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
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|---|---|---|---|---|---|---|
ArnaudPannatier/MLPMixer
|
[] | null |
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| 0 | null |
---
license: cc-by-nc-4.0
pipeline_tag: token-classification
tags:
- flair
- token-classification
widget:
- text: 上 在 慶 德 宮
- text: 上 在 慶 德 宮 停 常 參 只 晝 講
language:
- zh
- ko
library_name: flair
---
## History NER
Citation
If you apply this library or model to any project and research, please cite our code:
```python
@misc{Nara-Lab: History_NER_2023,
title = {Nara-Lab : History NER (Classical Chinese) Token Classification},
author = {Sojung Lucia Kim (Lucia), Tea Hong Jang (Ted), Joon Mo Ahn (Joon), Hyng Il Lee (Henry)},
year = {2023},
howpublished = {\url{https://huggingface.co/Nara-Lab/History_NER}},
}
```
## Contact
This is released as pre-trained NLP model in the hope that it will be helpful to many history research institutes and other entities. We look forward to contacting us from various places who wish to cooperate with us.
[email protected]
|
Arnold/common_voiceha
|
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| 0 | null |
---
tags:
- image-classification
- timm
library_name: timm
license: apache-2.0
datasets:
- imagenet-1k
---
# Model card for convit_small.fb_in1k
A ConViT image classification model. Trained on ImageNet-1k by paper authors.
## Model Details
- **Model Type:** Image classification / feature backbone
- **Model Stats:**
- Params (M): 27.8
- GMACs: 5.8
- Activations (M): 17.9
- Image size: 224 x 224
- **Papers:**
- ConViT: Improving Vision Transformers with Soft Convolutional Inductive Biases: https://arxiv.org/abs/2103.10697
- **Dataset:** ImageNet-1k
- **Original:** https://github.com/facebookresearch/convit
## Model Usage
### Image Classification
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model('convit_small.fb_in1k', pretrained=True)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)
```
### Image Embeddings
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'convit_small.fb_in1k',
pretrained=True,
num_classes=0, # remove classifier nn.Linear
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor
# or equivalently (without needing to set num_classes=0)
output = model.forward_features(transforms(img).unsqueeze(0))
# output is unpooled, a (1, 197, 432) shaped tensor
output = model.forward_head(output, pre_logits=True)
# output is a (1, num_features) shaped tensor
```
## Model Comparison
Explore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results).
## Citation
```bibtex
@article{d2021convit,
title={ConViT: Improving Vision Transformers with Soft Convolutional Inductive Biases},
author={d'Ascoli, St{'e}phane and Touvron, Hugo and Leavitt, Matthew and Morcos, Ari and Biroli, Giulio and Sagun, Levent},
journal={arXiv preprint arXiv:2103.10697},
year={2021}
}
```
|
Arnold/wav2vec2-hausa-demo-colab
|
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| 0 | null |
---
tags:
- image-classification
- timm
library_name: timm
license: apache-2.0
datasets:
- imagenet-1k
---
# Model card for convit_tiny.fb_in1k
A ConViT image classification model. Trained on ImageNet-1k by paper authors.
## Model Details
- **Model Type:** Image classification / feature backbone
- **Model Stats:**
- Params (M): 5.7
- GMACs: 1.3
- Activations (M): 7.9
- Image size: 224 x 224
- **Papers:**
- ConViT: Improving Vision Transformers with Soft Convolutional Inductive Biases: https://arxiv.org/abs/2103.10697
- **Dataset:** ImageNet-1k
- **Original:** https://github.com/facebookresearch/convit
## Model Usage
### Image Classification
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model('convit_tiny.fb_in1k', pretrained=True)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)
```
### Image Embeddings
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'convit_tiny.fb_in1k',
pretrained=True,
num_classes=0, # remove classifier nn.Linear
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor
# or equivalently (without needing to set num_classes=0)
output = model.forward_features(transforms(img).unsqueeze(0))
# output is unpooled, a (1, 197, 192) shaped tensor
output = model.forward_head(output, pre_logits=True)
# output is a (1, num_features) shaped tensor
```
## Model Comparison
Explore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results).
## Citation
```bibtex
@article{d2021convit,
title={ConViT: Improving Vision Transformers with Soft Convolutional Inductive Biases},
author={d'Ascoli, St{'e}phane and Touvron, Hugo and Leavitt, Matthew and Morcos, Ari and Biroli, Giulio and Sagun, Levent},
journal={arXiv preprint arXiv:2103.10697},
year={2021}
}
```
|
Arnold/wav2vec2-large-xlsr-hausa2-demo-colab
|
[
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"dataset:common_voice",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
] |
automatic-speech-recognition
|
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| 5 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: tuned_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. -->
# tuned_model
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) 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: 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
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
ArshdeepSekhon050/DialoGPT-medium-RickAndMorty
|
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| 0 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-cased-distilled-squad-finetuned-lr1e-06-epochs15
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-cased-distilled-squad-finetuned-lr1e-06-epochs15
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: 1.0730
## 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-06
- 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: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 8 | 1.0971 |
| No log | 2.0 | 16 | 1.0827 |
| No log | 3.0 | 24 | 1.0815 |
| No log | 4.0 | 32 | 1.0722 |
| No log | 5.0 | 40 | 1.0708 |
| No log | 6.0 | 48 | 1.0693 |
| No log | 7.0 | 56 | 1.0708 |
| No log | 8.0 | 64 | 1.0715 |
| No log | 9.0 | 72 | 1.0721 |
| No log | 10.0 | 80 | 1.0765 |
| No log | 11.0 | 88 | 1.0793 |
| No log | 12.0 | 96 | 1.0782 |
| No log | 13.0 | 104 | 1.0755 |
| No log | 14.0 | 112 | 1.0734 |
| No log | 15.0 | 120 | 1.0730 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
AryanLala/autonlp-Scientific_Title_Generator-34558227
|
[
"pytorch",
"pegasus",
"text2text-generation",
"en",
"dataset:AryanLala/autonlp-data-Scientific_Title_Generator",
"transformers",
"autonlp",
"co2_eq_emissions",
"autotrain_compatible",
"has_space"
] |
text2text-generation
|
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| 103 | null |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -2.25 +/- 0.47
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Ashagi/Ashvx
|
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| 0 | null |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: testing_img_token
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. -->
# testing_img_token
This model is a fine-tuned version of [SCUT-DLVCLab/lilt-roberta-en-base](https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9187
- Eading: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2}
- Ext: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 9}
- Ub heading: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 15}
- Overall Precision: 0.0
- Overall Recall: 0.0
- Overall F1: 0.0
- Overall Accuracy: 0.4848
## 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: 0.0005
- 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: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Eading | Ext | Ub heading | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------------------------------------------------------:|:---------------------------------------------------------:|:----------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 1.4635 | 1.43 | 10 | 1.2178 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 9} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 15} | 0.0 | 0.0 | 0.0 | 0.4848 |
| 1.2206 | 2.86 | 20 | 1.0016 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 9} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 15} | 0.0 | 0.0 | 0.0 | 0.4848 |
| 1.0569 | 4.29 | 30 | 0.9783 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 9} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 15} | 0.0 | 0.0 | 0.0 | 0.4848 |
| 1.0201 | 5.71 | 40 | 0.9273 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 9} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 15} | 0.0 | 0.0 | 0.0 | 0.4848 |
| 0.9888 | 7.14 | 50 | 0.9187 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 9} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 15} | 0.0 | 0.0 | 0.0 | 0.4848 |
### Framework versions
- Transformers 4.26.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.2
|
Ashkanmh/bert-base-parsbert-uncased-finetuned
|
[
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] |
fill-mask
|
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| 3 | null |
---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/ThomasSimonini/Huggy
2. Step 1: Find your model_id: Flynews/ppo-Huggy-bonus
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Atchuth/MBOT
|
[] | null |
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| 0 | null |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-PixelCopter
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 16.70 +/- 17.49
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
|
Atlasky/Turkish-Negator
|
[] | null |
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}
| 0 | 2023-04-24T05:40:28Z |
---
license: openrail
library_name: diffusers
pipeline_tag: text-to-image
---
|
Atlasky/turkish-negator-nn
|
[] | null |
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}
| 0 | 2023-04-24T05:44:35Z |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: gpt-expt-sp-v3-K-600-MA-Mac-actions-kmeans-v5
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt-expt-sp-v3-K-600-MA-Mac-actions-kmeans-v5
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: 0.0162
## 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: 0.0005
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 512
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 500
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:------:|:---------------:|
| 0.1893 | 21.37 | 5000 | 0.0961 |
| 0.0497 | 42.73 | 10000 | 0.0740 |
| 0.0486 | 64.1 | 15000 | 0.0601 |
| 0.0333 | 85.47 | 20000 | 0.0425 |
| 0.0251 | 106.84 | 25000 | 0.0226 |
| 0.0213 | 128.21 | 30000 | 0.0190 |
| 0.0198 | 149.57 | 35000 | 0.0185 |
| 0.0189 | 170.94 | 40000 | 0.0177 |
| 0.0183 | 192.31 | 45000 | 0.0175 |
| 0.0179 | 213.67 | 50000 | 0.0171 |
| 0.0175 | 235.04 | 55000 | 0.0168 |
| 0.0173 | 256.41 | 60000 | 0.0167 |
| 0.0171 | 277.78 | 65000 | 0.0166 |
| 0.0169 | 299.15 | 70000 | 0.0165 |
| 0.0168 | 320.51 | 75000 | 0.0165 |
| 0.0167 | 341.88 | 80000 | 0.0164 |
| 0.0166 | 363.25 | 85000 | 0.0163 |
| 0.0165 | 384.62 | 90000 | 0.0163 |
| 0.0164 | 405.98 | 95000 | 0.0163 |
| 0.0163 | 427.35 | 100000 | 0.0162 |
| 0.0163 | 448.72 | 105000 | 0.0162 |
| 0.0163 | 470.09 | 110000 | 0.0162 |
| 0.0162 | 491.45 | 115000 | 0.0162 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1.post200
- Datasets 2.9.0
- Tokenizers 0.13.2
|
Augustvember/WokkaBot8
|
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| 0 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: anercorpDataset_v2.1
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. -->
# anercorpDataset_v2.1
This model is a fine-tuned version of [CAMeL-Lab/bert-base-arabic-camelbert-mix-ner](https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-mix-ner) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2755
- Precision: 0.7604
- Recall: 0.6878
- F1: 0.7223
- Accuracy: 0.9461
## 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
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.206 | 1.0 | 7057 | 0.2703 | 0.6971 | 0.6367 | 0.6655 | 0.9377 |
| 0.2036 | 2.0 | 14114 | 0.2438 | 0.7460 | 0.6756 | 0.7090 | 0.9449 |
| 0.1404 | 3.0 | 21171 | 0.2755 | 0.7604 | 0.6878 | 0.7223 | 0.9461 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
Augustvember/wokka4
|
[
"conversational"
] |
conversational
|
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| 0 | null |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_health_gathering_supreme
type: doom_health_gathering_supreme
metrics:
- type: mean_reward
value: 9.48 +/- 4.14
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r Developer-Karthi/rl_course_vizdoom_health_gathering_supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m .usr.local.lib.python3.9.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m .usr.local.lib.python3.9.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
Ayham/albert_roberta_summarization_cnn_dailymail
|
[
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] |
text2text-generation
|
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"EncoderDecoderModel"
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}
| 6 | null |
---
license: openrail
datasets:
- AlekseyKorshuk/persona-chat
language:
- en
pipeline_tag: text-generation
---
---
language:
- en
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
# Model Details
## Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** Deeppavlov team
- **Model type:** seq2seq
- **Language(s) (NLP):** English
- **License:** MIT
- **Finetuned from model:** [facebook/bart-base](facebook/bart-base)
# Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
## Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
```python
from typing import List, TypedDict
from dataclasses import dataclass
from itertools import chain
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch
@dataclass
class H2PersonaChatHyperparametersV1:
"""
chat_history_pair_length: int - dialogue pairs amount from the end
"""
model_name: str = "facebook/bart-base"
chat_history_pair_length: int = 7
persona_max_length: int = 14
chat_max_length: int = 25
debug_status: int = 0
class PersonaChatDatasetSampleV1(TypedDict):
"""
persona: List[str] - person fact sentence set
history: List[str] - chating history
"""
persona: List[str]
history: List[str]
sample_id: str
class H2Seq2SeqInferenceSampleDictV1(TypedDict):
input_ids: List[int]
attention_mask: List[int]
class H2Seq2SeqInferenceSampleDictV2(TypedDict):
input_ids: torch.Tensor
attention_mask: torch.Tensor
def flat_list(list_of_lists: List[List]) -> List:
return list(chain.from_iterable(list_of_lists))
class H2Seq2SeqInferencePersonaSampleV1:
def __init__(
self,
dataset_sample: PersonaChatDatasetSampleV1,
tokenizer: AutoTokenizer,
hyperparameters: H2PersonaChatHyperparametersV1,
) -> None:
self.dataset_sample = dataset_sample
self.tokenizer = tokenizer
self.hyperparameters = hyperparameters
def add_spaces_after(
self,
items: List[str],
) -> List[str]:
items = [item + " " for item in items]
return items
@property
def bos_token_id(self):
if "t5" in self.hyperparameters.model_name:
return []
if self.tokenizer.bos_token_id is None:
return []
return [self.tokenizer.bos_token_id]
@property
def eos_token_id(self):
if self.tokenizer.eos_token_id is None:
return []
return [self.tokenizer.eos_token_id]
def add_sep_beetween(self, items: List[str], sep=" EOS ") -> List[str]:
for i in range(1, len(items)):
items[i] = sep + items[i]
return items
def add_spaces_between(self, items: List[str]) -> List[str]:
items = self.add_spaces_after(items)
items[-1] = items[-1].strip()
return items
def get_sample(self) -> H2Seq2SeqInferenceSampleDictV1:
dialog_history = self.dataset_sample["history"]
dialog_history = dialog_history[-self.hyperparameters.chat_history_pair_length * 2 - 1 :]
dialog_history = self.add_sep_beetween(dialog_history)
persona = self.dataset_sample["persona"]
persona = self.add_sep_beetween(
persona,
sep=" ",
)
KNOWLEDGE_IDS = self.tokenizer.encode(
" [KNOWLEDGE] ",
add_special_tokens=False,
)
CONTEXT_IDS = self.tokenizer.encode(
" [CONTEXT] ",
add_special_tokens=False,
)
encoded_history = self.tokenizer.batch_encode_plus(
dialog_history,
add_special_tokens=False,
truncation=True,
max_length=self.hyperparameters.chat_max_length,
)
encoded_history = flat_list(encoded_history["input_ids"])
encoded_persona = self.tokenizer.batch_encode_plus(
persona,
add_special_tokens=False,
truncation=True,
max_length=self.hyperparameters.persona_max_length,
)
encoded_persona = flat_list(encoded_persona["input_ids"])
input_ids = [
*self.bos_token_id,
*CONTEXT_IDS,
*encoded_history,
*KNOWLEDGE_IDS,
*encoded_persona,
*self.eos_token_id,
]
attention_mask = [1] * len(input_ids)
return H2Seq2SeqInferenceSampleDictV1(
input_ids=input_ids,
attention_mask=attention_mask,
)
class DialogBotV1:
def __init__(
self,
model: AutoModelForSeq2SeqLM,
tokenizer: AutoTokenizer,
hyperparameters: H2PersonaChatHyperparametersV1,
history: List[str] = None,
persona: List[str] = None,
device: str = "cuda",
shuffle_persona: bool = True,
):
self.model = model
self.tokenizer = tokenizer
self.hyperparameters = hyperparameters
self.device = device
self.shuffle_persona = shuffle_persona
self.debug_status = hyperparameters.debug_status
if history is None:
self.history = []
self.history = history
if persona is None:
self.persona = []
self.persona = persona
def _get_sample(
self,
persona: List[str],
history: List[str],
) -> H2Seq2SeqInferenceSampleDictV1:
dataset_sample = PersonaChatDatasetSampleV1(
persona=persona,
history=history,
)
sample = H2Seq2SeqInferencePersonaSampleV1(
tokenizer=self.tokenizer,
hyperparameters=self.hyperparameters,
dataset_sample=dataset_sample,
)
sample = sample.get_sample()
print(self.tokenizer.decode(sample['input_ids']))
for key in sample.keys():
sample[key] = torch.tensor(sample[key]).unsqueeze(0).to(self.device)
return sample
def next_response(
self,
**generation_params,
) -> str:
sample = self._get_sample(
persona=self.persona,
history=self.history,
)
answer = self.generate_response(
sample,
**generation_params,
)
answer = self.tokenizer.batch_decode(
answer,
skip_special_tokens=True,
)
self.history.append(answer[0])
return answer[0]
def generate_response(
self,
sample: H2Seq2SeqInferenceSampleDictV1,
**generation_params,
):
"""
generation_params - https://huggingface.co/docs/transformers/v4.24.0/en/main_classes/text_generation
"""
with torch.no_grad():
return self.model.generate(
**sample,
**generation_params,
)
PRETRAINED_MODEL_NAME_OR_PATH = "DeepPavlov/bart-base-en-persona-chat"
PAIR_DIALOG_HISTORY_LENGTH = 2
# CHAT_MAX_LENGTH for single sentence, in tokens
CHAT_MAX_LENGTH = 25
# PERSONA_MAX_LENGTH for single sentence, in tokens
PERSONA_MAX_LENGTH = 19
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = AutoModelForSeq2SeqLM.from_pretrained(PRETRAINED_MODEL_NAME_OR_PATH)
model.to(device)
model.eval()
tokenizer = AutoTokenizer.from_pretrained(PRETRAINED_MODEL_NAME_OR_PATH)
if torch.cuda.is_available():
model.half()
hyperparameters = H2PersonaChatHyperparametersV1(
chat_history_pair_length=PAIR_DIALOG_HISTORY_LENGTH,
persona_max_length=PERSONA_MAX_LENGTH,
chat_max_length=CHAT_MAX_LENGTH,
model_name=PRETRAINED_MODEL_NAME_OR_PATH,
)
persona = [
"I like to play guitar.",
"I hate onions."
