: multilingual_1_5
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. -->
# multilingual_1_5
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4579
- Accuracy: 0.43
- F1: 0.1480
## 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: 2
### Training results
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
albert-base-v2
|
[
"pytorch",
"tf",
"jax",
"rust",
"safetensors",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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}
| 4,785,283 | 2023-05-01T06:08:27Z |
---
license: bigscience-bloom-rail-1.0
datasets:
- OpenAssistant/oasst1
- RyokoAI/ShareGPT52K
- Dahoas/full-hh-rlhf
- liswei/rm-static-m2m100-zh
- fnlp/moss-002-sft-data
language:
- zh
- en
---
This is an attempt to replicate the RLHF pipeline
### Base Model
We used [bloomz-7b1-mt](https://huggingface.co/bigscience/bloomz-7b1-mt) because of its less-restricted license and multilingual ability.
### Supervised Fintune
For SFT we used a combination of multiple datasets including:
- [RyokoAI/ShareGPT52K](https://huggingface.co/datasets/RyokoAI/ShareGPT52K)
- [GPTeacher](https://github.com/teknium1/GPTeacher)
- [Alpaca-GPT4](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM) en & zh
- Filtered subset of machine-translated ShareGPT dataset into Chinese
### Reward Model
For RM we used the code of [reward-modeling](https://github.com/Dahoas/reward-modeling) repo and datasets from
- [oasst1](https://huggingface.co/datasets/OpenAssistant/oasst1)
- [Dahoas/full-hh-rlhf](https://huggingface.co/datasets/Dahoas/full-hh-rlhf)
- [liswei/rm-static-m2m100-zh](https://huggingface.co/datasets/liswei/rm-static-m2m100-zh)
### Reinforcement Learning
For RL we used the code of [trlx](https://github.com/CarperAI/trlx) with slight modification.
Instead of building value network upon the policy network with a single linear layer, we add another hydra head upon the reference network's frozen bottom layers as value network.
### Example
We used Vicuna v1.1 template for model training
```
from transformers import AutoModelForCausalLM, AutoTokenizer
checkpoint = "keyfan/bloomz-rlhf"
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForCausalLM.from_pretrained(checkpoint).cuda()
template = ("A chat between a curious human and an artificial intelligence assistant. "
"The assistant gives helpful, detailed, and polite answers to the human's questions. "
"USER: {}\nASSISTANT:")
question = template.format("Who was the president of the United States in 1955?")
inputs = tokenizer.encode(question, return_tensors="pt").cuda()
outputs = model.generate(inputs, do_sample=True, top_p=0.8, max_new_tokens=512)
print(tokenizer.decode(outputs[0]))
```
### Evalutions
Result on the Chinese [BELLE eval set](https://github.com/LianjiaTech/BELLE/tree/main/eval)
| others | rewrite | classification | generation | summarization | extract | open qa | brainstorming | closed qa | macro ave | macro ave w/o others |
| ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- |
| 0.619 | 0.873 | 0.706 | 0.934 | 0.755 | 0.619 | 0.527 | 0.908 | 0.615 | 0.728 | 0.742 |
* We found in GPT-4 evaluation the order in which the responses were presented has unneglectable affect on the final score even with the very-well designed Vicuna prompt. So we removed the score on the Vicuna eval set.
|
bert-base-chinese
|
[
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"zh",
"arxiv:1810.04805",
"transformers",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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}
}
| 3,377,486 | 2023-05-01T06:26:09Z |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: jikkyjohn/roberta-base-finetuned-squad
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. -->
# jikkyjohn/roberta-base-finetuned-squad
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.6641
- Train End Logits Accuracy: 0.8101
- Train Start Logits Accuracy: 0.7677
- Validation Loss: 0.8159
- Validation End Logits Accuracy: 0.7731
- Validation Start Logits Accuracy: 0.7410
- Epoch: 1
## 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', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 22142, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 1.0348 | 0.7257 | 0.6841 | 0.8335 | 0.7701 | 0.7375 | 0 |
| 0.6641 | 0.8101 | 0.7677 | 0.8159 | 0.7731 | 0.7410 | 1 |
### Framework versions
- Transformers 4.28.1
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
bert-large-cased-whole-word-masking
|
[
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"length_penalty": null,
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},
"text-generation": {
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},
"translation_en_to_de": {
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"max_length": null,
"num_beams": null,
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},
"translation_en_to_fr": {
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"translation_en_to_ro": {
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}
}
}
| 2,316 | 2023-05-01T06:37:59Z |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
instance_prompt: a photo of sks well-differentiated-adenocarcinoma
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- dreambooth
inference: true
---
# DreamBooth - anic87/well
This is a dreambooth model derived from runwayml/stable-diffusion-v1-5. The weights were trained on a photo of sks well-differentiated-adenocarcinoma using [DreamBooth](https://dreambooth.github.io/).
You can find some example images in the following.
DreamBooth for the text encoder was enabled: False.
|
bert-large-uncased-whole-word-masking
|
[
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
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": {
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},
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}
}
}
| 76,685 | 2023-05-01T06:47:13Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: sayakpcode/bert-finetuned-squad
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. -->
# sayakpcode/bert-finetuned-squad
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.5726
- 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': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 16635, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Epoch |
|:----------:|:-----:|
| 1.2725 | 0 |
| 0.7804 | 1 |
| 0.5726 | 2 |
### Framework versions
- Transformers 4.28.1
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
ctrl
|
[
"pytorch",
"tf",
"ctrl",
"en",
"arxiv:1909.05858",
"arxiv:1910.09700",
"transformers",
"license:bsd-3-clause",
"has_space"
] | null |
{
"architectures": null,
"model_type": "ctrl",
"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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}
}
| 17,007 | 2023-05-01T06:59:31Z |
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|
distilbert-base-cased-distilled-squad
|
[
"pytorch",
"tf",
"rust",
"safetensors",
"openvino",
"distilbert",
"question-answering",
"en",
"dataset:squad",
"arxiv:1910.01108",
"arxiv:1910.09700",
"transformers",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"has_space"
] |
question-answering
|
{
"architectures": [
"DistilBertForQuestionAnswering"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
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},
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}
}
}
| 257,745 | 2023-05-01T07:01:45Z |
---
license: other
---
画像は
1girl
Negative prompt: (bad quality:1.4),(low quality:1.4)
作り方
01
Anyの塗を強化します
325 2023.05.01 14.38.01 Anything-V3.0 + (Anything-V3.0 - v1-5-pruned) x alpha (0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.35,0.35,0.35,0.35,0.35,0.35 "0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.35,0.35,0.35,0.35,0.35,0.35
" 0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2 8712e20a5d 8712e20a5d e1441589a6 0 0.25 Add difference:A+(B-C)*alpha TRUE 1 ['safetensors', 'overwrite', 'fp16', 'save metadata', 'save model'] ['image'] normal
02
やりすぎた感あったので戻していきます
335 2023.05.01 14.53.34 01fp16 + (Anything-V3.0 - v1-5-pruned) x 0 b04662a8f6 8712e20a5d e1441589a6 0 0.25 Add difference:A+(B-C)*alpha FALSE 2 ['safetensors', 'overwrite', 'fp16', 'save metadata', 'save model'] ['image'] OUT04:transformer_blocks.0.attn2.to_q.weight:-1,OUT05:transformer_blocks.0.attn2.to_q.weight:-1,OUT03:transformer_blocks.0.attn2.to_q.weight:-1,IN07:transformer_blocks.0.attn2.to_q.weight:-1,IN08:transformer_blocks.0.attn2.to_q.weight:-1 normal
03
人体構造がおかしくなっていたので、深層を戻します
350 2023.05.01 15.20.04 02fp16 x 1 + v1-5-pruned x 0 8fd5caf2d7 e1441589a6 e1441589a6 0 0.25 Weight sum:A*(1-alpha)+B*alpha FALSE 4 ['safetensors', 'overwrite', 'fp16', 'save metadata', 'save model'] ['image'] OUT11:skip_connection:0.75,OUT10:skip_connection:0.75,OUT09:skip_connection:0.75,IN11 M00 OUT00 OUT01 OUT02:layers:0.75 normal
04
うまく戻らなかったのでもう一回戻します
351 2023.05.01 15.20.42 03fp16 x 1 + v1-5-pruned x 0 05d8ffcfcc e1441589a6 e1441589a6 0 0.25 Weight sum:A*(1-alpha)+B*alpha FALSE 4 ['safetensors', 'overwrite', 'fp16', 'save metadata', 'save model'] ['image'] OUT11:skip_connection:0.75,OUT10:skip_connection:0.75,OUT09:skip_connection:0.75,IN11 M00 OUT00 OUT01 OUT02:layers:0.75 normal
05
塗を01に近づけたかったので、cosineBで寄せます
352 2023.05.01 15.34.03 04fp16 x 0.5 + 01fp16 x 0.5 59d991b7f3 b04662a8f6 e1441589a6 0.5 0.25 Weight sum:A*(1-alpha)+B*alpha FALSE 5 ['safetensors', 'overwrite', 'fp16', 'save metadata', 'save model'] ['image'] cosineB
06
BASEで人体の安定化を図ってます。多分効果ないです
362 2023.05.01 15.44.09 05fp16 x (1-alpha) + v1-5-pruned x alpha (0.05,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0) 0.05,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2 5284dd1e4e e1441589a6 e1441589a6 0.5 0.25 Weight sum:A*(1-alpha)+B*alpha TRUE 6 ['safetensors', 'overwrite', 'fp16', 'save metadata', 'save model'] ['image'] normal
遠回りしまして完成です
別に01で完成でも、問題ないと思います。
ネガティブ系使うと安定すると思います。
|
distilbert-base-german-cased
|
[
"pytorch",
"safetensors",
"distilbert",
"fill-mask",
"de",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
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"DistilBertForMaskedLM"
],
"model_type": "distilbert",
"task_specific_params": {
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},
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}
| 43,667 | 2023-05-01T07:04:35Z |
---
tags:
- autotrain
- vision
- image-classification
datasets:
- nandodeomkar/autotrain-data-bone-fracture-detection
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
example_title: Palace
co2_eq_emissions:
emissions: 0.007494454669184296
---
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 54370127369
- CO2 Emissions (in grams): 0.0075
## Validation Metrics
- Loss: 0.261
- Accuracy: 0.923
- Precision: 0.800
- Recall: 1.000
- AUC: 0.972
- F1: 0.889
|
distilgpt2
|
[
"pytorch",
"tf",
"jax",
"tflite",
"rust",
"coreml",
"safetensors",
"gpt2",
"text-generation",
"en",
"dataset:openwebtext",
"arxiv:1910.01108",
"arxiv:2201.08542",
"arxiv:2203.12574",
"arxiv:1910.09700",
"arxiv:1503.02531",
"transformers",
"exbert",
"license:apache-2.0",
"model-index",
"co2_eq_emissions",
"has_space"
] |
text-generation
|
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| 1,611,668 | 2023-05-01T07:17:55Z |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
instance_prompt: a photo of sks poorly-differentiated-adenocarcinoma
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- dreambooth
inference: true
---
# DreamBooth - anic87/poor
This is a dreambooth model derived from runwayml/stable-diffusion-v1-5. The weights were trained on a photo of sks poorly-differentiated-adenocarcinoma using [DreamBooth](https://dreambooth.github.io/).
You can find some example images in the following.
DreamBooth for the text encoder was enabled: False.
|
AAli/distilgpt2-finetuned-wikitext2
|
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}
| 0 | 2023-05-01T09:45:22Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: classify_services_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. -->
# classify_services_model
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5475
- Accuracy: 0.7549
## 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: 32
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 71 | 0.6433 | 0.6640 |
| No log | 2.0 | 142 | 0.5475 | 0.7549 |
### Framework versions
- Transformers 4.27.4
- Pytorch 1.13.0
- Datasets 2.1.0
- Tokenizers 0.13.2
|
ASCCCCCCCC/distilbert-base-uncased-finetuned-clinc
|
[
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
] |
text-classification
|
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| 35 | 2023-05-01T11:47:15Z |
---
license: apache-2.0
tags:
- text_classification
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion_detection
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: 0.921
- name: F1
type: f1
value: 0.9210457518994596
---
<!-- 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_detection
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: 0.2211
- Accuracy: 0.921
- F1: 0.9210
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.7979 | 1.0 | 250 | 0.3147 | 0.906 | 0.9041 |
| 0.2464 | 2.0 | 500 | 0.2211 | 0.921 | 0.9210 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Aakansha/hs
|
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| 0 | 2023-05-01T12:13:21Z |
---
license: apache-2.0
tags:
- ctranslate2
---
# Fast-Inference with Ctranslate2
Speedup inference by 2x-8x using int8 inference in C++
quantized version of [declare-lab/flan-alpaca-base](https://huggingface.co/declare-lab/flan-alpaca-base)
```bash
pip install hf_hub_ctranslate2>=1.0.0 ctranslate2>=3.13.0
```
Checkpoint compatible to [ctranslate2](https://github.com/OpenNMT/CTranslate2) and [hf-hub-ctranslate2](https://github.com/michaelfeil/hf-hub-ctranslate2)
- `compute_type=int8_float16` for `device="cuda"`
- `compute_type=int8` for `device="cpu"`
```python
from hf_hub_ctranslate2 import TranslatorCT2fromHfHub, GeneratorCT2fromHfHub
model_name = "michaelfeil/ct2fast-flan-alpaca-base"
model = TranslatorCT2fromHfHub(
# load in int8 on CUDA
model_name_or_path=model_name,
device="cuda",
compute_type="int8_float16"
)
outputs = model.generate(
text=["How do you call a fast Flan-ingo?", "Translate to german: How are you doing?"],
min_decoding_length=24,
max_decoding_length=32,
max_input_length=512,
beam_size=5
)
print(outputs)
```
# Licence and other remarks:
This is just a quantized version. Licence conditions are intended to be idential to original huggingface repo.