]
history = [
"I hate to talk about politics, what about you?"
]
persona_bot = DialogBotV1(
model=model,
tokenizer=tokenizer,
hyperparameters=hyperparameters,
history=history,
persona=persona,
device=device,
)
GENERATION_PARAMS = {
"max_new_tokens": 60,
"penalty_alpha": 0.15,
"top_k": 10
}
response = persona_bot.next_response(
**GENERATION_PARAMS,
)
print(response)
# i am not into politics. i am into music.
```
## Recommendations
# Training Details
## Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
- [Data Source | EN Persona Chat](https://s3.amazonaws.com/datasets.huggingface.co/personachat/personachat_self_original.json)
[More Information Needed]
### Preprocessing
- Initial data was splitted by this script:
```python
def persona_chat_dataset_tranformer_v1(
initial_dataset_path: str,
output_folder: str,
) -> None:
"""
example
persona_chat_dataset_tranformer_v1(
initial_dataset_path="./datasets/persona_chat/persona_chat.json",
output_folder="./datasets/persona_chat",
)
"""
assert initial_dataset_path is not None, "initial_dataset_path is None"
assert output_folder is not None, "output_folder is None"
with open(initial_dataset_path) as f:
initial_dataset = json.load(f)
train_dataset = initial_dataset["train"]
val_len = len(initial_dataset["valid"])
valid_dataset = initial_dataset["valid"][: val_len // 2]
test_dataset = initial_dataset["valid"][val_len // 2 :]
print(
f"Dataset lengths: train {len(train_dataset)}, valid {len(valid_dataset)}, test {len(test_dataset)}"
)
# save json files
with open(output_folder + "/train.json", "w") as f:
json.dump(train_dataset, f)
with open(output_folder + "/valid.json", "w") as f:
json.dump(valid_dataset, f)
with open(output_folder + "/test.json", "w") as f:
json.dump(test_dataset, f)
print("Datasets saved.")
```
# Evaluation
### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
- BLUEL
- CharF
- RougeL
|
Ayham/bert_bert_summarization_cnn_dailymail
|
[
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] |
text2text-generation
|
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}
}
}
| 4 | null |
---
tags:
- autotrain
- vision
- image-classification
datasets:
- nicolasvillamilsanchez/autotrain-data-petrography
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: 0.6949743007043966
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 51985122663
- CO2 Emissions (in grams): 0.6950
## Validation Metrics
- Loss: 0.149
- Accuracy: 0.980
- Macro F1: 0.978
- Micro F1: 0.980
- Weighted F1: 0.980
- Macro Precision: 0.976
- Micro Precision: 0.980
- Weighted Precision: 0.983
- Macro Recall: 0.983
- Micro Recall: 0.980
- Weighted Recall: 0.980
|
Ayham/distilbert_gpt2_summarization_cnndm
|
[
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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}
}
}
| 6 | null |
---
pipeline_tag: text-classification
widget:
- text: "Paris is the <mask> of France."
example_title: "Capital"
- text: "The goal of life is <mask>."
example_title: "Philosophy"
---
|
Ayham/xlnet_bert_summarization_cnn_dailymail
|
[
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
"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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"max_length": null,
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"prefix": null
}
}
}
| 7 | null |
---
datasets:
- yelp_review_full
language:
- en
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- code
widget:
- text: "messi is the best player of the world"
- text: "Milan is the best football club"
- text: "soccer is the most popular sport in the world"
---
|
Ayran/DialoGPT-small-harry-potter-1-through-3
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
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},
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},
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},
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},
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}
}
}
| 12 | null |
---
pipeline_tag: text-generation
widget:
- text: "Hello World!"
- text: "Have you ever wondered about"
---
|
AyushPJ/ai-club-inductions-21-nlp-ALBERT
|
[
"pytorch",
"albert",
"question-answering",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] |
question-answering
|
{
"architectures": [
"AlbertForQuestionAnswering"
],
"model_type": "albert",
"task_specific_params": {
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},
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}
}
}
| 8 | 2023-04-24T08:07:18Z |
---
license: apache-2.0
language:
- zh
tags:
- Chinese
---
# Open-Chinese-LLaMA
This project is a **Chinese large language model base** generated through **incremental pre-training on Chinese datasets** based on [LLaMA](https://github.com/facebookresearch/llama)-7B.
## Features
* This project provides a Chinese pre-trained model obtained through full-tuning, including Huggingface version weights.
* Compared to the original LLaMA, this model has significantly improved Chinese understanding and generation capabilities, achieving outstanding results in various downstream tasks. See [Evaluation](##Evaluation) for details.
* This project provides tools for converting Huggingface version weights and Meta version weights.
* Supports [🤗transformers](https://github.com/huggingface/transformers), and provides command-line tools for easy model testing.
## Contents
* [Model Download](##Model%20Download)
* [Local Demo](##Local%20Demo)
* [Evaluation](##Evaluation)
* [Model Format Conversion](##Model%20Format%20Conversion)
## Model Download
| Model Name | Weight Type | Download Link | SHA256 |
| --------------------------- | -------- | ------------------------------------------------------------ | ---------------------- |
| Open-Chinese-LLaMA-7B-Patch | Patch | [[🤗Huggingface]]() <br> [[Baidu Cloud]](https://pan.baidu.com/s/14E7iZKcH-5SHMDu97k70cg?pwd=gk34)<br>[[Google Driver]](https://drive.google.com/drive/folders/1THvuFzq_wojVfMLYV1qsSE_ddSjG0Ypv?usp=sharing) | [SHA256](./SHA256.txt) |
### Usage Notes
Meta officially released [LLaMA](https://github.com/facebookresearch/llama) does not open-source weights. To comply with relevant licenses, the model released this time is of the **patch** type, and must be used in conjunction with the official original weights.
We provide a [script](https://github.com/OpenLMLab/OpenChineseLLaMA) for installing the **patch**. After obtaining the official weights through regular channels, you can install the patch as follows:
```bash
python tools/patch_model.py --base_model <path_or_name_to_original_model>
--patch_model openlmlab/open-chinese-llama-7b-patch
--base_model_format <hf_or_raw>
```
Note: The installation method of this patch is inplace installation, that is, the installed patch is the complete Huggingface version of this model weight, and you can use transformers to load the model.
Note: This script depends on [OpenLMLab/collie](https://github.com/OpenLMLab/collie), please install this framework using the following command:
```bash
pip install git+https://github.com/OpenLMLab/collie.git
```
## Local Demo
For quick and easy model testing, we provide a command-line version of the demo. After successfully installing the patch according to [Usage Notes](###Usage%20Notes), you can use the script to start an interactive interface:
```bash
python cli_demo.py --model openlmlab/open-chinese-llama-7b-patch
--devices 0
--max_length 1024
--do_sample true
--top_k 40
--top_p 0.8
--temperature 0.7
--penalty 1.02
```
### Examples
Open-Chinese-LLaMA-7B on the left, original LLaMA on the right:
<div align=center><img src="https://raw.githubusercontent.com/OpenLMLab/OpenChineseLLaMA/main/pics/cli_demo1.png"></div>
<center style="font-size:14px;color:#C0C0C0;text-decoration:underline">text generation</center>
<br>
<div align=center><img src="https://raw.githubusercontent.com/OpenLMLab/OpenChineseLLaMA/main/pics/cli_demo2.png"></div>
<center style="font-size:14px;color:#C0C0C0;text-decoration:underline">code generation</center>
<br>
<div align=center><img src="https://raw.githubusercontent.com/OpenLMLab/OpenChineseLLaMA/main/pics/cli_demo3.png"></div>
<center style="font-size:14px;color:#C0C0C0;text-decoration:underline">instructions (Note: None have been Instruct-tuning)</center>
<br>
## Evaluation
Open-Chinese-LLaMA-7B performs far better than the original LLaMA on various tasks in Chinese and English datasets. The evaluation results of this model on some datasets are given below (the following indicators are Accuracy, the bigger the better):
| Dataset | LLAMA 7B | Open-Chinese-LLaMA-7B |
| -------- | -------- | ----------- |
| OCNLI | 31.5 | 45.5 |
| CHID | 25.87 | 71.47 |
| TNEWS | 8.70 | 26.78 |
| CMRC | 11.89 | 34.48 |
| PIQA | 79.8 | 77.31 |
| HumanEval | 10.5 | 14.63 |
| MBPP | 17.7 | 17.2 |
| **Average** | 26.57 | 41.05 |
Note: See [Benchmark.md](./benchmark/Benchmark.md) for full results
## Model Format Conversion
The model generated by [`patch_model.py`]((https://github.com/OpenLMLab/OpenChineseLLaMA)) in this project is **hf** format which can be loaded by [🤗transformers](https://github.com/huggingface/transformers). For convenience, we also provide a [conversion tool]((https://github.com/OpenLMLab/OpenChineseLLaMA)) between the official version model (raw) and hf:
```bash
python convert_model.py --model_path <path_or_name_to_your_hf_or_raw_model>
--source_format hf
--target_format raw
--target_path <path_you_want_to_save_the_converted_model>
--raw_parallel_degree 2
--raw_parallel_devices 0,1
```
Tip: When converting a model in raw format, you need to specify the tensor parallel size and corresponding device, and it can only be converted on a machine with a corresponding number of graphics cards.
|
AyushPJ/ai-club-inductions-21-nlp-ELECTRA-base-squad
|
[
"pytorch",
"electra",
"question-answering",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] |
question-answering
|
{
"architectures": [
"ElectraForQuestionAnswering"
],
"model_type": "electra",
"task_specific_params": {
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},
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},
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},
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}
}
}
| 12 | null |
---
datasets:
- yelp_review_full
language:
- en
pipeline_tag: text-generation
tags:
- code
widget:
- text: "Is pizza a delicious food ?"
- text: "what is the best football club in the world ?"
---
|
BME-TMIT/foszt2oszt
|
[
"pytorch",
"encoder-decoder",
"text2text-generation",
"hu",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
{
"architectures": [
"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
"task_specific_params": {
"conversational": {
"max_length": null
},
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},
"text-generation": {
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},
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},
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},
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"prefix": null
}
}
}
| 15 | null |
---
datasets:
- GEM/xwikis
metrics:
- bleu
widget:
- text: "I use this word because"
- text: "This example has written"
---
|
BOON/electra_qa
|
[] | null |
{
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}
}
}
| 0 | null |
# Model Card for PickScore v1
This model is a scoring function for images generated from text. It takes as input a prompt and a generated image and outputs a score.