|
AdapterHub/bert-base-uncased-pf-rotten_tomatoes
|
[
"bert",
"en",
"dataset:rotten_tomatoes",
"arxiv:2104.08247",
"adapter-transformers",
"text-classification",
"adapterhub:sentiment/rotten_tomatoes"
] |
text-classification
|
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| 2 | 2023-05-01T14:18:38Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bert-large-cased-sigir-support-refute-no-label-40-2nd-test-LR10-8-fast-19
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. -->
# bert-large-cased-sigir-support-refute-no-label-40-2nd-test-LR10-8-fast-19
This model is a fine-tuned version of [jojoUla/bert-large-cased-sigir-support-refute-no-label-40](https://huggingface.co/jojoUla/bert-large-cased-sigir-support-refute-no-label-40) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1514
## 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: 4e-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: 8.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 5.1321 | 1.0 | 1 | 1.7547 |
| 2.4462 | 2.0 | 2 | 8.9579 |
| 2.1549 | 3.0 | 3 | 4.2014 |
| 1.7913 | 4.0 | 4 | 0.0533 |
| 1.6807 | 5.0 | 5 | 0.7624 |
| 1.1819 | 6.0 | 6 | 0.4617 |
| 2.5666 | 7.0 | 7 | 3.3459 |
| 1.8443 | 8.0 | 8 | 1.1587 |
### Framework versions
- Transformers 4.29.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
AdapterHub/roberta-base-pf-ud_pos
|
[
"roberta",
"en",
"dataset:universal_dependencies",
"arxiv:2104.08247",
"adapter-transformers",
"token-classification",
"adapterhub:pos/ud_ewt"
] |
token-classification
|
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| 8 | null |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: chilenoGPT
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. -->
# chilenoGPT
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.3921
## 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
- lr_scheduler_warmup_steps: 30414
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 4.4985 | 1.0 | 3802 | 4.3106 |
| 4.1063 | 2.0 | 7604 | 3.9798 |
| 3.8797 | 3.0 | 11406 | 3.7886 |
| 3.7554 | 4.0 | 15208 | 3.6645 |
| 3.616 | 5.0 | 19010 | 3.5792 |
| 3.534 | 6.0 | 22812 | 3.5152 |
| 3.4631 | 7.0 | 26614 | 3.4632 |
| 3.3867 | 8.0 | 30416 | 3.4330 |
| 3.2781 | 9.0 | 34218 | 3.3975 |
| 3.2074 | 10.0 | 38020 | 3.3921 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Tokenizers 0.13.3
|
AdapterHub/roberta-base-pf-wic
|
[
"roberta",
"en",
"arxiv:2104.08247",
"adapter-transformers",
"text-classification",
"adapterhub:wordsence/wic"
] |
text-classification
|
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| 0 | null |
---
license: creativeml-openrail-m
tags:
- stablediffusionapi.com
- stable-diffusion-api
- text-to-image
- ultra-realistic
pinned: true
---
# LastOfUs_Buildings 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 "lastofusbuildings"
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/lastofusbuildings)
Credits: [View credits](https://civitai.com/?query=LastOfUs_Buildings)
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": "lastofusbuildings",
"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**
|
Adrianaforididk/Jinx
|
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| 0 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-large-xls-r-300m-sinhala
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. -->
# wav2vec2-large-xls-r-300m-sinhala
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 5.4599
- eval_wer: 1.0
- eval_runtime: 185.9221
- eval_samples_per_second: 6.729
- eval_steps_per_second: 0.844
- epoch: 2.53
- step: 100
## 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: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 1.18.3
- Tokenizers 0.13.3
|
AimB/mT5-en-kr-aihub-netflix
|
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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 playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="wzhao82/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"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
AimB/mT5-en-kr-natural
|
[
"pytorch",
"mt5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
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| 78 | null |
---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: BERThard
results: []
license: mit
datasets:
- hard
language:
- ar
pipeline_tag: text-classification
---
<!-- 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. -->
# BERThard
This model is a fine-tuned version of [aubmindlab/bert-base-arabertv2](https://huggingface.co/aubmindlab/bert-base-arabertv2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4141
- Accuracy: 0.8311
## 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: 8
- 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 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.4488 | 1.0 | 5946 | 0.4104 | 0.8232 |
| 0.3866 | 2.0 | 11892 | 0.4047 | 0.8288 |
| 0.3462 | 3.0 | 17838 | 0.4141 | 0.8311 |
### Framework versions
- Transformers 4.28.1
- Pytorch 1.12.1+cu116
- Datasets 2.4.0
- Tokenizers 0.12.1
|
AimB/mT5-en-kr-opus
|
[] | null |
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| 0 | 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 playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="wzhao82/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"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
Aimendo/Triage
|
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}
| 0 | null |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-pg1-pixelcopter
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 66.80 +/- 54.12
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
|
Akashpb13/Kabyle_xlsr
|
[
"pytorch",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"kab",
"dataset:mozilla-foundation/common_voice_8_0",
"transformers",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"sw",
"robust-speech-event",
"model_for_talk",
"hf-asr-leaderboard",
"license:apache-2.0",
"model-index"
] |
automatic-speech-recognition
|
{
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"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
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}
| 3 | null |
---
license: creativeml-openrail-m
base_model: anic87/pcam-tumor-text
instance_prompt: a photo of sks noncancerous-pathology-tissue
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- dreambooth
inference: true
---
# DreamBooth - anic87/pcam-tumor-normal-text
This is a dreambooth model derived from anic87/pcam-tumor-text. The weights were trained on a photo of sks noncancerous-pathology-tissue using [DreamBooth](https://dreambooth.github.io/).
You can find some example images in the following.
DreamBooth for the text encoder was enabled: True.
|
Akashpb13/xlsr_kurmanji_kurdish
|
[
"pytorch",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"kmr",
"ku",
"dataset:mozilla-foundation/common_voice_8_0",
"transformers",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"robust-speech-event",
"model_for_talk",
"hf-asr-leaderboard",
"license:apache-2.0",
"model-index"
] |
automatic-speech-recognition
|
{
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"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
"task_specific_params": {
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}
}
}
| 10 | null |
---
license: creativeml-openrail-m
tags:
- text-to-image
widget:
- text: ultmedm
---
### edm-ultmedm-v2 Dreambooth model trained by wimvanhenden with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v1-5 base model
You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb). Don't forget to use the concept prompts!
Sample pictures of:
ultmedm (use that on your prompt)
|
Akashpb13/xlsr_maltese_wav2vec2
|
[
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"mt",
"dataset:common_voice",
"transformers",
"audio",
"speech",
"xlsr-fine-tuning-week",
"license:apache-2.0",
"model-index"
] |
automatic-speech-recognition
|
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"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
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}
}
}
| 8 | null |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: ly111/t5small-finetuned-xsum
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. -->
# ly111/t5small-finetuned-xsum
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 3.1350
- Validation Loss: 2.7416
- Train Rouge1: 20.9336
- Train Rouge2: 3.9725
- Train Rougel: 16.4166
- Train Rougelsum: 16.3889
- Train Gen Len: 18.7077
- Epoch: 0
## 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': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Rouge1 | Train Rouge2 | Train Rougel | Train Rougelsum | Train Gen Len | Epoch |
|:----------:|:---------------:|:------------:|:------------:|:------------:|:---------------:|:-------------:|:-----:|
| 3.1350 | 2.7416 | 20.9336 | 3.9725 | 16.4166 | 16.3889 | 18.7077 | 0 |
### Framework versions
- Transformers 4.28.1
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
AkshatSurolia/ICD-10-Code-Prediction
|
[
"pytorch",
"bert",
"transformers",
"text-classification",
"license:apache-2.0",
"has_space"
] |
text-classification
|
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}
| 994 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: DSChallengeLearningRate
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. -->
# DSChallengeLearningRate
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: 0.3253
- Accuracy: 0.9202
## 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: 16
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1889 | 1.0 | 746 | 0.3511 | 0.9122 |
| 0.1358 | 2.0 | 1492 | 0.3253 | 0.9202 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
AkshaySg/langid
|
[
"multilingual",
"dataset:VoxLingua107",
"speechbrain",
"audio-classification",
"embeddings",
"Language",
"Identification",
"pytorch",
"ECAPA-TDNN",
"TDNN",
"VoxLingua107",
"license:apache-2.0"
] |
audio-classification
|
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| 2 | null |
---
license: apache-2.0
tags:
- summarization
- generated_from_trainer
datasets:
- wiki_lingua
metrics:
- rouge
model-index:
- name: wiki_lingua-fr-8-3-5.6e-05-mt5-small-finetuned
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: wiki_lingua
type: wiki_lingua
config: fr
split: test
args: fr
metrics:
- name: Rouge1
type: rouge
value: 19.9596
---
<!-- 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. -->
# wiki_lingua-fr-8-3-5.6e-05-mt5-small-finetuned
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the wiki_lingua dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9117
- Rouge1: 19.9596
- Rouge2: 7.5052
- Rougel: 17.4363
- Rougelsum: 19.5192
# Baseline LEAD-64
- Rouge1: 22.4
- Rouge2: 5.92
- Rougel: 14.44
- Rougelsum: 14.44
## 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: 5.6e-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
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|
| 2.8962 | 1.0 | 5428 | 2.0026 | 18.8621 | 6.6127 | 16.0264 | 18.4354 |
| 2.313 | 2.0 | 10856 | 1.9260 | 19.7274 | 7.2791 | 17.0466 | 19.2904 |
| 2.2248 | 3.0 | 16284 | 1.9117 | 19.9596 | 7.5052 | 17.4363 | 19.5192 |
### Framework versions
- Transformers 4.27.4
- Pytorch 1.13.0
- Datasets 2.1.0
- Tokenizers 0.13.2
|
Akuva2001/SocialGraph
|
[
"has_space"
] | null |
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| 0 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: DSChallengeWeightDecay
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. -->
# DSChallengeWeightDecay
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: 0.4742
- Accuracy: 0.9199
## 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: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.049 | 1.0 | 746 | 0.4537 | 0.9152 |
| 0.0204 | 2.0 | 1492 | 0.4742 | 0.9199 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
AlErysvi/Erys
|
[] | null |
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| 0 | null |
---
license: mit
tags:
- pytorch
- diffusers
- unconditional-image-generation
- diffusion-models-class
---
# 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('SaudxInu/sd-class-butterflies-32')
image = pipeline().images[0]
image
```
|
AlanDev/test
|
[] | null |
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| 0 | null |
Access to model mrsdoublecups2023/MRSDOUBLECUPS is restricted and you are not in the authorized list. Visit https://huggingface.co/mrsdoublecups2023/MRSDOUBLECUPS to ask for access.
|
AlbertHSU/BertTEST
|
[
"pytorch"
] | null |
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| 8 | 2023-05-01T18:48:33Z |
---
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/1080023535320424450/kR1TtMHc_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">Matthew Keys</div>
<div style="text-align: center; font-size: 14px;">@matthewkeyslive</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 Matthew Keys.
| Data | Matthew Keys |
| --- | --- |
| Tweets downloaded | 3235 |
| Retweets | 865 |
| Short tweets | 188 |
| Tweets kept | 2182 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/5fkgkze3/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 @matthewkeyslive's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/wvvli2np) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/wvvli2np/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/matthewkeyslive')
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)
|
Aleksandar/electra-srb-ner
|
[
"pytorch",
"safetensors",
"electra",
"token-classification",
"dataset:wikiann",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] |
token-classification
|
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| 15 | 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: 7.48 +/- 2.78
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="alix03/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"])
```
|
Aleksandar/electra-srb-oscar
|
[
"pytorch",
"electra",
"fill-mask",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] |
fill-mask
|
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| 6 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-3000-samples
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. -->
# finetuning-sentiment-model-3000-samples
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: 0.3061
- Accuracy: 0.8567
- F1: 0.8562
## 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: 2
### Training results
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Tokenizers 0.13.3
|
AlexDemon/Alex
|
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}
| 0 | null |
---
license: apache-2.0
datasets:
- vicgalle/alpaca-gpt4
- ehartford/WizardLM_alpaca_evol_instruct_70k_unfiltered
language:
- en
library_name: transformers
pipeline_tag: text-generation
---
# openaccess-ai-collective/llama-13b-alpaca-wizard
## Trained
- `vicgalle/alpaca-gpt4` 1 epoch, learning rate 3e-5 https://wandb.ai/wing-lian/wizard-vicuna-gpt4/overview
- `deepspeed scripts/finetune.py configs/axolotl/wizard-vicuna-13b-step1.yml --deepspeed configs/ds_config.json --num_epochs 2 --warmup_steps 46 --logging_steps 1 --save_steps 23`
- `wizardlm` https://wandb.ai/wing-lian/wizard-vicuna-gpt4/runs/4y38knw4
- `deepspeed scripts/finetune.py configs/axolotl/wizard-vicuna-13b-step2.yml --deepspeed configs/ds_config-step2.json --num_epochs 2 --logging_steps 1`
- `vicuna` TBD
<pre>Brought to you by the OpenAccess AI Collective</pre>
|
AlexN/xls-r-300m-fr-0
|
[
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"fr",
"dataset:mozilla-foundation/common_voice_8_0",
"transformers",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"robust-speech-event",
"hf-asr-leaderboard",
"license:apache-2.0",
"model-index"
] |
automatic-speech-recognition
|
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| 4 | 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: 0.925
- name: F1
type: f1
value: 0.9251879205114556
---
<!-- 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: 0.2273
- Accuracy: 0.925
- F1: 0.9252
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8401 | 1.0 | 250 | 0.3279 | 0.9025 | 0.8981 |
| 0.2575 | 2.0 | 500 | 0.2273 | 0.925 | 0.9252 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Alexander-Learn/bert-finetuned-squad-accelerate
|
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| 0 | null |
---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: BERTmsda
results: []
license: mit
language:
- ar
pipeline_tag: text-classification
---
<!-- 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. -->
# BERTmsda
This model is a fine-tuned version of [aubmindlab/bert-base-arabertv2](https://huggingface.co/aubmindlab/bert-base-arabertv2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4153
- Accuracy: 0.8629
## 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: 8
- 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 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3887 | 1.0 | 2937 | 0.3706 | 0.8546 |
| 0.301 | 2.0 | 5874 | 0.3746 | 0.8632 |
| 0.2284 | 3.0 | 8811 | 0.4153 | 0.8629 |
### Framework versions
- Transformers 4.28.1
- Pytorch 1.12.1+cu116
- Datasets 2.4.0
- Tokenizers 0.12.1
|
Alexandru/creative_copilot
|
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| 0 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilbert-id-law
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-id-law
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8912
## 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
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 2.2874 | 1.0 | 6262 | 2.1654 |
| 2.0961 | 2.0 | 12524 | 2.0036 |
| 2.0255 | 3.0 | 18786 | 1.9364 |
| 1.9767 | 4.0 | 25048 | 1.9011 |
| 1.9579 | 5.0 | 31310 | 1.8912 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Aliraza47/BERT
|
[] | null |
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| 0 | null |
---
license: apache-2.0
datasets:
- vicgalle/alpaca-gpt4
language:
- en
library_name: transformers
pipeline_tag: conversational
---
# Llama Adapter 13B
- Based on https://github.com/ZrrSkywalker/LLaMA-Adapter using HF transformers
- See HF PR https://github.com/huggingface/peft/pull/268
- W&B data: https://wandb.ai/wing-lian/llama-adapter-13b
|
Alvenir/wav2vec2-base-da
|
[
"pytorch",
"wav2vec2",
"pretraining",
"da",
"transformers",
"speech",
"license:apache-2.0"
] | null |
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"Wav2Vec2ForPreTraining"
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}
| 62 | 2023-05-01T21:52:07Z |
---
license: openrail
datasets:
- OpenAssistant/oasst1
language:
- en
- es
metrics:
- accuracy
library_name: adapter-transformers
pipeline_tag: feature-extraction
tags:
- art
---
|
aisoftware/Loquela
|
[
"onnx"
] | null |
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}
| 0 | null |
---
tags:
- conversational
---
# Bisho DialoGPT Model
|
Amrrs/south-indian-foods
|
[
"pytorch",
"tensorboard",
"vit",
"image-classification",
"transformers",
"huggingpics",
"model-index",
"autotrain_compatible"
] |
image-classification
|
{
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"ViTForImageClassification"
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"model_type": "vit",
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}
}
| 21 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- rotten_tomatoes
metrics:
- accuracy
model-index:
- name: test_trainer
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: rotten_tomatoes
type: rotten_tomatoes
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.501
---
<!-- 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. -->
# test_trainer
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the rotten_tomatoes dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7153
- Accuracy: 0.501
## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6412 | 0.01 | 1 | 0.7288 | 0.501 |
| 0.6171 | 0.02 | 2 | 0.7083 | 0.501 |
| 0.5805 | 0.02 | 3 | 0.7153 | 0.501 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Anders/itu-ams-summa
|
[] | null |
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| 0 | 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: 0.922
- name: F1
type: f1
value: 0.9221186592426542
---
<!-- 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: 0.2225
- Accuracy: 0.922
- F1: 0.9221
## 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 | 0.3273 | 0.9025 | 0.8984 |
| No log | 2.0 | 500 | 0.2225 | 0.922 | 0.9221 |
### Framework versions
- Transformers 4.28.1
- Pytorch 1.11.0
- Datasets 2.11.0
- Tokenizers 0.13.3
|
Andranik/TestQA2
|
[
"pytorch",
"electra",
"question-answering",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] |
question-answering
|
{
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"ElectraForQuestionAnswering"
],
"model_type": "electra",
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| 8 | null |
---
license: creativeml-openrail-m
base_model: stabilityai/stable-diffusion-2-1-base
instance_prompt: a photo of sks tumor-tissue-histology
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- dreambooth
inference: true
---
# DreamBooth - anic87/crc-tumor-text
This is a dreambooth model derived from stabilityai/stable-diffusion-2-1-base. The weights were trained on a photo of sks tumor-tissue-histology using [DreamBooth](https://dreambooth.github.io/).