It can be used as a general scoring function, and for tasks such as human preference prediction, model evaluation, image ranking, and more.
See our paper [Pick-a-Pic: An Open Dataset of User Preferences for Text-to-Image Generation](https://arxiv.org/abs/2305.01569) for more details.
## Model Details
### Model Description
This model was finetuned from CLIP-H using the [Pick-a-Pic dataset](https://huggingface.co/datasets/yuvalkirstain/pickapic_v1).
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [See the PickScore repo](https://github.com/yuvalkirstain/PickScore)
- **Paper:** [Pick-a-Pic: An Open Dataset of User Preferences for Text-to-Image Generation](https://arxiv.org/abs/2305.01569).
- **Demo [optional]:** [Huggingface Spaces demo for PickScore](https://huggingface.co/spaces/yuvalkirstain/PickScore)
## How to Get Started with the Model
Use the code below to get started with the model.
```python
# import
from transformers import AutoProcessor, AutoModel
# load model
device = "cuda"
processor_name_or_path = "laion/CLIP-ViT-H-14-laion2B-s32B-b79K"
model_pretrained_name_or_path = "yuvalkirstain/PickScore_v1"
processor = AutoProcessor.from_pretrained(processor_name_or_path)
model = AutoModel.from_pretrained(model_pretrained_name_or_path).eval().to(device)
def calc_probs(prompt, images):
# preprocess
image_inputs = processor(
images=images,
padding=True,
truncation=True,
max_length=77,
return_tensors="pt",
).to(device)
text_inputs = processor(
text=prompt,
padding=True,
truncation=True,
max_length=77,
return_tensors="pt",
).to(device)
with torch.no_grad():
# embed
image_embs = model.get_image_features(**image_inputs)
image_embs = image_embs / torch.norm(image_embs, dim=-1, keepdim=True)
text_embs = model.get_text_features(**text_inputs)
text_embs = text_embs / torch.norm(text_embs, dim=-1, keepdim=True)
# score
scores = model.logit_scale.exp() * (text_embs @ image_embs.T)[0]
# get probabilities if you have multiple images to choose from
probs = torch.softmax(scores, dim=-1)
return probs.cpu().tolist()
pil_images = [Image.open("my_amazing_images/1.jpg"), Image.open("my_amazing_images/2.jpg")]
prompt = "fantastic, increadible prompt"
print(calc_probs(prompt, pil_images))
```
## Training Details
### Training Data
This model was trained on the [Pick-a-Pic dataset](https://huggingface.co/datasets/yuvalkirstain/pickapic_v1).
### Training Procedure
TODO - add paper.
## Citation [optional]
If you find this work useful, please cite:
```bibtex
@inproceedings{Kirstain2023PickaPicAO,
title={Pick-a-Pic: An Open Dataset of User Preferences for Text-to-Image Generation},
author={Yuval Kirstain and Adam Polyak and Uriel Singer and Shahbuland Matiana and Joe Penna and Omer Levy},
year={2023}
}
```
**APA:**
[More Information Needed]
|
Bagus/SER-LSSED
|
[] | null |
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}
| 0 | null |
---
license: mit
---
recreated the method in https://arxiv.org/abs/2304.11062
combined it with a t5-base due to the encoder already using relative positionsl embeddings. modified a few scripts i found deep in the gptneox history to adapt the decoder to have similar embeddings and applied the logic to the decoder as well. testing is showing, at least so far, that this effectively avoids rhe context issue that transformers-xl has.
causing its inference to scale linearly against not only its seq length but its hidden layers, width, and depth, its still preliminary but this feels almost as STOA as my other models
if you liked this model, consider checking our Autonomous Generative Instrument lnstrument project
where updates about the new foundation model will be released. feel free to check out our website Alignmentlab.ai
|
Banshee/LukeSkywalker
|
[] | null |
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}
| 0 | null |
---
pipeline_tag: text-classification
---
widget:
- text: "Jens Peter Hansen kommer fra Danmark"
|
Banshee/dialoGPT-small-luke
|
[] | null |
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}
| 0 | 2023-04-24T09:08:57Z |
---
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="mojemai/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"])
```
|
BaptisteDoyen/camembert-base-xnli
|
[
"pytorch",
"tf",
"camembert",
"text-classification",
"fr",
"dataset:xnli",
"transformers",
"zero-shot-classification",
"xnli",
"nli",
"license:mit",
"has_space"
] |
zero-shot-classification
|
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| 405,474 | 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: -221.38 +/- 108.44
name: mean_reward
verified: false
---
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
```python
{'exp_name': 'ppo'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'env_id': 'LunarLander-v2'
'total_timesteps': 50000
'learning_rate': 0.00025
'num_envs': 4
'num_steps': 128
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.95
'num_minibatches': 4
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.3
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'Waterboy96/PPO-Lunar'
'batch_size': 512
'minibatch_size': 128}
```
|
BatuhanYilmaz/bert-finetuned-mrpc
|
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}
| 0 | 2023-04-24T09:18:10Z |
---
language:
- en
license: creativeml-openrail-m
thumbnail: "https://huggingface.co/Guizmus/SDArt_UnmythicalCreatures/resolve/main/showcase.jpg"
tags:
- stable-diffusion
- text-to-image
- image-to-image
---
# SDArt : Un-Mythical Creatures (version based on 1.5)
## Theme
[Excerpt from “Chronicles of Curiosity: Professor Lawrence's Field Guide”]
> It was a creature of un-mythical origins, imbued by the forest itself. Its body was sleek and agile, with fur the color of a moonless deep blue midnight sky and eyes that glowed like embers in the darkness. But what truly set it apart were the petals that bloomed from its back, opening and closing like a flower that only blossomed at night.
> The creature's habitat was the forest floor, where it moved with an effortless grace that defied the laws of physics. Its body seemed to flow like water over the tangled roots and fallen leaves, its every movement a thing of hypnotic beauty. It had a natural affinity for the plants around it, but not in the way that one might expect. Rather than using them for sustenance, the creature communed with them, drawing strength from the very life force that pulsed through their veins.
> And so it was that the creature of un-mythical origins, born from the very essence of the earth itself, came to be known by a name whispered on the lips of those who had glimpsed its rare presence - Nightshade.
🐉 Delve into mythology and create a brand new creature that doesn't exist in legends or myths. Give these creatures of non-legend a new lease of life! 🦞
**Challenges:**
* At least one feature inspired by a real-life animal or plant.
* Incorporate an element of the creature's habitat into its design.
* Give your creature a name.
*What's the next entry in the Field Guide?*
## Model description
This is a model related to the "Challenge of the WeekEnd" contest on [Stable Diffusion discord](https://discord.gg/stablediffusion).
I try to make a model out of all the submission for people to continue enjoy the theme after the even, and see a little of their designs in other people's creations. The token stays "SDArt" and I balance the learning on the low side, so that it doesn't just replicate creations.
The total dataset is made of 31 pictures. It was trained on [Stable diffusion 1.5](https://huggingface.co/runwayml/stable-diffusion-v1-5). I used [EveryDream](https://github.com/victorchall/EveryDream2trainer) to do the training, 100 total repeat per picture. The pictures were tagged using the token "SDArt", and an arbitrary token I choose. The dataset is provided below, as well as a list of usernames and their corresponding token.
The recommended sampling is k_Euler_a or DPM++ 2M Karras on 20 steps, CFGS 7.5 .
[The model is also available here](https://huggingface.co/Guizmus/SDArt_UnmythicalCreatures768) in a version trained on 2.1 as a base.
## Trained tokens
* SDArt
* rcn
* bnp
* reba
* aten
* cous
* aved
* omd
* ront
* kuro
* asot
* psst
* rolf
* muc
* kts
* utm
* vaw
* mss
* guin
* crit
* mlas
* isch
* phol
* dds
* acu
* pte
* rean
* soca
* sohn
* sown
* ssh
* stix
## Download links
[SafeTensors](https://huggingface.co/Guizmus/SDArt_UnmythicalCreatures/resolve/main/SDArt_UnmythicalCreatures.safetensors)
[Dataset](https://huggingface.co/Guizmus/SDArt_UnmythicalCreatures/resolve/main/dataset.zip)
## 🧨 Diffusers
This model can be used just like any other Stable Diffusion model. For more information,
please have a look at the [Stable Diffusion](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion).
You can also export the model to [ONNX](https://huggingface.co/docs/diffusers/optimization/onnx), [MPS](https://huggingface.co/docs/diffusers/optimization/mps) and/or [FLAX/JAX]().
```python
from diffusers import StableDiffusionPipeline
import torch
model_id = "Guizmus/SDArt_UnmythicalCreatures"
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
pipe = pipe.to("cuda")
prompt = "SDArt rolf"
image = pipe(prompt).images[0]
image.save("./SDArt.png")
```
|
BatuhanYilmaz/bert-finetuned-nerxD
|
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| 0 | null |
---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### wp_converter Dreambooth model trained by oussama120 with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Sample pictures of this concept:
|
BatuhanYilmaz/code-search-net-tokenizer1
|
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| 0 | null |
---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
datasets:
- cord-layoutlmv3
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-finetuned-cord_100
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: cord-layoutlmv3
type: cord-layoutlmv3
config: cord
split: test
args: cord
metrics:
- name: Precision
type: precision
value: 0.4079861111111111
- name: Recall
type: recall
value: 0.5276946107784432
- name: F1
type: f1
value: 0.46018276762402094
- name: Accuracy
type: accuracy
value: 0.40025466893039047
---
<!-- 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. -->
# layoutlmv3-finetuned-cord_100
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the cord-layoutlmv3 dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8382
- Precision: 0.4080
- Recall: 0.5277
- F1: 0.4602
- Accuracy: 0.4003
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 0.33 | 50 | 3.1357 | 0.4186 | 0.5082 | 0.4591 | 0.3688 |
| No log | 0.67 | 100 | 2.8382 | 0.4080 | 0.5277 | 0.4602 | 0.4003 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
BatuhanYilmaz/distilbert-base-uncased-finetuned-squad-d5716d28
|
[
"pytorch",
"distilbert",
"fill-mask",
"en",
"dataset:squad",
"arxiv:1910.01108",
"transformers",
"question-answering",
"license:apache-2.0",
"autotrain_compatible"
] |
question-answering
|
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"DistilBertForMaskedLM"
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}
| 18 | 2023-04-24T09:19:42Z |
---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids
2. Step 1: Find your model_id: MohammedEltoum/ppo-Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
BatuhanYilmaz/dummy
|
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| 0 | null |
---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
datasets:
- funsd
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-finetuned-funsd
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: funsd
type: funsd
config: funsd
split: test
args: funsd
metrics:
- name: Precision
type: precision
value: 0.7998102466793169
- name: Recall
type: recall
value: 0.8375558867362146
- name: F1
type: f1
value: 0.8182479980587235
- name: Accuracy
type: accuracy
value: 0.826102460477832
---
<!-- 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. -->
# layoutlmv3-finetuned-funsd
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the funsd dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0068
- Precision: 0.7998
- Recall: 0.8376
- F1: 0.8182
- Accuracy: 0.8261
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 2500
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 3.33 | 250 | 0.5828 | 0.7015 | 0.8033 | 0.7490 | 0.8022 |
| 0.6702 | 6.67 | 500 | 0.5765 | 0.7499 | 0.8073 | 0.7775 | 0.8253 |
| 0.6702 | 10.0 | 750 | 0.7082 | 0.7755 | 0.8236 | 0.7988 | 0.8160 |
| 0.1797 | 13.33 | 1000 | 0.7819 | 0.7807 | 0.8366 | 0.8077 | 0.8256 |
| 0.1797 | 16.67 | 1250 | 0.8199 | 0.7997 | 0.8311 | 0.8151 | 0.8227 |
| 0.0745 | 20.0 | 1500 | 0.9025 | 0.7943 | 0.8286 | 0.8111 | 0.8231 |
| 0.0745 | 23.33 | 1750 | 0.9159 | 0.7941 | 0.8470 | 0.8197 | 0.8248 |
| 0.041 | 26.67 | 2000 | 1.0012 | 0.7989 | 0.8385 | 0.8182 | 0.8210 |
| 0.041 | 30.0 | 2250 | 0.9852 | 0.8024 | 0.8450 | 0.8231 | 0.8301 |
| 0.0246 | 33.33 | 2500 | 1.0068 | 0.7998 | 0.8376 | 0.8182 | 0.8261 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
BeIR/query-gen-msmarco-t5-large-v1
|
[
"pytorch",
"jax",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
{
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"T5ForConditionalGeneration"
],
"model_type": "t5",
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"translation_en_to_de": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to German: "
},
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"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to French: "
},
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"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to Romanian: "
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| 1,225 | null |
---
pipeline_tag: text-classification
widget:
- text: "the weather is hot"
- text: "I love summer"
---
|
Beatriz/model_name
|
[] | null |
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| 0 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: T5_large6_512_128
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_large6_512_128
This model is a fine-tuned version of [LucasThil/T5_large5_512_128](https://huggingface.co/LucasThil/T5_large5_512_128) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1120
- Rouge1: 0.8656
- Rouge2: 0.7421
- Rougel: 0.8655
- Rougelsum: 0.8655
- Gen Len: 9.2037
## 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-07
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 0.1215 | 1.0 | 6786 | 0.1120 | 0.8658 | 0.7424 | 0.8657 | 0.8657 | 9.2036 |
| 0.1263 | 2.0 | 13572 | 0.1120 | 0.8656 | 0.7421 | 0.8655 | 0.8655 | 9.2037 |
### Framework versions
- Transformers 4.28.1
- Pytorch 1.13.1
- Datasets 2.11.0
- Tokenizers 0.13.3
|
Begimay/Task
|
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}
}
| 0 | null |
---
pipeline_tag: text-generation
widget:
- text: "last year I went to Canada"
- text: "the weather is hot"
---
|
Benicio/t5-small-finetuned-en-to-ro
|
[] | null |
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| 0 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: t5-small-finetuned-topics-customdata2
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-small-finetuned-topics-customdata2
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:
- Loss: 2.8050
- Rouge1: 32.4786
- Rouge2: 24.1587
- Rougel: 31.9438
- Rougelsum: 31.8923
- Gen Len: 10.6455
## 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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 2.7203 | 1.0 | 1000 | 2.8050 | 32.4786 | 24.1587 | 31.9438 | 31.8923 | 10.6455 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
Betaniaolivo/Foto
|
[] | null |
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}
| 0 | null |
---
widget:
- text: "I love football so much"
example_title: "Positive Mostafa example 1"
- text: "I don't really like this type of food"
example_title: "Negative Mostafa example 2"
---
|
Bharathdamu/wav2vec2-model-hindi-stt
|
[] | 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: 0.926
- name: F1
type: f1
value: 0.9259175826084659
---
<!-- 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: 0.2129
- Accuracy: 0.926
- F1: 0.9259
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.7946 | 1.0 | 250 | 0.3013 | 0.905 | 0.9019 |
| 0.2432 | 2.0 | 500 | 0.2129 | 0.926 | 0.9259 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
BigSalmon/FormalRobertaa
|
[
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible",
"has_space"
] |
fill-mask
|
{
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
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},
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}
| 5 | null |
---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: AntBulletEnv-v0
type: AntBulletEnv-v0
metrics:
- type: mean_reward
value: 1925.43 +/- 108.55
name: mean_reward
verified: false
---
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
BigSalmon/FormalRobertaaa
|
[
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
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"RobertaForMaskedLM"
],
"model_type": "roberta",
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},
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},
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}
}
| 12 | null |
---
license: mit
---
This repo contains a low-rank adapter (LoRA) for LLaMA-7b
fit on the [Stanford-Alpaca-52k](https://github.com/tatsu-lab/stanford_alpaca)
and [databricks-dolly-15k](https://github.com/databrickslabs/dolly/tree/master/data) data in Afrikaans.
### Dataset Creation
1. English Instructions: The English instuctions are obtained from [alpaca-52k](https://github.com/tatsu-lab/stanford_alpaca), and [dolly-15k](https://github.com/databrickslabs/dolly/tree/master/data).
2. Instruction Translation: The instructions (and inputs) are translated into the target languages using Google Translation API (conducted on April 2023).
3. Output Generation: We generate output from `gpt-3.5-turbo` for each language (conducted on April 2023).
<h3 align="center">
<img src="https://raw.githubusercontent.com/fajri91/eval_picts/master/BactrianX_dataset.jpg" width="950" align="center">
</h3>
### Training Parameters
The code for training the model is provided in our [github](https://github.com/mbzuai-nlp/Bactrian-X), which is adapted from [Alpaca-LoRA](https://github.com/tloen/alpaca-lora).
This version of the weights was trained with the following hyperparameters:
- Epochs: 5
- Batch size: 128
- Cutoff length: 512
- Learning rate: 3e-4
- Lora _r_: 16
- Lora target modules: q_proj, k_proj, v_proj, o_proj
That is:
```
python finetune.py \
--base_model='decapoda-research/llama-7b-hf' \
--num_epochs=5 \
--cutoff_len=512 \
--group_by_length \
--output_dir='./bactrian-af-7b-lora' \
--lora_target_modules='[q_proj,k_proj,v_proj,o_proj]' \
--lora_r=16 \
--micro_batch_size=32
```
Instructions for running it can be found at https://github.com/MBZUAI-nlp/Bactrian-X.
### Discussion of Biases
(1) Translation bias; (2) Potential English-culture bias in the translated dataset.
### Citation Information
```
@misc{bactrian,
author = {Haonan Li and Fajri Koto and Timothy Baldwin},
title = {Bactrian-X: A Multilingual Replicable Instruction-Following Model},
year = {2023},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/MBZUAI-nlp/Bactrian-X}},
}
```
|
BigSalmon/GPTHeHe
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"has_space"
] |
text-generation
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
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}
| 8 | null |
---
license: mit
---
This repo contains a low-rank adapter (LoRA) for LLaMA-7b
fit on the [Stanford-Alpaca-52k](https://github.com/tatsu-lab/stanford_alpaca)
and [databricks-dolly-15k](https://github.com/databrickslabs/dolly/tree/master/data) data in Bengali.
### Dataset Creation
1. English Instructions: The English instuctions are obtained from [alpaca-52k](https://github.com/tatsu-lab/stanford_alpaca), and [dolly-15k](https://github.com/databrickslabs/dolly/tree/master/data).
2. Instruction Translation: The instructions (and inputs) are translated into the target languages using Google Translation API (conducted on April 2023).
3. Output Generation: We generate output from `gpt-3.5-turbo` for each language (conducted on April 2023).
<h3 align="center">
<img src="https://raw.githubusercontent.com/fajri91/eval_picts/master/BactrianX_dataset.jpg" width="950" align="center">
</h3>
### Training Parameters
The code for training the model is provided in our [github](https://github.com/mbzuai-nlp/Bactrian-X), which is adapted from [Alpaca-LoRA](https://github.com/tloen/alpaca-lora).
This version of the weights was trained with the following hyperparameters:
- Epochs: 5
- Batch size: 128
- Cutoff length: 512
- Learning rate: 3e-4
- Lora _r_: 16
- Lora target modules: q_proj, k_proj, v_proj, o_proj
That is:
```
python finetune.py \
--base_model='decapoda-research/llama-7b-hf' \
--num_epochs=5 \
--cutoff_len=512 \
--group_by_length \
--output_dir='./bactrian-bn-7b-lora' \
--lora_target_modules='[q_proj,k_proj,v_proj,o_proj]' \
--lora_r=16 \
--micro_batch_size=32
```
Instructions for running it can be found at https://github.com/MBZUAI-nlp/Bactrian-X.
### Discussion of Biases
(1) Translation bias; (2) Potential English-culture bias in the translated dataset.
### Citation Information
```
@misc{bactrian,
author = {Haonan Li and Fajri Koto and Timothy Baldwin},
title = {Bactrian-X: A Multilingual Replicable Instruction-Following Model},
year = {2023},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/MBZUAI-nlp/Bactrian-X}},
}
```
|
BigSalmon/GPTIntro
|
[] | null |
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}
| 0 | null |
---
license: mit
---
This repo contains a low-rank adapter (LoRA) for LLaMA-7b
fit on the [Stanford-Alpaca-52k](https://github.com/tatsu-lab/stanford_alpaca)
and [databricks-dolly-15k](https://github.com/databrickslabs/dolly/tree/master/data) data in Czech.
### Dataset Creation
1. English Instructions: The English instuctions are obtained from [alpaca-52k](https://github.com/tatsu-lab/stanford_alpaca), and [dolly-15k](https://github.com/databrickslabs/dolly/tree/master/data).
2. Instruction Translation: The instructions (and inputs) are translated into the target languages using Google Translation API (conducted on April 2023).
3. Output Generation: We generate output from `gpt-3.5-turbo` for each language (conducted on April 2023).
<h3 align="center">
<img src="https://raw.githubusercontent.com/fajri91/eval_picts/master/BactrianX_dataset.jpg" width="950" align="center">
</h3>
### Training Parameters
The code for training the model is provided in our [github](https://github.com/mbzuai-nlp/Bactrian-X), which is adapted from [Alpaca-LoRA](https://github.com/tloen/alpaca-lora).
This version of the weights was trained with the following hyperparameters:
- Epochs: 5
- Batch size: 128
- Cutoff length: 512
- Learning rate: 3e-4
- Lora _r_: 16
- Lora target modules: q_proj, k_proj, v_proj, o_proj
That is:
```
python finetune.py \
--base_model='decapoda-research/llama-7b-hf' \
--num_epochs=5 \
--cutoff_len=512 \
--group_by_length \
--output_dir='./bactrian-cs-7b-lora' \
--lora_target_modules='[q_proj,k_proj,v_proj,o_proj]' \
--lora_r=16 \
--micro_batch_size=32
```
Instructions for running it can be found at https://github.com/MBZUAI-nlp/Bactrian-X.
### Discussion of Biases
(1) Translation bias; (2) Potential English-culture bias in the translated dataset.
### Citation Information
```
@misc{bactrian,
author = {Haonan Li and Fajri Koto and Timothy Baldwin},
title = {Bactrian-X: A Multilingual Replicable Instruction-Following Model},
year = {2023},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/MBZUAI-nlp/Bactrian-X}},
}
```
|
BigSalmon/GPTNeo350MInformalToFormalLincoln2
|
[
"pytorch",
"gpt_neo",
"text-generation",
"transformers",
"has_space"
] |
text-generation
|
{
"architectures": [
"GPTNeoForCausalLM"
],
"model_type": "gpt_neo",
"task_specific_params": {
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},
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},
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},
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},
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}
| 8 | null |
---
license: mit
---
This repo contains a low-rank adapter (LoRA) for LLaMA-7b
fit on the [Stanford-Alpaca-52k](https://github.com/tatsu-lab/stanford_alpaca)
and [databricks-dolly-15k](https://github.com/databrickslabs/dolly/tree/master/data) data in Spanish.
### Dataset Creation
1. English Instructions: The English instuctions are obtained from [alpaca-52k](https://github.com/tatsu-lab/stanford_alpaca), and [dolly-15k](https://github.com/databrickslabs/dolly/tree/master/data).
2. Instruction Translation: The instructions (and inputs) are translated into the target languages using Google Translation API (conducted on April 2023).
3. Output Generation: We generate output from `gpt-3.5-turbo` for each language (conducted on April 2023).
<h3 align="center">
<img src="https://raw.githubusercontent.com/fajri91/eval_picts/master/BactrianX_dataset.jpg" width="950" align="center">
</h3>
### Training Parameters
The code for training the model is provided in our [github](https://github.com/mbzuai-nlp/Bactrian-X), which is adapted from [Alpaca-LoRA](https://github.com/tloen/alpaca-lora).
This version of the weights was trained with the following hyperparameters:
- Epochs: 5
- Batch size: 128
- Cutoff length: 512
- Learning rate: 3e-4
- Lora _r_: 16
- Lora target modules: q_proj, k_proj, v_proj, o_proj
That is:
```
python finetune.py \
--base_model='decapoda-research/llama-7b-hf' \
--num_epochs=5 \
--cutoff_len=512 \
--group_by_length \
--output_dir='./bactrian-es-7b-lora' \
--lora_target_modules='[q_proj,k_proj,v_proj,o_proj]' \
--lora_r=16 \
--micro_batch_size=32
```
Instructions for running it can be found at https://github.com/MBZUAI-nlp/Bactrian-X.
### Discussion of Biases
(1) Translation bias; (2) Potential English-culture bias in the translated dataset.
### Citation Information
```
@misc{bactrian,
author = {Haonan Li and Fajri Koto and Timothy Baldwin},
title = {Bactrian-X: A Multilingual Replicable Instruction-Following Model},
year = {2023},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/MBZUAI-nlp/Bactrian-X}},
}
```
|
BigSalmon/GPTNeo350MInformalToFormalLincoln3
|
[
"pytorch",
"gpt_neo",
"text-generation",
"transformers",
"has_space"
] |
text-generation
|
{
"architectures": [
"GPTNeoForCausalLM"
],
"model_type": "gpt_neo",
"task_specific_params": {
"conversational": {
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},
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},
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}
}
| 10 | null |
---
license: mit
---
This repo contains a low-rank adapter (LoRA) for LLaMA-7b
fit on the [Stanford-Alpaca-52k](https://github.com/tatsu-lab/stanford_alpaca)
and [databricks-dolly-15k](https://github.com/databrickslabs/dolly/tree/master/data) data in Estonian.
### Dataset Creation
1. English Instructions: The English instuctions are obtained from [alpaca-52k](https://github.com/tatsu-lab/stanford_alpaca), and [dolly-15k](https://github.com/databrickslabs/dolly/tree/master/data).
2. Instruction Translation: The instructions (and inputs) are translated into the target languages using Google Translation API (conducted on April 2023).
3. Output Generation: We generate output from `gpt-3.5-turbo` for each language (conducted on April 2023).
<h3 align="center">
<img src="https://raw.githubusercontent.com/fajri91/eval_picts/master/BactrianX_dataset.jpg" width="950" align="center">
</h3>
### Training Parameters
The code for training the model is provided in our [github](https://github.com/mbzuai-nlp/Bactrian-X), which is adapted from [Alpaca-LoRA](https://github.com/tloen/alpaca-lora).
This version of the weights was trained with the following hyperparameters:
- Epochs: 5
- Batch size: 128
- Cutoff length: 512
- Learning rate: 3e-4
- Lora _r_: 16
- Lora target modules: q_proj, k_proj, v_proj, o_proj
That is:
```
python finetune.py \
--base_model='decapoda-research/llama-7b-hf' \
--num_epochs=5 \
--cutoff_len=512 \
--group_by_length \
--output_dir='./bactrian-et-7b-lora' \
--lora_target_modules='[q_proj,k_proj,v_proj,o_proj]' \
--lora_r=16 \
--micro_batch_size=32
```
Instructions for running it can be found at https://github.com/MBZUAI-nlp/Bactrian-X.
### Discussion of Biases
(1) Translation bias; (2) Potential English-culture bias in the translated dataset.
### Citation Information
```
@misc{bactrian,
author = {Haonan Li and Fajri Koto and Timothy Baldwin},
title = {Bactrian-X: A Multilingual Replicable Instruction-Following Model},
year = {2023},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/MBZUAI-nlp/Bactrian-X}},
}
```
|
BigSalmon/InfillFormalLincoln
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"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": true,
"max_length": 50
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 8 | null |
---
license: mit
---
This repo contains a low-rank adapter (LoRA) for LLaMA-7b
fit on the [Stanford-Alpaca-52k](https://github.com/tatsu-lab/stanford_alpaca)
and [databricks-dolly-15k](https://github.com/databrickslabs/dolly/tree/master/data) data in Hebrew.
### Dataset Creation
1. English Instructions: The English instuctions are obtained from [alpaca-52k](https://github.com/tatsu-lab/stanford_alpaca), and [dolly-15k](https://github.com/databrickslabs/dolly/tree/master/data).
2. Instruction Translation: The instructions (and inputs) are translated into the target languages using Google Translation API (conducted on April 2023).