You can find some example images in the following.
DreamBooth for the text encoder was enabled: True.
|
AndreLiu1225/t5-news-summarizer
|
[
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"T5ForConditionalGeneration"
],
"model_type": "t5",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": true,
"length_penalty": 2,
"max_length": 200,
"min_length": 30,
"no_repeat_ngram_size": 3,
"num_beams": 4,
"prefix": "summarize: "
},
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},
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},
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},
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}
}
}
| 10 | 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: 274.59 +/- 18.01
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)
```python
from huggingface_sb3 import load_from_hub
from stable_baselines3 import PPO
from stable_baselines3.common.env_util import make_vec_env
from stable_baselines3.common.evaluation import evaluate_policy
checkpoint = load_from_hub("jkkawach/ppo-LunarLander-v2", "ppo-LunarLander-v2.zip")
model = PPO.load(checkpoint)
env = make_vec_env("LunarLander-v2", n_envs=1)
print("Evaluating model")
mean_reward, std_reward = evaluate_policy(
model,
env,
n_eval_episodes=20,
deterministic=True,
)
print(f"Mean reward = {mean_reward:.2f} +/- {std_reward:.2f}")
obs = env.reset()
try:
while True:
action, _states = model.predict(obs, deterministic=True)
obs, rewards, dones, info = env.step(action)
env.render()
except KeyboardInterrupt:
pass
```
|
AndreLiu1225/t5-news
|
[
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"T5ForConditionalGeneration"
],
"model_type": "t5",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": true,
"length_penalty": 2,
"max_length": 200,
"min_length": 30,
"no_repeat_ngram_size": 3,
"num_beams": 4,
"prefix": "summarize: "
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to German: "
},
"translation_en_to_fr": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to French: "
},
"translation_en_to_ro": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to Romanian: "
}
}
}
| 18 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
split: test
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.93208
- name: F1
type: f1
value: 0.9324367340442463
---
<!-- 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 imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2312
- Accuracy: 0.9321
- F1: 0.9324
## 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: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.2634 | 1.0 | 1563 | 0.1887 | 0.9275 | 0.9268 |
| 0.1467 | 2.0 | 3126 | 0.2312 | 0.9321 | 0.9324 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
AndrewChar/model-QA-5-epoch-RU
|
[
"tf",
"distilbert",
"question-answering",
"ru",
"dataset:sberquad",
"transformers",
"generated_from_keras_callback",
"autotrain_compatible"
] |
question-answering
|
{
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"DistilBertForQuestionAnswering"
],
"model_type": "distilbert",
"task_specific_params": {
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| 109 | null |
---
license: creativeml-openrail-m
base_model: /projects/ac67/projects/diffusers/examples/dreambooth/crc-tumor-text/
instance_prompt: a photo of sks tissue-histology-without-tumor
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- dreambooth
inference: true
---
# DreamBooth - anic87/crc-tumor-normal-text
This is a dreambooth model derived from /projects/ac67/projects/diffusers/examples/dreambooth/crc-tumor-text/. The weights were trained on a photo of sks tissue-histology-without-tumor using [DreamBooth](https://dreambooth.github.io/).
You can find some example images in the following.
DreamBooth for the text encoder was enabled: True.
|
AndrewMcDowell/wav2vec2-xls-r-1B-german
|
[
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"de",
"dataset:mozilla-foundation/common_voice_8_0",
"transformers",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"robust-speech-event",
"hf-asr-leaderboard",
"license:apache-2.0",
"model-index"
] |
automatic-speech-recognition
|
{
"architectures": [
"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
"task_specific_params": {
"conversational": {
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| 8 | null |
Ivo22 is a language model designed for processing and exchanging text in Serbian and English. It was trained exclusively on literary works and technical literature for writing code in the Arduino C programming language.
|
AndrewMcDowell/wav2vec2-xls-r-300m-arabic
|
[
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"ar",
"dataset:mozilla-foundation/common_voice_7_0",
"transformers",
"generated_from_trainer",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_7_0",
"robust-speech-event",
"license:apache-2.0",
"model-index"
] |
automatic-speech-recognition
|
{
"architectures": [
"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
"task_specific_params": {
"conversational": {
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},
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}
| 4 | null |
---
license: mit
---
### ahx-beta-45043dc on Stable Diffusion
This is the `<ahx-beta-45043dc>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as a `style`:
|
Andrey1989/mbert-finetuned-ner_2
|
[] | null |
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| 0 | null |
Access to model Hakulani/t5-end2end-questions-generation is restricted and you are not in the authorized list. Visit https://huggingface.co/Hakulani/t5-end2end-questions-generation to ask for access.
|
Andrey1989/mt5-small-finetuned-mlsum-fr
|
[] | null |
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| 0 | null |
---
license: unknown
duplicated_from: Mintchocowasabi230331/PrivateMYMODEL
---
개인용 모델 모음
2023년 04월 21일까지의 모은 하드의 모델들 / 로라들 / VAE들이 모아져있음
|
Andrija/RobertaFastBPE
|
[] | null |
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| 0 | null |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: ratish/DBERT_Fault_v1
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. -->
# ratish/DBERT_Fault_v1
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:
- Train Loss: 1.0328
- Validation Loss: 1.1356
- Train Accuracy: 0.5897
- Epoch: 4
## 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', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 1520, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 1.6393 | 1.5483 | 0.3590 | 0 |
| 1.5144 | 1.5141 | 0.3590 | 1 |
| 1.4025 | 1.4162 | 0.4103 | 2 |
| 1.2335 | 1.3052 | 0.5128 | 3 |
| 1.0328 | 1.1356 | 0.5897 | 4 |
### Framework versions
- Transformers 4.28.1
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Andrija/SRoBERTa-L-NER
|
[
"pytorch",
"roberta",
"token-classification",
"hr",
"sr",
"multilingual",
"dataset:hr500k",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] |
token-classification
|
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| 6 | null |
Quantized from https://huggingface.co/Neko-Institute-of-Science/pygmalion-7b
|
Andrija/SRoBERTa-base
|
[
"pytorch",
"roberta",
"fill-mask",
"hr",
"sr",
"multilingual",
"dataset:oscar",
"dataset:leipzig",
"transformers",
"masked-lm",
"license:apache-2.0",
"autotrain_compatible"
] |
fill-mask
|
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| 80 | null |
---
license: mit
---