3. Output Generation: We generate output from `gpt-3.5-turbo` for each language (conducted on April 2023).
<h3 align="center">
<img src="https://raw.githubusercontent.com/fajri91/eval_picts/master/BactrianX_dataset.jpg" width="950" align="center">
</h3>
### Training Parameters
The code for training the model is provided in our [github](https://github.com/mbzuai-nlp/Bactrian-X), which is adapted from [Alpaca-LoRA](https://github.com/tloen/alpaca-lora).
This version of the weights was trained with the following hyperparameters:
- Epochs: 5
- Batch size: 128
- Cutoff length: 512
- Learning rate: 3e-4
- Lora _r_: 16
- Lora target modules: q_proj, k_proj, v_proj, o_proj
That is:
```
python finetune.py \
--base_model='decapoda-research/llama-7b-hf' \
--num_epochs=5 \
--cutoff_len=512 \
--group_by_length \
--output_dir='./bactrian-he-7b-lora' \
--lora_target_modules='[q_proj,k_proj,v_proj,o_proj]' \
--lora_r=16 \
--micro_batch_size=32
```
Instructions for running it can be found at https://github.com/MBZUAI-nlp/Bactrian-X.
### Discussion of Biases
(1) Translation bias; (2) Potential English-culture bias in the translated dataset.
### Citation Information
```
@misc{bactrian,
author = {Haonan Li and Fajri Koto and Timothy Baldwin},
title = {Bactrian-X: A Multilingual Replicable Instruction-Following Model},
year = {2023},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/MBZUAI-nlp/Bactrian-X}},
}
```
|
BigSalmon/InformalToFormalLincoln14
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"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": true,
"max_length": 50
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 5 | null |
---
license: mit
---
This repo contains a low-rank adapter (LoRA) for LLaMA-7b
fit on the [Stanford-Alpaca-52k](https://github.com/tatsu-lab/stanford_alpaca)
and [databricks-dolly-15k](https://github.com/databrickslabs/dolly/tree/master/data) data in Hindi.
### Dataset Creation
1. English Instructions: The English instuctions are obtained from [alpaca-52k](https://github.com/tatsu-lab/stanford_alpaca), and [dolly-15k](https://github.com/databrickslabs/dolly/tree/master/data).
2. Instruction Translation: The instructions (and inputs) are translated into the target languages using Google Translation API (conducted on April 2023).
3. Output Generation: We generate output from `gpt-3.5-turbo` for each language (conducted on April 2023).
<h3 align="center">
<img src="https://raw.githubusercontent.com/fajri91/eval_picts/master/BactrianX_dataset.jpg" width="950" align="center">
</h3>
### Training Parameters
The code for training the model is provided in our [github](https://github.com/mbzuai-nlp/Bactrian-X), which is adapted from [Alpaca-LoRA](https://github.com/tloen/alpaca-lora).
This version of the weights was trained with the following hyperparameters:
- Epochs: 5
- Batch size: 128
- Cutoff length: 512
- Learning rate: 3e-4
- Lora _r_: 16
- Lora target modules: q_proj, k_proj, v_proj, o_proj
That is:
```
python finetune.py \
--base_model='decapoda-research/llama-7b-hf' \
--num_epochs=5 \
--cutoff_len=512 \
--group_by_length \
--output_dir='./bactrian-hi-7b-lora' \
--lora_target_modules='[q_proj,k_proj,v_proj,o_proj]' \
--lora_r=16 \
--micro_batch_size=32
```
Instructions for running it can be found at https://github.com/MBZUAI-nlp/Bactrian-X.
### Discussion of Biases
(1) Translation bias; (2) Potential English-culture bias in the translated dataset.
### Citation Information
```
@misc{bactrian,
author = {Haonan Li and Fajri Koto and Timothy Baldwin},
title = {Bactrian-X: A Multilingual Replicable Instruction-Following Model},
year = {2023},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/MBZUAI-nlp/Bactrian-X}},
}
```
|
BigSalmon/InformalToFormalLincoln15
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"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": true,
"max_length": 50
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 11 | null |
---
license: mit
---
This repo contains a low-rank adapter (LoRA) for LLaMA-7b
fit on the [Stanford-Alpaca-52k](https://github.com/tatsu-lab/stanford_alpaca)
and [databricks-dolly-15k](https://github.com/databrickslabs/dolly/tree/master/data) data in Croatian.
### Dataset Creation
1. English Instructions: The English instuctions are obtained from [alpaca-52k](https://github.com/tatsu-lab/stanford_alpaca), and [dolly-15k](https://github.com/databrickslabs/dolly/tree/master/data).
2. Instruction Translation: The instructions (and inputs) are translated into the target languages using Google Translation API (conducted on April 2023).
3. Output Generation: We generate output from `gpt-3.5-turbo` for each language (conducted on April 2023).
<h3 align="center">
<img src="https://raw.githubusercontent.com/fajri91/eval_picts/master/BactrianX_dataset.jpg" width="950" align="center">
</h3>
### Training Parameters
The code for training the model is provided in our [github](https://github.com/mbzuai-nlp/Bactrian-X), which is adapted from [Alpaca-LoRA](https://github.com/tloen/alpaca-lora).
This version of the weights was trained with the following hyperparameters:
- Epochs: 5
- Batch size: 128
- Cutoff length: 512
- Learning rate: 3e-4
- Lora _r_: 16
- Lora target modules: q_proj, k_proj, v_proj, o_proj
That is:
```
python finetune.py \
--base_model='decapoda-research/llama-7b-hf' \
--num_epochs=5 \
--cutoff_len=512 \
--group_by_length \
--output_dir='./bactrian-hr-7b-lora' \
--lora_target_modules='[q_proj,k_proj,v_proj,o_proj]' \
--lora_r=16 \
--micro_batch_size=32
```
Instructions for running it can be found at https://github.com/MBZUAI-nlp/Bactrian-X.
### Discussion of Biases
(1) Translation bias; (2) Potential English-culture bias in the translated dataset.
### Citation Information
```
@misc{bactrian,
author = {Haonan Li and Fajri Koto and Timothy Baldwin},
title = {Bactrian-X: A Multilingual Replicable Instruction-Following Model},
year = {2023},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/MBZUAI-nlp/Bactrian-X}},
}
```
|
BigSalmon/InformalToFormalLincoln22
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"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": true,
"max_length": 50
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 6 | null |
---
license: mit
---
This repo contains a low-rank adapter (LoRA) for LLaMA-7b
fit on the [Stanford-Alpaca-52k](https://github.com/tatsu-lab/stanford_alpaca)
and [databricks-dolly-15k](https://github.com/databrickslabs/dolly/tree/master/data) data in Khmer.
### Dataset Creation
1. English Instructions: The English instuctions are obtained from [alpaca-52k](https://github.com/tatsu-lab/stanford_alpaca), and [dolly-15k](https://github.com/databrickslabs/dolly/tree/master/data).
2. Instruction Translation: The instructions (and inputs) are translated into the target languages using Google Translation API (conducted on April 2023).
3. Output Generation: We generate output from `gpt-3.5-turbo` for each language (conducted on April 2023).
<h3 align="center">
<img src="https://raw.githubusercontent.com/fajri91/eval_picts/master/BactrianX_dataset.jpg" width="950" align="center">
</h3>
### Training Parameters
The code for training the model is provided in our [github](https://github.com/mbzuai-nlp/Bactrian-X), which is adapted from [Alpaca-LoRA](https://github.com/tloen/alpaca-lora).
This version of the weights was trained with the following hyperparameters:
- Epochs: 5
- Batch size: 128
- Cutoff length: 512
- Learning rate: 3e-4
- Lora _r_: 16
- Lora target modules: q_proj, k_proj, v_proj, o_proj
That is:
```
python finetune.py \
--base_model='decapoda-research/llama-7b-hf' \
--num_epochs=5 \
--cutoff_len=512 \
--group_by_length \
--output_dir='./bactrian-km-7b-lora' \
--lora_target_modules='[q_proj,k_proj,v_proj,o_proj]' \
--lora_r=16 \
--micro_batch_size=32
```
Instructions for running it can be found at https://github.com/MBZUAI-nlp/Bactrian-X.
### Discussion of Biases
(1) Translation bias; (2) Potential English-culture bias in the translated dataset.
### Citation Information
```
@misc{bactrian,
author = {Haonan Li and Fajri Koto and Timothy Baldwin},
title = {Bactrian-X: A Multilingual Replicable Instruction-Following Model},
year = {2023},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/MBZUAI-nlp/Bactrian-X}},
}
```
|
BigSalmon/InformalToFormalLincoln23
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"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": true,
"max_length": 50
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 5 | null |
---
license: mit
---
This repo contains a low-rank adapter (LoRA) for LLaMA-7b
fit on the [Stanford-Alpaca-52k](https://github.com/tatsu-lab/stanford_alpaca)
and [databricks-dolly-15k](https://github.com/databrickslabs/dolly/tree/master/data) data in Korean.
### Dataset Creation
1. English Instructions: The English instuctions are obtained from [alpaca-52k](https://github.com/tatsu-lab/stanford_alpaca), and [dolly-15k](https://github.com/databrickslabs/dolly/tree/master/data).
2. Instruction Translation: The instructions (and inputs) are translated into the target languages using Google Translation API (conducted on April 2023).
3. Output Generation: We generate output from `gpt-3.5-turbo` for each language (conducted on April 2023).
<h3 align="center">
<img src="https://raw.githubusercontent.com/fajri91/eval_picts/master/BactrianX_dataset.jpg" width="950" align="center">
</h3>
### Training Parameters
The code for training the model is provided in our [github](https://github.com/mbzuai-nlp/Bactrian-X), which is adapted from [Alpaca-LoRA](https://github.com/tloen/alpaca-lora).
This version of the weights was trained with the following hyperparameters:
- Epochs: 5
- Batch size: 128
- Cutoff length: 512
- Learning rate: 3e-4
- Lora _r_: 16
- Lora target modules: q_proj, k_proj, v_proj, o_proj
That is:
```
python finetune.py \
--base_model='decapoda-research/llama-7b-hf' \
--num_epochs=5 \
--cutoff_len=512 \
--group_by_length \
--output_dir='./bactrian-ko-7b-lora' \
--lora_target_modules='[q_proj,k_proj,v_proj,o_proj]' \
--lora_r=16 \
--micro_batch_size=32
```
Instructions for running it can be found at https://github.com/MBZUAI-nlp/Bactrian-X.
### Discussion of Biases
(1) Translation bias; (2) Potential English-culture bias in the translated dataset.
### Citation Information
```
@misc{bactrian,
author = {Haonan Li and Fajri Koto and Timothy Baldwin},
title = {Bactrian-X: A Multilingual Replicable Instruction-Following Model},
year = {2023},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/MBZUAI-nlp/Bactrian-X}},
}
```
|
BigSalmon/InformalToFormalLincoln25
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"has_space"
] |
text-generation
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"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": true,
"max_length": 50
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 10 | null |
---
license: mit
---
This repo contains a low-rank adapter (LoRA) for LLaMA-7b
fit on the [Stanford-Alpaca-52k](https://github.com/tatsu-lab/stanford_alpaca)
and [databricks-dolly-15k](https://github.com/databrickslabs/dolly/tree/master/data) data in Latvian.
### Dataset Creation
1. English Instructions: The English instuctions are obtained from [alpaca-52k](https://github.com/tatsu-lab/stanford_alpaca), and [dolly-15k](https://github.com/databrickslabs/dolly/tree/master/data).
2. Instruction Translation: The instructions (and inputs) are translated into the target languages using Google Translation API (conducted on April 2023).
3. Output Generation: We generate output from `gpt-3.5-turbo` for each language (conducted on April 2023).
<h3 align="center">
<img src="https://raw.githubusercontent.com/fajri91/eval_picts/master/BactrianX_dataset.jpg" width="950" align="center">
</h3>
### Training Parameters
The code for training the model is provided in our [github](https://github.com/mbzuai-nlp/Bactrian-X), which is adapted from [Alpaca-LoRA](https://github.com/tloen/alpaca-lora).
This version of the weights was trained with the following hyperparameters:
- Epochs: 5
- Batch size: 128
- Cutoff length: 512
- Learning rate: 3e-4
- Lora _r_: 16
- Lora target modules: q_proj, k_proj, v_proj, o_proj
That is:
```
python finetune.py \
--base_model='decapoda-research/llama-7b-hf' \
--num_epochs=5 \
--cutoff_len=512 \
--group_by_length \
--output_dir='./bactrian-lv-7b-lora' \
--lora_target_modules='[q_proj,k_proj,v_proj,o_proj]' \
--lora_r=16 \
--micro_batch_size=32
```
Instructions for running it can be found at https://github.com/MBZUAI-nlp/Bactrian-X.
### Discussion of Biases
(1) Translation bias; (2) Potential English-culture bias in the translated dataset.
### Citation Information
```
@misc{bactrian,
author = {Haonan Li and Fajri Koto and Timothy Baldwin},
title = {Bactrian-X: A Multilingual Replicable Instruction-Following Model},
year = {2023},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/MBZUAI-nlp/Bactrian-X}},
}
```
|
BigSalmon/InformalToFormalLincolnDistilledGPT2
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"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": true,
"max_length": 50
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 7 | null |
---
license: mit
---
This repo contains a low-rank adapter (LoRA) for LLaMA-7b
fit on the [Stanford-Alpaca-52k](https://github.com/tatsu-lab/stanford_alpaca)
and [databricks-dolly-15k](https://github.com/databrickslabs/dolly/tree/master/data) data in Macedonian.
### Dataset Creation
1. English Instructions: The English instuctions are obtained from [alpaca-52k](https://github.com/tatsu-lab/stanford_alpaca), and [dolly-15k](https://github.com/databrickslabs/dolly/tree/master/data).
2. Instruction Translation: The instructions (and inputs) are translated into the target languages using Google Translation API (conducted on April 2023).
3. Output Generation: We generate output from `gpt-3.5-turbo` for each language (conducted on April 2023).
<h3 align="center">
<img src="https://raw.githubusercontent.com/fajri91/eval_picts/master/BactrianX_dataset.jpg" width="950" align="center">
</h3>
### Training Parameters
The code for training the model is provided in our [github](https://github.com/mbzuai-nlp/Bactrian-X), which is adapted from [Alpaca-LoRA](https://github.com/tloen/alpaca-lora).
This version of the weights was trained with the following hyperparameters:
- Epochs: 5
- Batch size: 128
- Cutoff length: 512
- Learning rate: 3e-4
- Lora _r_: 16
- Lora target modules: q_proj, k_proj, v_proj, o_proj
That is:
```
python finetune.py \
--base_model='decapoda-research/llama-7b-hf' \
--num_epochs=5 \
--cutoff_len=512 \
--group_by_length \
--output_dir='./bactrian-mk-7b-lora' \
--lora_target_modules='[q_proj,k_proj,v_proj,o_proj]' \
--lora_r=16 \
--micro_batch_size=32
```
Instructions for running it can be found at https://github.com/MBZUAI-nlp/Bactrian-X.
### Discussion of Biases
(1) Translation bias; (2) Potential English-culture bias in the translated dataset.
### Citation Information
```
@misc{bactrian,
author = {Haonan Li and Fajri Koto and Timothy Baldwin},
title = {Bactrian-X: A Multilingual Replicable Instruction-Following Model},
year = {2023},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/MBZUAI-nlp/Bactrian-X}},
}
```
|
BigSalmon/Lincoln4
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"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": true,
"max_length": 50
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
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"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 11 | null |
---
license: mit
---
This repo contains a low-rank adapter (LoRA) for LLaMA-7b
fit on the [Stanford-Alpaca-52k](https://github.com/tatsu-lab/stanford_alpaca)
and [databricks-dolly-15k](https://github.com/databrickslabs/dolly/tree/master/data) data in Malayalam.
### Dataset Creation
1. English Instructions: The English instuctions are obtained from [alpaca-52k](https://github.com/tatsu-lab/stanford_alpaca), and [dolly-15k](https://github.com/databrickslabs/dolly/tree/master/data).
2. Instruction Translation: The instructions (and inputs) are translated into the target languages using Google Translation API (conducted on April 2023).
3. Output Generation: We generate output from `gpt-3.5-turbo` for each language (conducted on April 2023).
<h3 align="center">
<img src="https://raw.githubusercontent.com/fajri91/eval_picts/master/BactrianX_dataset.jpg" width="950" align="center">
</h3>
### Training Parameters
The code for training the model is provided in our [github](https://github.com/mbzuai-nlp/Bactrian-X), which is adapted from [Alpaca-LoRA](https://github.com/tloen/alpaca-lora).
This version of the weights was trained with the following hyperparameters:
- Epochs: 5
- Batch size: 128
- Cutoff length: 512
- Learning rate: 3e-4
- Lora _r_: 16
- Lora target modules: q_proj, k_proj, v_proj, o_proj
That is:
```
python finetune.py \
--base_model='decapoda-research/llama-7b-hf' \
--num_epochs=5 \
--cutoff_len=512 \
--group_by_length \
--output_dir='./bactrian-ml-7b-lora' \
--lora_target_modules='[q_proj,k_proj,v_proj,o_proj]' \
--lora_r=16 \
--micro_batch_size=32
```
Instructions for running it can be found at https://github.com/MBZUAI-nlp/Bactrian-X.
### Discussion of Biases
(1) Translation bias; (2) Potential English-culture bias in the translated dataset.
### Citation Information
```
@misc{bactrian,
author = {Haonan Li and Fajri Koto and Timothy Baldwin},
title = {Bactrian-X: A Multilingual Replicable Instruction-Following Model},
year = {2023},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/MBZUAI-nlp/Bactrian-X}},
}
```
|
BigSalmon/MrLincoln125MNeo
|
[
"pytorch",
"tensorboard",
"gpt_neo",
"text-generation",
"transformers"
] |
text-generation
|
{
"architectures": [
"GPTNeoForCausalLM"
],
"model_type": "gpt_neo",
"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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"max_length": null,
"num_beams": null,
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},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 12 | null |
---
tags:
- generated_from_trainer
model-index:
- name: finetunedtopicscustomdata4
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. -->
# finetunedtopicscustomdata4
This model is a fine-tuned version of [fabiochiu/t5-base-medium-title-generation](https://huggingface.co/fabiochiu/t5-base-medium-title-generation) 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: 2e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
BigSalmon/MrLincoln13
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"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": true,
"max_length": 50
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 9 | 2023-04-24T10:47:51Z |
---
license: mit
---
This repo contains a low-rank adapter (LoRA) for LLaMA-7b
fit on the [Stanford-Alpaca-52k](https://github.com/tatsu-lab/stanford_alpaca)
and [databricks-dolly-15k](https://github.com/databrickslabs/dolly/tree/master/data) data in Dutch.
### Dataset Creation
1. English Instructions: The English instuctions are obtained from [alpaca-52k](https://github.com/tatsu-lab/stanford_alpaca), and [dolly-15k](https://github.com/databrickslabs/dolly/tree/master/data).
2. Instruction Translation: The instructions (and inputs) are translated into the target languages using Google Translation API (conducted on April 2023).
3. Output Generation: We generate output from `gpt-3.5-turbo` for each language (conducted on April 2023).
<h3 align="center">
<img src="https://raw.githubusercontent.com/fajri91/eval_picts/master/BactrianX_dataset.jpg" width="950" align="center">
</h3>
### Training Parameters
The code for training the model is provided in our [github](https://github.com/mbzuai-nlp/Bactrian-X), which is adapted from [Alpaca-LoRA](https://github.com/tloen/alpaca-lora).
This version of the weights was trained with the following hyperparameters:
- Epochs: 5
- Batch size: 128
- Cutoff length: 512
- Learning rate: 3e-4
- Lora _r_: 16
- Lora target modules: q_proj, k_proj, v_proj, o_proj
That is:
```
python finetune.py \
--base_model='decapoda-research/llama-7b-hf' \
--num_epochs=5 \
--cutoff_len=512 \
--group_by_length \
--output_dir='./bactrian-nl-7b-lora' \
--lora_target_modules='[q_proj,k_proj,v_proj,o_proj]' \
--lora_r=16 \
--micro_batch_size=32
```
Instructions for running it can be found at https://github.com/MBZUAI-nlp/Bactrian-X.
### Discussion of Biases
(1) Translation bias; (2) Potential English-culture bias in the translated dataset.
### Citation Information
```
@misc{bactrian,
author = {Haonan Li and Fajri Koto and Timothy Baldwin},
title = {Bactrian-X: A Multilingual Replicable Instruction-Following Model},
year = {2023},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/MBZUAI-nlp/Bactrian-X}},
}
```
|
BigSalmon/MrLincoln2
|
[
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"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": true,
"max_length": 50
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 9 | null |
---
license: mit
---
This repo contains a low-rank adapter (LoRA) for LLaMA-7b
fit on the [Stanford-Alpaca-52k](https://github.com/tatsu-lab/stanford_alpaca)
and [databricks-dolly-15k](https://github.com/databrickslabs/dolly/tree/master/data) data in Pashto.
### Dataset Creation
1. English Instructions: The English instuctions are obtained from [alpaca-52k](https://github.com/tatsu-lab/stanford_alpaca), and [dolly-15k](https://github.com/databrickslabs/dolly/tree/master/data).
2. Instruction Translation: The instructions (and inputs) are translated into the target languages using Google Translation API (conducted on April 2023).
3. Output Generation: We generate output from `gpt-3.5-turbo` for each language (conducted on April 2023).
<h3 align="center">
<img src="https://raw.githubusercontent.com/fajri91/eval_picts/master/BactrianX_dataset.jpg" width="950" align="center">
</h3>
### Training Parameters
The code for training the model is provided in our [github](https://github.com/mbzuai-nlp/Bactrian-X), which is adapted from [Alpaca-LoRA](https://github.com/tloen/alpaca-lora).
This version of the weights was trained with the following hyperparameters:
- Epochs: 5
- Batch size: 128
- Cutoff length: 512
- Learning rate: 3e-4
- Lora _r_: 16
- Lora target modules: q_proj, k_proj, v_proj, o_proj
That is:
```
python finetune.py \
--base_model='decapoda-research/llama-7b-hf' \
--num_epochs=5 \
--cutoff_len=512 \
--group_by_length \
--output_dir='./bactrian-ps-7b-lora' \
--lora_target_modules='[q_proj,k_proj,v_proj,o_proj]' \
--lora_r=16 \
--micro_batch_size=32
```
Instructions for running it can be found at https://github.com/MBZUAI-nlp/Bactrian-X.
### Discussion of Biases
(1) Translation bias; (2) Potential English-culture bias in the translated dataset.
### Citation Information
```
@misc{bactrian,
author = {Haonan Li and Fajri Koto and Timothy Baldwin},
title = {Bactrian-X: A Multilingual Replicable Instruction-Following Model},
year = {2023},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/MBZUAI-nlp/Bactrian-X}},
}
```
|
BigSalmon/MrLincoln8
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"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": true,
"max_length": 50
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 12 | null |
---
license: mit
---
This repo contains a low-rank adapter (LoRA) for LLaMA-7b
fit on the [Stanford-Alpaca-52k](https://github.com/tatsu-lab/stanford_alpaca)
and [databricks-dolly-15k](https://github.com/databrickslabs/dolly/tree/master/data) data in Slovenian.
### Dataset Creation
1. English Instructions: The English instuctions are obtained from [alpaca-52k](https://github.com/tatsu-lab/stanford_alpaca), and [dolly-15k](https://github.com/databrickslabs/dolly/tree/master/data).
2. Instruction Translation: The instructions (and inputs) are translated into the target languages using Google Translation API (conducted on April 2023).
3. Output Generation: We generate output from `gpt-3.5-turbo` for each language (conducted on April 2023).
<h3 align="center">
<img src="https://raw.githubusercontent.com/fajri91/eval_picts/master/BactrianX_dataset.jpg" width="950" align="center">
</h3>
### Training Parameters
The code for training the model is provided in our [github](https://github.com/mbzuai-nlp/Bactrian-X), which is adapted from [Alpaca-LoRA](https://github.com/tloen/alpaca-lora).
This version of the weights was trained with the following hyperparameters:
- Epochs: 5
- Batch size: 128
- Cutoff length: 512
- Learning rate: 3e-4
- Lora _r_: 16
- Lora target modules: q_proj, k_proj, v_proj, o_proj
That is:
```
python finetune.py \
--base_model='decapoda-research/llama-7b-hf' \
--num_epochs=5 \
--cutoff_len=512 \
--group_by_length \
--output_dir='./bactrian-sl-7b-lora' \
--lora_target_modules='[q_proj,k_proj,v_proj,o_proj]' \
--lora_r=16 \
--micro_batch_size=32
```
Instructions for running it can be found at https://github.com/MBZUAI-nlp/Bactrian-X.
### Discussion of Biases
(1) Translation bias; (2) Potential English-culture bias in the translated dataset.
### Citation Information
```
@misc{bactrian,
author = {Haonan Li and Fajri Koto and Timothy Baldwin},
title = {Bactrian-X: A Multilingual Replicable Instruction-Following Model},
year = {2023},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/MBZUAI-nlp/Bactrian-X}},
}
```
|
BigSalmon/PhraseBerta
|
[
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 10 | null |
---
license: mit
---
This repo contains a low-rank adapter (LoRA) for LLaMA-7b
fit on the [Stanford-Alpaca-52k](https://github.com/tatsu-lab/stanford_alpaca)
and [databricks-dolly-15k](https://github.com/databrickslabs/dolly/tree/master/data) data in Tagalog.
### Dataset Creation
1. English Instructions: The English instuctions are obtained from [alpaca-52k](https://github.com/tatsu-lab/stanford_alpaca), and [dolly-15k](https://github.com/databrickslabs/dolly/tree/master/data).
2. Instruction Translation: The instructions (and inputs) are translated into the target languages using Google Translation API (conducted on April 2023).
3. Output Generation: We generate output from `gpt-3.5-turbo` for each language (conducted on April 2023).
<h3 align="center">
<img src="https://raw.githubusercontent.com/fajri91/eval_picts/master/BactrianX_dataset.jpg" width="950" align="center">
</h3>
### Training Parameters
The code for training the model is provided in our [github](https://github.com/mbzuai-nlp/Bactrian-X), which is adapted from [Alpaca-LoRA](https://github.com/tloen/alpaca-lora).
This version of the weights was trained with the following hyperparameters:
- Epochs: 5
- Batch size: 128
- Cutoff length: 512
- Learning rate: 3e-4
- Lora _r_: 16
- Lora target modules: q_proj, k_proj, v_proj, o_proj
That is:
```
python finetune.py \
--base_model='decapoda-research/llama-7b-hf' \
--num_epochs=5 \
--cutoff_len=512 \
--group_by_length \
--output_dir='./bactrian-tl-7b-lora' \
--lora_target_modules='[q_proj,k_proj,v_proj,o_proj]' \
--lora_r=16 \
--micro_batch_size=32
```
Instructions for running it can be found at https://github.com/MBZUAI-nlp/Bactrian-X.
### Discussion of Biases
(1) Translation bias; (2) Potential English-culture bias in the translated dataset.
### Citation Information
```
@misc{bactrian,
author = {Haonan Li and Fajri Koto and Timothy Baldwin},
title = {Bactrian-X: A Multilingual Replicable Instruction-Following Model},
year = {2023},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/MBZUAI-nlp/Bactrian-X}},
}
```
|
BigSalmon/Points
|
[
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"transformers",
"has_space"
] |
text-generation
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"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": true,
"max_length": 50
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 13 | null |
---
license: mit
---
This repo contains a low-rank adapter (LoRA) for LLaMA-7b
fit on the [Stanford-Alpaca-52k](https://github.com/tatsu-lab/stanford_alpaca)
and [databricks-dolly-15k](https://github.com/databrickslabs/dolly/tree/master/data) data in Turkish.
### Dataset Creation
1. English Instructions: The English instuctions are obtained from [alpaca-52k](https://github.com/tatsu-lab/stanford_alpaca), and [dolly-15k](https://github.com/databrickslabs/dolly/tree/master/data).
2. Instruction Translation: The instructions (and inputs) are translated into the target languages using Google Translation API (conducted on April 2023).