### ahx-beta-4504eb3 on Stable Diffusion
This is the `<ahx-beta-4504eb3>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as a `style`:
|
Andrija/SRoBERTaFastBPE-2
|
[
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
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"RobertaForMaskedLM"
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| 7 | null |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: soumya13/GPT2_CleanDesc_MAKE_v1.5
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. -->
# soumya13/GPT2_CleanDesc_MAKE_v1.5
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0052
- Validation Loss: 0.0002
- Train Accuracy: 1.0
- Epoch: 24
## 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', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 7600, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 4.0208 | 1.7346 | 0.3590 | 0 |
| 1.5028 | 0.9649 | 0.6923 | 1 |
| 0.8469 | 0.4756 | 0.9231 | 2 |
| 0.4664 | 0.1764 | 0.9231 | 3 |
| 0.2632 | 0.0836 | 0.9744 | 4 |
| 0.1579 | 0.0488 | 0.9744 | 5 |
| 0.1298 | 0.0250 | 1.0 | 6 |
| 0.0962 | 0.0136 | 1.0 | 7 |
| 0.0498 | 0.0041 | 1.0 | 8 |
| 0.0520 | 0.0022 | 1.0 | 9 |
| 0.0418 | 0.0016 | 1.0 | 10 |
| 0.0403 | 0.0013 | 1.0 | 11 |
| 0.0281 | 0.0009 | 1.0 | 12 |
| 0.0236 | 0.0008 | 1.0 | 13 |
| 0.0150 | 0.0008 | 1.0 | 14 |
| 0.0173 | 0.0007 | 1.0 | 15 |
| 0.0160 | 0.0005 | 1.0 | 16 |
| 0.0302 | 0.0004 | 1.0 | 17 |
| 0.0250 | 0.0003 | 1.0 | 18 |
| 0.0069 | 0.0003 | 1.0 | 19 |
| 0.0241 | 0.0003 | 1.0 | 20 |
| 0.0100 | 0.0003 | 1.0 | 21 |
| 0.0114 | 0.0002 | 1.0 | 22 |
| 0.0172 | 0.0002 | 1.0 | 23 |
| 0.0052 | 0.0002 | 1.0 | 24 |
### Framework versions
- Transformers 4.28.1
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Andry/111
|
[] | null |
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| 0 | 2023-05-02T00:30:43Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: ratish/DBERT_Fault_v1.1
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. -->
# ratish/DBERT_Fault_v1.1
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:
- Train Loss: 0.2870
- Validation Loss: 0.7250
- Train Accuracy: 0.6667
- Epoch: 3
## 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', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 1520, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 0.6423 | 0.7830 | 0.5128 | 0 |
| 0.5827 | 0.6110 | 0.6667 | 1 |
| 0.4089 | 0.7688 | 0.6410 | 2 |
| 0.2870 | 0.7250 | 0.6667 | 3 |
### Framework versions
- Transformers 4.28.1
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Andy1621/uniformer
|
[
"license:mit",
"has_space"
] | null |
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| 0 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilgpt2-finetuned-wikitext2
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. -->
# distilgpt2-finetuned-wikitext2
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.6421
## 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
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.7602 | 1.0 | 2334 | 3.6669 |
| 3.653 | 2.0 | 4668 | 3.6472 |
| 3.6006 | 3.0 | 7002 | 3.6421 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
AndyJ/clinicalBERT
|
[
"pytorch",
"transformers"
] | null |
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| 4 | null |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: ratish/DBERT_Fault_v1.2
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. -->
# ratish/DBERT_Fault_v1.2
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:
- Train Loss: 0.0745
- Validation Loss: 0.5163
- Train Accuracy: 0.7949
- Epoch: 8
## 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', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 3040, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 0.6576 | 0.6994 | 0.5128 | 0 |
| 0.5903 | 0.5923 | 0.7179 | 1 |
| 0.4339 | 0.4675 | 0.7949 | 2 |
| 0.3132 | 0.5160 | 0.7179 | 3 |
| 0.2338 | 0.5044 | 0.7692 | 4 |
| 0.2483 | 0.7382 | 0.6923 | 5 |
| 0.1555 | 0.4269 | 0.8462 | 6 |
| 0.0881 | 0.4634 | 0.7949 | 7 |
| 0.0745 | 0.5163 | 0.7949 | 8 |
### Framework versions
- Transformers 4.28.1
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Ani123/Ani
|
[] | null |
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| 0 | null |
---
tags:
- generated_from_keras_callback
model-index:
- name: RobCaamano/toxicity_weighted
results: []
---
# RobCaamano/toxicity_weighted
This model was trained from scratch on Distilbert Base Uncased.
It achieves the following results on the evaluation set:
- Train Loss: 0.0240
- Train Precision: 0.9522
- Train Recall: 0.9190
- Epoch: 11
## Model description
Finetuned model that uses Distilbert Base Uncased to detect types of toxic text. These include: "toxic", "severe_toxic", "obscene", "threat", "insult" & "identity_hate".
## Intended uses & limitations
Intended to classify text into different types of toxicity when it is detected. Trained off a small dataset with underrepresented categories.
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': 3e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Precision | Train Recall | Epoch |
|:----------:|:---------------:|:------------:|:-----:|
| 0.0440 | 0.9059 | 0.8294 | 7 |
| 0.0380 | 0.9223 | 0.8632 | 8 |
| 0.0314 | 0.9335 | 0.8838 | 9 |
| 0.0282 | 0.9437 | 0.9075 | 10 |
| 0.0240 | 0.9522 | 0.9190 | 11 |
### Framework versions
- Transformers 4.28.1
- TensorFlow 2.10.0
- Datasets 2.11.0
- Tokenizers 0.13.3
|
Anirbanbhk/Hate-speech-Pretrained-movies
|
[
"tf",
"bert",
"text-classification",
"transformers"
] |
text-classification
|
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| 20 | null |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: ratish/DBERT_Fault_v1.3
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. -->
# ratish/DBERT_Fault_v1.3
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:
- Train Loss: 0.2492
- Validation Loss: 0.6244
- Train Accuracy: 0.6410
- Epoch: 5
## 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', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2128, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 0.6707 | 0.7045 | 0.5128 | 0 |
| 0.6048 | 0.6772 | 0.5897 | 1 |
| 0.4599 | 0.4915 | 0.7692 | 2 |
| 0.3711 | 0.4883 | 0.6923 | 3 |
| 0.3174 | 0.5588 | 0.7436 | 4 |
| 0.2492 | 0.6244 | 0.6410 | 5 |
### Framework versions
- Transformers 4.28.1
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Anji/roberta-base-squad2-finetuned-squad
|
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| 0 | null |
---
language:
- es
pipeline_tag: zero-shot-classification
license: apache-2.0
tags:
- "longformer"
- "national library of spain"
- "spanish"
- "bne"
datasets:
- "hackathon-pln-es/nli-es"
widget:
- text: >-
Para detener la pandemia, es importante que todos se presenten a
vacunarse.
candidate_labels: salud, deporte, entretenimiento
---
|
Ankitha/DialoGPT-small-harrypotter
|
[] | null |
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| 0 | null |
---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
---
# Likang/OSS-Governance
This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("Likang/OSS-Governance")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
|
Ankitha/DialoGPT-small-harrypottery
|
[] | null |
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| 0 | null |
Access to model speedppc/autotrain-beeline-human-v2-54590127716 is restricted and you are not in the authorized list. Visit https://huggingface.co/speedppc/autotrain-beeline-human-v2-54590127716 to ask for access.