3. Output Generation: We generate output from `gpt-3.5-turbo` for each language (conducted on April 2023).
<h3 align="center">
<img src="https://raw.githubusercontent.com/fajri91/eval_picts/master/BactrianX_dataset.jpg" width="950" align="center">
</h3>
### Training Parameters
The code for training the model is provided in our [github](https://github.com/mbzuai-nlp/Bactrian-X), which is adapted from [Alpaca-LoRA](https://github.com/tloen/alpaca-lora).
This version of the weights was trained with the following hyperparameters:
- Epochs: 5
- Batch size: 128
- Cutoff length: 512
- Learning rate: 3e-4
- Lora _r_: 16
- Lora target modules: q_proj, k_proj, v_proj, o_proj
That is:
```
python finetune.py \
--base_model='decapoda-research/llama-7b-hf' \
--num_epochs=5 \
--cutoff_len=512 \
--group_by_length \
--output_dir='./bactrian-tr-7b-lora' \
--lora_target_modules='[q_proj,k_proj,v_proj,o_proj]' \
--lora_r=16 \
--micro_batch_size=32
```
Instructions for running it can be found at https://github.com/MBZUAI-nlp/Bactrian-X.
### Discussion of Biases
(1) Translation bias; (2) Potential English-culture bias in the translated dataset.
### Citation Information
```
@misc{bactrian,
author = {Haonan Li and Fajri Koto and Timothy Baldwin},
title = {Bactrian-X: A Multilingual Replicable Instruction-Following Model},
year = {2023},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/MBZUAI-nlp/Bactrian-X}},
}
```
|
BigSalmon/Points2
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"has_space"
] |
text-generation
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"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": true,
"max_length": 50
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 12 | null |
---
license: mit
---
This repo contains a low-rank adapter (LoRA) for LLaMA-7b
fit on the [Stanford-Alpaca-52k](https://github.com/tatsu-lab/stanford_alpaca)
and [databricks-dolly-15k](https://github.com/databrickslabs/dolly/tree/master/data) data in Ukrainian.
### Dataset Creation
1. English Instructions: The English instuctions are obtained from [alpaca-52k](https://github.com/tatsu-lab/stanford_alpaca), and [dolly-15k](https://github.com/databrickslabs/dolly/tree/master/data).
2. Instruction Translation: The instructions (and inputs) are translated into the target languages using Google Translation API (conducted on April 2023).
3. Output Generation: We generate output from `gpt-3.5-turbo` for each language (conducted on April 2023).
<h3 align="center">
<img src="https://raw.githubusercontent.com/fajri91/eval_picts/master/BactrianX_dataset.jpg" width="950" align="center">
</h3>
### Training Parameters
The code for training the model is provided in our [github](https://github.com/mbzuai-nlp/Bactrian-X), which is adapted from [Alpaca-LoRA](https://github.com/tloen/alpaca-lora).
This version of the weights was trained with the following hyperparameters:
- Epochs: 5
- Batch size: 128
- Cutoff length: 512
- Learning rate: 3e-4
- Lora _r_: 16
- Lora target modules: q_proj, k_proj, v_proj, o_proj
That is:
```
python finetune.py \
--base_model='decapoda-research/llama-7b-hf' \
--num_epochs=5 \
--cutoff_len=512 \
--group_by_length \
--output_dir='./bactrian-uk-7b-lora' \
--lora_target_modules='[q_proj,k_proj,v_proj,o_proj]' \
--lora_r=16 \
--micro_batch_size=32
```
Instructions for running it can be found at https://github.com/MBZUAI-nlp/Bactrian-X.
### Discussion of Biases
(1) Translation bias; (2) Potential English-culture bias in the translated dataset.
### Citation Information
```
@misc{bactrian,
author = {Haonan Li and Fajri Koto and Timothy Baldwin},
title = {Bactrian-X: A Multilingual Replicable Instruction-Following Model},
year = {2023},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/MBZUAI-nlp/Bactrian-X}},
}
```
|
BigSalmon/Robertsy
|
[
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 4 | null |
---
license: mit
---
This repo contains a low-rank adapter (LoRA) for LLaMA-7b
fit on the [Stanford-Alpaca-52k](https://github.com/tatsu-lab/stanford_alpaca)
and [databricks-dolly-15k](https://github.com/databrickslabs/dolly/tree/master/data) data in Urdu.
### Dataset Creation
1. English Instructions: The English instuctions are obtained from [alpaca-52k](https://github.com/tatsu-lab/stanford_alpaca), and [dolly-15k](https://github.com/databrickslabs/dolly/tree/master/data).
2. Instruction Translation: The instructions (and inputs) are translated into the target languages using Google Translation API (conducted on April 2023).
3. Output Generation: We generate output from `gpt-3.5-turbo` for each language (conducted on April 2023).
<h3 align="center">
<img src="https://raw.githubusercontent.com/fajri91/eval_picts/master/BactrianX_dataset.jpg" width="950" align="center">
</h3>
### Training Parameters
The code for training the model is provided in our [github](https://github.com/mbzuai-nlp/Bactrian-X), which is adapted from [Alpaca-LoRA](https://github.com/tloen/alpaca-lora).
This version of the weights was trained with the following hyperparameters:
- Epochs: 5
- Batch size: 128
- Cutoff length: 512
- Learning rate: 3e-4
- Lora _r_: 16
- Lora target modules: q_proj, k_proj, v_proj, o_proj
That is:
```
python finetune.py \
--base_model='decapoda-research/llama-7b-hf' \
--num_epochs=5 \
--cutoff_len=512 \
--group_by_length \
--output_dir='./bactrian-ur-7b-lora' \
--lora_target_modules='[q_proj,k_proj,v_proj,o_proj]' \
--lora_r=16 \
--micro_batch_size=32
```
Instructions for running it can be found at https://github.com/MBZUAI-nlp/Bactrian-X.
### Discussion of Biases
(1) Translation bias; (2) Potential English-culture bias in the translated dataset.
### Citation Information
```
@misc{bactrian,
author = {Haonan Li and Fajri Koto and Timothy Baldwin},
title = {Bactrian-X: A Multilingual Replicable Instruction-Following Model},
year = {2023},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/MBZUAI-nlp/Bactrian-X}},
}
```
|
BigSalmon/Rowerta
|
[
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 4 | null |
---
license: mit
---
This repo contains a low-rank adapter (LoRA) for LLaMA-7b
fit on the [Stanford-Alpaca-52k](https://github.com/tatsu-lab/stanford_alpaca)
and [databricks-dolly-15k](https://github.com/databrickslabs/dolly/tree/master/data) data in Vietnamese.
### Dataset Creation
1. English Instructions: The English instuctions are obtained from [alpaca-52k](https://github.com/tatsu-lab/stanford_alpaca), and [dolly-15k](https://github.com/databrickslabs/dolly/tree/master/data).
2. Instruction Translation: The instructions (and inputs) are translated into the target languages using Google Translation API (conducted on April 2023).
3. Output Generation: We generate output from `gpt-3.5-turbo` for each language (conducted on April 2023).
<h3 align="center">
<img src="https://raw.githubusercontent.com/fajri91/eval_picts/master/BactrianX_dataset.jpg" width="950" align="center">
</h3>
### Training Parameters
The code for training the model is provided in our [github](https://github.com/mbzuai-nlp/Bactrian-X), which is adapted from [Alpaca-LoRA](https://github.com/tloen/alpaca-lora).
This version of the weights was trained with the following hyperparameters:
- Epochs: 5
- Batch size: 128
- Cutoff length: 512
- Learning rate: 3e-4
- Lora _r_: 16
- Lora target modules: q_proj, k_proj, v_proj, o_proj
That is:
```
python finetune.py \
--base_model='decapoda-research/llama-7b-hf' \
--num_epochs=5 \
--cutoff_len=512 \
--group_by_length \
--output_dir='./bactrian-vi-7b-lora' \
--lora_target_modules='[q_proj,k_proj,v_proj,o_proj]' \
--lora_r=16 \
--micro_batch_size=32
```
Instructions for running it can be found at https://github.com/MBZUAI-nlp/Bactrian-X.
### Discussion of Biases
(1) Translation bias; (2) Potential English-culture bias in the translated dataset.
### Citation Information
```
@misc{bactrian,
author = {Haonan Li and Fajri Koto and Timothy Baldwin},
title = {Bactrian-X: A Multilingual Replicable Instruction-Following Model},
year = {2023},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/MBZUAI-nlp/Bactrian-X}},
}
```
|
BigSalmon/SimplifyText
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"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": true,
"max_length": 50
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
}
| 17 | null |
---
license: mit
---
This repo contains a low-rank adapter (LoRA) for LLaMA-7b
fit on the [Stanford-Alpaca-52k](https://github.com/tatsu-lab/stanford_alpaca)
and [databricks-dolly-15k](https://github.com/databrickslabs/dolly/tree/master/data) data in Xhosa.
### Dataset Creation
1. English Instructions: The English instuctions are obtained from [alpaca-52k](https://github.com/tatsu-lab/stanford_alpaca), and [dolly-15k](https://github.com/databrickslabs/dolly/tree/master/data).
2. Instruction Translation: The instructions (and inputs) are translated into the target languages using Google Translation API (conducted on April 2023).
3. Output Generation: We generate output from `gpt-3.5-turbo` for each language (conducted on April 2023).
<h3 align="center">
<img src="https://raw.githubusercontent.com/fajri91/eval_picts/master/BactrianX_dataset.jpg" width="950" align="center">
</h3>
### Training Parameters
The code for training the model is provided in our [github](https://github.com/mbzuai-nlp/Bactrian-X), which is adapted from [Alpaca-LoRA](https://github.com/tloen/alpaca-lora).
This version of the weights was trained with the following hyperparameters:
- Epochs: 5
- Batch size: 128
- Cutoff length: 512
- Learning rate: 3e-4
- Lora _r_: 16
- Lora target modules: q_proj, k_proj, v_proj, o_proj
That is:
```
python finetune.py \
--base_model='decapoda-research/llama-7b-hf' \
--num_epochs=5 \
--cutoff_len=512 \
--group_by_length \
--output_dir='./bactrian-xh-7b-lora' \
--lora_target_modules='[q_proj,k_proj,v_proj,o_proj]' \
--lora_r=16 \
--micro_batch_size=32
```
Instructions for running it can be found at https://github.com/MBZUAI-nlp/Bactrian-X.
### Discussion of Biases
(1) Translation bias; (2) Potential English-culture bias in the translated dataset.
### Citation Information
```
@misc{bactrian,
author = {Haonan Li and Fajri Koto and Timothy Baldwin},
title = {Bactrian-X: A Multilingual Replicable Instruction-Following Model},
year = {2023},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/MBZUAI-nlp/Bactrian-X}},
}
```
|
BigSalmon/T52
|
[
"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: "
}
}
}
| 8 | 2023-04-24T10:50:05Z |
---
license: mit
---
This repo contains a low-rank adapter (LoRA) for LLaMA-7b
fit on the [Stanford-Alpaca-52k](https://github.com/tatsu-lab/stanford_alpaca)
and [databricks-dolly-15k](https://github.com/databrickslabs/dolly/tree/master/data) data in Chinese.
### Dataset Creation
1. English Instructions: The English instuctions are obtained from [alpaca-52k](https://github.com/tatsu-lab/stanford_alpaca), and [dolly-15k](https://github.com/databrickslabs/dolly/tree/master/data).
2. Instruction Translation: The instructions (and inputs) are translated into the target languages using Google Translation API (conducted on April 2023).
3. Output Generation: We generate output from `gpt-3.5-turbo` for each language (conducted on April 2023).
<h3 align="center">
<img src="https://raw.githubusercontent.com/fajri91/eval_picts/master/BactrianX_dataset.jpg" width="950" align="center">
</h3>
### Training Parameters
The code for training the model is provided in our [github](https://github.com/mbzuai-nlp/Bactrian-X), which is adapted from [Alpaca-LoRA](https://github.com/tloen/alpaca-lora).
This version of the weights was trained with the following hyperparameters:
- Epochs: 5
- Batch size: 128
- Cutoff length: 512
- Learning rate: 3e-4
- Lora _r_: 16
- Lora target modules: q_proj, k_proj, v_proj, o_proj
That is:
```
python finetune.py \
--base_model='decapoda-research/llama-7b-hf' \
--num_epochs=5 \
--cutoff_len=512 \
--group_by_length \
--output_dir='./bactrian-zh-7b-lora' \
--lora_target_modules='[q_proj,k_proj,v_proj,o_proj]' \
--lora_r=16 \
--micro_batch_size=32
```
Instructions for running it can be found at https://github.com/MBZUAI-nlp/Bactrian-X.
### Discussion of Biases
(1) Translation bias; (2) Potential English-culture bias in the translated dataset.
### Citation Information
```
@misc{bactrian,
author = {Haonan Li and Fajri Koto and Timothy Baldwin},
title = {Bactrian-X: A Multilingual Replicable Instruction-Following Model},
year = {2023},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/MBZUAI-nlp/Bactrian-X}},
}
```
|
BigSalmon/T5F
|
[
"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: "
}
}
}
| 6 | 2023-04-24T10:50:15Z |
---
license: mit
---
This repo contains a low-rank adapter (LoRA) for LLaMA-7b
fit on the [Stanford-Alpaca-52k](https://github.com/tatsu-lab/stanford_alpaca)
and [databricks-dolly-15k](https://github.com/databrickslabs/dolly/tree/master/data) data in English.
### Dataset Creation
1. English Instructions: The English instuctions are obtained from [alpaca-52k](https://github.com/tatsu-lab/stanford_alpaca), and [dolly-15k](https://github.com/databrickslabs/dolly/tree/master/data).
2. Instruction Translation: The instructions (and inputs) are translated into the target languages using Google Translation API (conducted on April 2023).
3. Output Generation: We generate output from `gpt-3.5-turbo` for each language (conducted on April 2023).
<h3 align="center">
<img src="https://raw.githubusercontent.com/fajri91/eval_picts/master/BactrianX_dataset.jpg" width="950" align="center">
</h3>
### Training Parameters
The code for training the model is provided in our [github](https://github.com/mbzuai-nlp/Bactrian-X), which is adapted from [Alpaca-LoRA](https://github.com/tloen/alpaca-lora).