|
Ann2020/distilbert-base-uncased-finetuned-ner
|
[
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"dataset:conll2003",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible"
] |
token-classification
|
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"DistilBertForTokenClassification"
],
"model_type": "distilbert",
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| 4 | null |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: ratish/DBERT_Fault_v1.4
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. -->
# ratish/DBERT_Fault_v1.4
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:
- Train Loss: 0.0165
- Validation Loss: 1.1489
- Train Accuracy: 0.7436
- Epoch: 13
## 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', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2128, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 0.6677 | 0.7030 | 0.5128 | 0 |
| 0.6287 | 0.6204 | 0.7436 | 1 |
| 0.4746 | 0.4927 | 0.7949 | 2 |
| 0.3647 | 0.5168 | 0.7692 | 3 |
| 0.2682 | 0.5776 | 0.7436 | 4 |
| 0.2184 | 0.4834 | 0.8205 | 5 |
| 0.1997 | 0.5296 | 0.7692 | 6 |
| 0.1188 | 0.6967 | 0.7949 | 7 |
| 0.0945 | 0.6440 | 0.8205 | 8 |
| 0.0539 | 0.6911 | 0.7949 | 9 |
| 0.0271 | 0.8044 | 0.7949 | 10 |
| 0.0242 | 0.7906 | 0.7949 | 11 |
| 0.0264 | 0.8078 | 0.8462 | 12 |
| 0.0165 | 1.1489 | 0.7436 | 13 |
### Framework versions
- Transformers 4.28.1
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Ann2020/rubert-base-cased-finetuned-ner
|
[] | null |
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| 0 | null |
---
tags:
- generated_from_trainer
model-index:
- name: t5-MCQ-question-generator_val
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. -->
# t5-MCQ-question-generator_val
This model is a fine-tuned version of [Bilkies/t5-MCQ-question-generator_v1](https://huggingface.co/Bilkies/t5-MCQ-question-generator_v1) 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: 0.001
- train_batch_size: 4
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Anomic/DialoGPT-medium-loki
|
[] | null |
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| 0 | null |
---
tags:
- generated_from_trainer
model-index:
- name: kaz_legal_distilbert_full_corpus_10.0
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. -->
# kaz_legal_distilbert_full_corpus_10.0
This model is a fine-tuned version of [S:\src\pipelines\distil_bert\models\3-epochs-full\model\checkpoint-11000](https://huggingface.co/S:\src\pipelines\distil_bert\models\3-epochs-full\model\checkpoint-11000) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.9163
## 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 6.6228 | 0.32 | 1000 | 6.4585 |
| 6.2811 | 0.64 | 2000 | 6.2602 |
| 6.1313 | 0.96 | 3000 | 6.1338 |
| 5.9671 | 1.28 | 4000 | 5.9722 |
| 5.8529 | 1.6 | 5000 | 5.8634 |
| 5.7655 | 1.92 | 6000 | 5.7714 |
| 5.6925 | 2.24 | 7000 | 5.6978 |
| 5.6311 | 2.56 | 8000 | 5.6411 |
| 5.5994 | 2.88 | 9000 | 5.6108 |
| 5.5758 | 3.2 | 10000 | 5.5260 |
| 5.4439 | 3.52 | 11000 | 5.3753 |
| 5.3187 | 3.84 | 12000 | 5.2224 |
| 5.0263 | 5.12 | 16000 | 4.7246 |
| 4.6228 | 6.4 | 20000 | 4.3538 |
| 4.31 | 7.68 | 24000 | 4.0828 |
| 4.1015 | 8.96 | 28000 | 3.9163 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Anonymous/ReasonBERT-BERT
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
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"BertModel"
],
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| 5 | 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="melobron/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"])
```
|
Anonymous/ReasonBERT-TAPAS
|
[
"pytorch",
"tapas",
"feature-extraction",
"transformers"
] |
feature-extraction
|
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"TapasModel"
],
"model_type": "tapas",
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}
| 7 | null |
---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: kaz_legal_distilbert_full_corpus_10.0_6
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. -->
# kaz_legal_distilbert_full_corpus_10.0_6
This model is a fine-tuned version of [kaisar-barlybay-sse/kaz_legal_distilbert_full_corpus_10.0](https://huggingface.co/kaisar-barlybay-sse/kaz_legal_distilbert_full_corpus_10.0) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1161
- Accuracy: 0.3812
## 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: 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: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.4011 | 1.0 | 501 | 1.3857 | 0.2974 |
| 1.3922 | 2.0 | 1002 | 1.4143 | 0.3772 |
| 1.285 | 3.0 | 1503 | 1.5145 | 0.3752 |
| 1.0606 | 4.0 | 2004 | 1.6871 | 0.3832 |
| 0.8706 | 5.0 | 2505 | 2.0051 | 0.3852 |
| 0.7318 | 6.0 | 3006 | 2.1161 | 0.3812 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Anonymous0230/model_name
|
[] | null |
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| 0 | 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: 7.54 +/- 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="melobron/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"])
```
|
AnonymousNLP/pretrained-model-1
|
[
"pytorch",
"gpt2",
"transformers"
] | null |
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"GPT2DoubleHeadsModel"
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| 4 | 2023-05-02T01:30:17Z |
---
tags:
- autotrain
- summarization
language:
- en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- Adongua/autotrain-data-test3-gam-t5
co2_eq_emissions:
emissions: 1.6667833786317627
---
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 54599127739
- CO2 Emissions (in grams): 1.6668
## Validation Metrics
- Loss: 0.678
- Rouge1: 36.735
- Rouge2: 12.751
- RougeL: 34.758
- RougeLsum: 34.868
- Gen Len: 13.670
## 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/Adongua/autotrain-test3-gam-t5-54599127739
```
|
AnonymousNLP/pretrained-model-2
|
[
"pytorch",
"gpt2",
"transformers"
] | null |
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"GPT2DoubleHeadsModel"
],
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}
| 4 | 2023-05-02T01:30:25Z |
---
tags:
- chatbot
---
import nltk
import numpy as np
import random
class Chatbot:
def __init__(self, train_data):
self.train_data = train_data
self.vocabulary = set()
self.word_to_index = {}
self.index_to_word = {}
self.create_vocabulary()
self.build_model()
def create_vocabulary(self):
for sentence in self.train_data:
for word in sentence:
self.vocabulary.add(word)
self.vocabulary = sorted(self.vocabulary)
self.word_to_index = {word: i for i, word in enumerate(self.vocabulary)}
self.index_to_word = {i: word for i, word in enumerate(self.vocabulary)}
def build_model(self):
self.num_words = len(self.vocabulary)
self.W = np.random.randn(self.num_words, self.num_words)
self.b = np.random.randn(self.num_words)
def predict(self, sentence):
# Convert the sentence to a sequence of indices.
indices = []
for word in sentence:
indices.append(self.word_to_index[word])
# Calculate the probability of each possible next word.
probabilities = np.dot(indices, self.W) + self.b
# Choose the word with the highest probability.
next_word = self.index_to_word[np.argmax(probabilities)]
return next_word
def generate_text(self, start_text, max_length=100):
sentence = start_text
for _ in range(max_length):
next_word = self.predict(sentence)
sentence += " " + next_word
return sentence
def respond_to(self, input_text):
input_words = nltk.word_tokenize(input_text.lower())
# Check for special commands
if input_words[0] == "repeat":
return " ".join(input_words[1:])
elif input_words[0] == "generate":
start_text = " ".join(input_words[1:])
return self.generate_text(start_text)
else:
# Find the most similar sentence in the training data.
similarity_scores = []
for sentence in self.train_data:
similarity_score = nltk.jaccard_distance(set(sentence), set(input_words))
similarity_scores.append(similarity_score)
most_similar_index = np.argmin(similarity_scores)
most_similar_sentence = self.train_data[most_similar_index]
# Generate a response based on the most similar sentence.
response = ""
for word in most_similar_sentence:
response += self.predict([word]) + " "
return response.strip()
def main():
# Load the training data.
train_data = nltk.corpus.reuters.sents()
# Create the chatbot.
chatbot = Chatbot(train_data)
# Start a conversation.
print("Chatbot: Hi, I'm a chatbot. What can I help you with?")
while True:
user_input = input("User: ")
if user_input.lower() in ["bye", "goodbye", "exit", "quit"]:
print("Chatbot: Goodbye!")
break
response = chatbot.respond_to(user_input)
print("Chatbot:", response)
if __name__ == "__main__":
main()
|
AnonymousSub/declutr-biomed-roberta-papers
|
[
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
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}
}
}
| 7 | null |
<table>
<thead>
<tr>
<th>Epoch</th>
<th>Training Loss</th>
<th>Validation Loss</th>
<th>Rouge1</th>
<th>Rouge2</th>
<th>Rougel</th>
<th>Rougelsum</th>
<th>Gen Len</th>
</tr>
</thead>
<tr>
<td>0</td>
<td>0.630300</td>
<td>0.412157</td>
<td>0.417600</td>
<td>0.263800</td>
<td>0.332800</td>
<td>0.406200</td>
<td>794.000000</td>
</tr>
<tr>
<td>1</td>
<td>0.445600</td>
<td>0.371808</td>
<td>0.516700</td>
<td>0.336200</td>
<td>0.415500</td>
<td>0.508000</td>
<td>560.642900</td>
</tr>
<tr>
<td>2</td>
<td>0.398800</td>
<td>0.350914</td>
<td>0.562700</td>
<td>0.375400</td>
<td>0.443900</td>
<td>0.552700</td>
<td>523.714300</td>
</tr>
<tr>
<td>4</td>
<td>0.350600</td>
<td>0.334888</td>
<td>0.553300</td>
<td>0.364900</td>
<td>0.427100</td>
<td>0.538800</td>
<td>464.035700</td>
</tr>
<tr>
<td>5</td>
<td>0.334300</td>
<td>0.326556</td>
<td>0.552100</td>
<td>0.361400</td>
<td>0.429900</td>
<td>0.540300</td>
<td>517.821400</td>
</tr>
<tr>
<td>6</td>
<td>0.322300</td>
<td>0.321693</td>
<td>0.596600</td>
<td>0.400800</td>
<td>0.469400</td>
<td>0.586400</td>
<td>414.892900</td>
</tr>
<tr>
<td>8</td>
<td>0.308800</td>
<td>0.321562</td>
<td>0.594200</td>
<td>0.389100</td>
<td>0.458500</td>
<td>0.581800</td>
<td>401.357100</td>
</tr>
<tr>
<td>8</td>
<td>0.300100</td>
<td>0.319800</td>
<td>0.586200</td>
<td>0.376100</td>
<td>0.453400</td>
<td>0.571500</td>
<td>381.357100</td>
</tr>
<tr>
<td>9</td>
<td>0.291200</td>
<td>0.319443</td>
<td>0.611500</td>
<td>0.399600</td>
<td>0.468600</td>
<td>0.597500</td>
<td>368.821400</td>
</tr>
<tr>
<td>10</td>
<td>0.282900</td>
<td>0.318927</td>
<td>0.593200</td>
<td>0.388700</td>
<td>0.459100</td>
<td>0.579800</td>
<td>354.285700</td>
</tr>
<tr>
<td>12</td>
<td>0.273700</td>
<td>0.319651</td>
<td>0.594000</td>
<td>0.394200</td>
<td>0.457000</td>
<td>0.580800</td>
<td>386.785700</td>
</tr>
<tr>
<td>12</td>
<td>0.268100</td>
<td>0.315178</td>
<td>0.603700</td>
<td>0.396100</td>
<td>0.465300</td>
<td>0.588500</td>
<td>365.714300</td>
</tr>
<tr>
<td>13</td>
<td>0.262000</td>
<td>0.312819</td>
<td>0.601500</td>
<td>0.402800</td>
<td>0.471700</td>
<td>0.586000</td>
<td>377.250000</td>
</tr>
<tr>
<td>14</td>
<td>0.254900</td>
<td>0.316255</td>
<td>0.601200</td>
<td>0.397600</td>
<td>0.469700</td>
<td>0.587900</td>
<td>353.071400</td>
</tr>
<tr>
<td>16</td>
<td>0.248500</td>
<td>0.316413</td>
<td>0.610300</td>
<td>0.407900</td>
<td>0.476000</td>
<td>0.597400</td>
<td>341.464300</td>
</tr>
<tr>
<td>16</td>
<td>0.243600</td>
<td>0.315982</td>
<td>0.611400</td>
<td>0.404900</td>
<td>0.483200</td>
<td>0.598300</td>
<td>379.571400</td>
</tr>
<tr>
<td>17</td>
<td>0.238900</td>
<td>0.318108</td>
<td>0.608100</td>
<td>0.408200</td>
<td>0.486100</td>
<td>0.594000</td>
<td>375.964300</td>
</tr>
<tr>
<td>18</td>
<td>0.233900</td>
<td>0.317792</td>
<td>0.600200</td>
<td>0.406300</td>
<td>0.471700</td>
<td>0.587600</td>
<td>346.964300</td>
</tr>
<tr>
<td>19</td>
<td>0.229600</td>
<td>0.322435</td>
<td>0.599100</td>
<td>0.407100</td>
<td>0.479600</td>
<td>0.586600</td>
<td>362.571400</td>
</tr>
</table>
|
AnonymousSub/rule_based_bert_triplet_epochs_1_shard_1_wikiqa_copy
|
[
"pytorch",
"bert",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"BertModel"
],
"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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},
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}
}
}
| 1 | null |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 580.50 +/- 192.75
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Felix555 -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Felix555 -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga Felix555
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
AnonymousSub/rule_based_roberta_bert_triplet_epochs_1_shard_1_squad2.0
|
[
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] |
question-answering
|
{
"architectures": [
"RobertaForQuestionAnswering"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
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},
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},
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}
}
}
| 3 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilbert-finetuned-lr1e-07-epochs25
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-finetuned-lr1e-07-epochs25
This model is a fine-tuned version of [distilbert-base-cased-distilled-squad](https://huggingface.co/distilbert-base-cased-distilled-squad) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 5.4974
## 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-07
- 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: 25
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 10 | 6.3781 |
| No log | 2.0 | 20 | 6.2766 |
| No log | 3.0 | 30 | 6.1849 |
| No log | 4.0 | 40 | 6.1020 |
| No log | 5.0 | 50 | 6.0250 |
| No log | 6.0 | 60 | 5.9616 |
| No log | 7.0 | 70 | 5.9008 |
| No log | 8.0 | 80 | 5.8449 |
| No log | 9.0 | 90 | 5.7978 |
| No log | 10.0 | 100 | 5.7540 |
| No log | 11.0 | 110 | 5.7150 |
| No log | 12.0 | 120 | 5.6789 |
| No log | 13.0 | 130 | 5.6482 |
| No log | 14.0 | 140 | 5.6217 |
| No log | 15.0 | 150 | 5.5974 |
| No log | 16.0 | 160 | 5.5775 |
| No log | 17.0 | 170 | 5.5601 |
| No log | 18.0 | 180 | 5.5449 |
| No log | 19.0 | 190 | 5.5323 |
| No log | 20.0 | 200 | 5.5215 |
| No log | 21.0 | 210 | 5.5128 |
| No log | 22.0 | 220 | 5.5062 |
| No log | 23.0 | 230 | 5.5013 |
| No log | 24.0 | 240 | 5.4983 |
| No log | 25.0 | 250 | 5.4974 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
AnonymousSub/rule_based_roberta_only_classfn_twostage_epochs_1_shard_1_wikiqa
|
[
"pytorch",
"roberta",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"RobertaForSequenceClassification"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
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},
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},
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},
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}
}
}
| 24 | 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: 57.04 +/- 104.43
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': 'train'
'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': 1000000
'learning_rate': 0.0002
'num_envs': 16
'num_steps': 512
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.95
'num_minibatches': 32
'update_epochs': 10
'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': 'dungtd2403/ppo-Lunarlander-v1-unit8'
'batch_size': 8192
'minibatch_size': 256}
```
|
AnonymousSub/rule_based_roberta_twostage_quadruplet_epochs_1_shard_1
|
[
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] |
feature-extraction
|
{
"architectures": [
"RobertaModel"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
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},
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},
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}
| 5 | null |
---
tags:
- generated_from_trainer
datasets:
- sem_eval_2018_task_1
metrics:
- f1
- accuracy
model-index:
- name: arabert-analysis
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: sem_eval_2018_task_1
type: sem_eval_2018_task_1
config: subtask5.arabic
split: validation
args: subtask5.arabic
metrics:
- name: F1
type: f1
value: 0.6712830957230141
- name: Accuracy
type: accuracy
value: 0.2547008547008547
---
<!-- 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. -->
# arabert-analysis
This model is a fine-tuned version of [aubmindlab/bert-large-arabertv02](https://huggingface.co/aubmindlab/bert-large-arabertv02) on the sem_eval_2018_task_1 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2997
- F1: 0.6713
- Roc Auc: 0.7730
- Accuracy: 0.2547
## 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: 20
- eval_batch_size: 20
- 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 | F1 | Roc Auc | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
| No log | 1.0 | 114 | 0.4142 | 0.3601 | 0.6079 | 0.0889 |
| No log | 2.0 | 228 | 0.3328 | 0.6358 | 0.7492 | 0.2205 |
| No log | 3.0 | 342 | 0.3123 | 0.6625 | 0.7659 | 0.2444 |
| No log | 4.0 | 456 | 0.3015 | 0.6716 | 0.7724 | 0.2496 |
| 0.3612 | 5.0 | 570 | 0.2997 | 0.6713 | 0.7730 | 0.2547 |
### Framework versions
- Transformers 4.29.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
AnonymousSub/rule_based_roberta_twostagetriplet_hier_epochs_1_shard_1_squad2.0
|
[
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] |
question-answering
|
{
"architectures": [
"RobertaForQuestionAnswering"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
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},
"summarization": {
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},
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"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 |
---
tags:
- conversational
---
#trail-2 chatbot model cricket
|
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