This version of the weights was trained with the following hyperparameters:
- Epochs: 5
- Batch size: 128
- Cutoff length: 512
- Learning rate: 3e-4
- Lora _r_: 16
- Lora target modules: q_proj, k_proj, v_proj, o_proj
That is:
```
python finetune.py \
--base_model='decapoda-research/llama-7b-hf' \
--num_epochs=5 \
--cutoff_len=512 \
--group_by_length \
--output_dir='./bactrian-en-7b-lora' \
--lora_target_modules='[q_proj,k_proj,v_proj,o_proj]' \
--lora_r=16 \
--micro_batch_size=32
```
Instructions for running it can be found at https://github.com/MBZUAI-nlp/Bactrian-X.
### Discussion of Biases
(1) Translation bias; (2) Potential English-culture bias in the translated dataset.
### Citation Information
```
@misc{bactrian,
author = {Haonan Li and Fajri Koto and Timothy Baldwin},
title = {Bactrian-X: A Multilingual Replicable Instruction-Following Model},
year = {2023},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/MBZUAI-nlp/Bactrian-X}},
}
```
|
BigSalmon/T5Salmon2
|
[
"pytorch",
"jax",
"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: "
}
}
}
| 13 | null |
---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
---
# HasinMDG/distiluse-base-multilingual-cased-IPTC-L1
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("HasinMDG/distiluse-base-multilingual-cased-IPTC-L1")
# 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}
}
```
|
BigSalmon/TS3
|
[
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible",
"has_space"
] |
text2text-generation
|
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| 7 | null |
#### Table of contents
1. [Introduction](#introduction)
2. [Using PhoBERT with `transformers`](#transformers)
- [Installation](#install2)
- [Pre-trained models](#models2)
- [Example usage](#usage2)
3. [Using PhoBERT with `fairseq`](#fairseq)
4. [Notes](#vncorenlp)
# <a name="introduction"></a> PhoBERT: Pre-trained language models for Vietnamese
Pre-trained PhoBERT models are the state-of-the-art language models for Vietnamese ([Pho](https://en.wikipedia.org/wiki/Pho), i.e. "Phở", is a popular food in Vietnam):
- Two PhoBERT versions of "base" and "large" are the first public large-scale monolingual language models pre-trained for Vietnamese. PhoBERT pre-training approach is based on [RoBERTa](https://github.com/pytorch/fairseq/blob/master/examples/roberta/README.md) which optimizes the [BERT](https://github.com/google-research/bert) pre-training procedure for more robust performance.
- PhoBERT outperforms previous monolingual and multilingual approaches, obtaining new state-of-the-art performances on four downstream Vietnamese NLP tasks of Part-of-speech tagging, Dependency parsing, Named-entity recognition and Natural language inference.
The general architecture and experimental results of PhoBERT can be found in our [paper](https://www.aclweb.org/anthology/2020.findings-emnlp.92/):
@inproceedings{phobert,
title = {{PhoBERT: Pre-trained language models for Vietnamese}},
author = {Dat Quoc Nguyen and Anh Tuan Nguyen},
booktitle = {Findings of the Association for Computational Linguistics: EMNLP 2020},
year = {2020},
pages = {1037--1042}
}
**Please CITE** our paper when PhoBERT is used to help produce published results or is incorporated into other software.
## <a name="transformers"></a> Using PhoBERT with `transformers`
### Installation <a name="install2"></a>
- Install `transformers` with pip: `pip install transformers`, or [install `transformers` from source](https://huggingface.co/docs/transformers/installation#installing-from-source). <br />
Note that we merged a slow tokenizer for PhoBERT into the main `transformers` branch. The process of merging a fast tokenizer for PhoBERT is in the discussion, as mentioned in [this pull request](https://github.com/huggingface/transformers/pull/17254#issuecomment-1133932067). If users would like to utilize the fast tokenizer, the users might install `transformers` as follows:
```
git clone --single-branch --branch fast_tokenizers_BARTpho_PhoBERT_BERTweet https://github.com/datquocnguyen/transformers.git
cd transformers
pip3 install -e .
```
- Install `tokenizers` with pip: `pip3 install tokenizers`
### Pre-trained models <a name="models2"></a>
Model | #params | Arch. | Max length | Pre-training data
---|---|---|---|---
`vinai/phobert-base` | 135M | base | 256 | 20GB of Wikipedia and News texts
`vinai/phobert-large` | 370M | large | 256 | 20GB of Wikipedia and News texts
`vinai/phobert-base-v2` | 135M | base | 256 | 20GB of Wikipedia and News texts + 120GB of texts from OSCAR-2301
### Example usage <a name="usage2"></a>
```python
import torch
from transformers import AutoModel, AutoTokenizer
phobert = AutoModel.from_pretrained("vinai/phobert-base-v2")
tokenizer = AutoTokenizer.from_pretrained("vinai/phobert-base-v2")
# INPUT TEXT MUST BE ALREADY WORD-SEGMENTED!
sentence = 'Chúng_tôi là những nghiên_cứu_viên .'
input_ids = torch.tensor([tokenizer.encode(sentence)])
with torch.no_grad():
features = phobert(input_ids) # Models outputs are now tuples
## With TensorFlow 2.0+:
# from transformers import TFAutoModel
# phobert = TFAutoModel.from_pretrained("vinai/phobert-base")
```
## <a name="fairseq"></a> Using PhoBERT with `fairseq`
Please see details at [HERE](https://github.com/VinAIResearch/PhoBERT/blob/master/README_fairseq.md)!
## <a name="vncorenlp"></a> Notes
In case the input texts are `raw`, i.e. without word segmentation, a word segmenter must be applied to produce word-segmented texts before feeding to PhoBERT. As PhoBERT employed the [RDRSegmenter](https://github.com/datquocnguyen/RDRsegmenter) from [VnCoreNLP](https://github.com/vncorenlp/VnCoreNLP) to pre-process the pre-training data (including [Vietnamese tone normalization](https://github.com/VinAIResearch/BARTpho/blob/main/VietnameseToneNormalization.md) and word and sentence segmentation), it is recommended to also use the same word segmenter for PhoBERT-based downstream applications w.r.t. the input raw texts.
#### Installation
pip install py_vncorenlp
#### Example usage <a name="example"></a>
```python
import py_vncorenlp
# Automatically download VnCoreNLP components from the original repository
# and save them in some local machine folder
py_vncorenlp.download_model(save_dir='/absolute/path/to/vncorenlp')
# Load the word and sentence segmentation component
rdrsegmenter = py_vncorenlp.VnCoreNLP(annotators=["wseg"], save_dir='/absolute/path/to/vncorenlp')
text = "Ông Nguyễn Khắc Chúc đang làm việc tại Đại học Quốc gia Hà Nội. Bà Lan, vợ ông Chúc, cũng làm việc tại đây."
output = rdrsegmenter.word_segment(text)
print(output)
# ['Ông Nguyễn_Khắc_Chúc đang làm_việc tại Đại_học Quốc_gia Hà_Nội .', 'Bà Lan , vợ ông Chúc , cũng làm_việc tại đây .']
```
## License
MIT License
Copyright (c) 2020 VinAI Research
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
|
BigTooth/DialoGPT-Megumin
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
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| 16 | null |
---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: test
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 1.0
---
# test
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
#### electron tube
#### kondansator
|
BillelBenoudjit/jplu-wikiann
|
[
"fr",
"dataset:wikiann",
"model-index"
] | null |
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}
| 0 | 2023-04-24T11:04:29Z |
---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
datasets:
- generated
model-index:
- name: layoutlmv3-finetuned-generated
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. -->
# layoutlmv3-finetuned-generated
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the generated dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.0009
- eval_precision: 1.0
- eval_recall: 1.0
- eval_f1: 1.0
- eval_accuracy: 1.0
- eval_runtime: 4.3549
- eval_samples_per_second: 11.481
- eval_steps_per_second: 5.741
- epoch: 30.0
- step: 1500
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 2500
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
Binbin/test
|
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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
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.9345
- name: F1
type: f1
value: 0.9346825135706527
---
<!-- 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: 0.1528
- Accuracy: 0.9345
- F1: 0.9347
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.1782 | 1.0 | 250 | 0.1814 | 0.9335 | 0.9330 |
| 0.1111 | 2.0 | 500 | 0.1528 | 0.9345 | 0.9347 |
### Framework versions
- Transformers 4.13.0
- Pytorch 1.13.0
- Datasets 2.8.0
- Tokenizers 0.10.3
|
Biniam/en_ti_translate
|
[
"pytorch",
"marian",
"text2text-generation",
"transformers",
"translation",
"autotrain_compatible"
] |
translation
|
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}
| 14 | null |
<p>Tips for Spotting Romance Scam Bots</p> <p class="MsoNormal"><span style="font-size: 12.0pt; line-height: 107%; font-family: 'Times New Roman',serif; mso-fareast-font-family: 'Times New Roman'; mso-font-kerning: 0pt; mso-ligatures: none;">Romance scams have been a prevalent issue for years, but with the rise of technology, it's become easier for scammers to create sophisticated bots with artificial intelligence that mimic human behavior. </span>According to the Federal Trade Commission, Americans lost $304 million to romance scams in 2020, with the median loss being $2,500 per victim. <span style="font-size: 12.0pt; line-height: 107%; font-family: 'Times New Roman',serif; mso-fareast-font-family: 'Times New Roman'; mso-font-kerning: 0pt; mso-ligatures: none;">These bots prey on vulnerabilities and can be difficult to spot. </span>These scams can be devastating, both emotionally and financially, and it's essential to know how to protect yourself. W<span style="font-size: 12.0pt; line-height: 107%; font-family: 'Times New Roman',serif; mso-fareast-font-family: 'Times New Roman'; mso-font-kerning: 0pt; mso-ligatures: none;">ith some knowledge and the right tools, it's possible to protect yourself from these malicious bots. In this article, we'll provide some tips on how to spot romance scam AI bots and protect yourself from falling victim to their scams.</span></p> <p><strong>What are Romance Scams?</strong></p> <p><span style="mso-spacerun: yes;"> </span>Romance scams occur when a scammer creates a fake online profile on a dating site or app to gain the trust of their victim. The scammer then uses that trust to get their victim to send them money or personal information. These scams are often referred to as catfishing, a term popularized by the documentary of the same name.</p> <p><strong>Red Flags: Conversation Flow</strong></p> <p>Although bots are becoming more advanced and harder to spot in romance scams, there are still some red flags that are noticeable. Here are some warning signs to look out for when trying to spot a bot:</p> <p style="margin-left: .5in; text-indent: -.25in; mso-list: l0 level1 lfo1; tab-stops: list .5in;"><!-- [if !supportLists]--><span style="mso-list: Ignore;">1.<span style="font: 7.0pt 'Times New Roman';"> </span></span><!--[endif]--><strong>Repeating phrases:</strong> Often repeat the same phrases or questions over and over again, even if they don't make sense in the context of the conversation</p> <p style="margin-left: .5in; text-indent: -.25in; mso-list: l0 level1 lfo1; tab-stops: list .5in;"><!-- [if !supportLists]--><span style="mso-list: Ignore;">2.<span style="font: 7.0pt 'Times New Roman';"> </span></span><!--[endif]--><strong>Inconsistent responses:</strong> May respond to a question with a completely unrelated answer or change the subject altogether</p> <p style="margin-left: .5in; text-indent: -.25in; mso-list: l0 level1 lfo1; tab-stops: list .5in;"><!-- [if !supportLists]--><span style="mso-list: Ignore;">3.<span style="font: 7.0pt 'Times New Roman';"> </span></span><!--[endif]--><strong>Lack of personalization:</strong> Often avoid personal details about themselves or their lives and stick to general topics</p> <p style="margin-left: .5in; text-indent: -.25in; mso-list: l0 level1 lfo1; tab-stops: list .5in;"><!-- [if !supportLists]--><span style="mso-list: Ignore;">4.<span style="font: 7.0pt 'Times New Roman';"> </span></span><!--[endif]--><strong>Strange timing, immediate responses:</strong> Bots may send messages instantly at odd times of the day or night</p> <p><strong>Red Flags: Topics</strong></p> <p>Romance scam bots generally gravitate towards certain conversation topics that are glaring red flags. These include:</p> <p style="margin-left: .5in; text-indent: -.25in; mso-list: l2 level1 lfo2; tab-stops: list .5in;"><!-- [if !supportLists]--><span style="mso-list: Ignore;">1.<span style="font: 7.0pt 'Times New Roman';"> </span></span><!--[endif]--><strong>They ask for money:</strong> May ask for money for a variety of reasons, such as medical bills, travel expenses, or other emergencies. Be wary of anyone who asks for money early on in the relationship.</p> <p style="margin-left: .5in; text-indent: -.25in; mso-list: l2 level1 lfo2; tab-stops: list .5in;"><!-- [if !supportLists]--><span style="mso-list: Ignore;">2.<span style="font: 7.0pt 'Times New Roman';"> </span></span><!--[endif]--><strong>They don't want to meet in person:</strong> May make excuses for why they can't meet in person or avoid talking about meeting altogether.</p> <p style="margin-left: .5in; text-indent: -.25in; mso-list: l2 level1 lfo2; tab-stops: list .5in;"><!-- [if !supportLists]--><span style="mso-list: Ignore;">3.<span style="font: 7.0pt 'Times New Roman';"> </span></span><!--[endif]--><strong>They ask for personal information:</strong> May ask for your personal information, such as your social security number or bank account information, to steal your identity or money.</p> <p style="margin-left: .5in; text-indent: -.25in; mso-list: l2 level1 lfo2; tab-stops: list .5in;"><!-- [if !supportLists]--><span style="mso-list: Ignore;">4.<span style="font: 7.0pt 'Times New Roman';"> </span></span><!--[endif]--><strong>They have inconsistencies in their story:</strong> May make up stories about their background or life that don't add up or change over time.</p> <p>The best way to protect yourself from romance scams is to be cautious and vigilant. Here are some tips to keep in mind:</p> <p style="margin-left: .5in; text-indent: -.25in; mso-list: l1 level1 lfo3; tab-stops: list .5in;"><!-- [if !supportLists]--><span style="mso-list: Ignore;">1.<span style="font: 7.0pt 'Times New Roman';"> </span></span><!--[endif]--><strong>Take it slow:</strong> Don't rush into a relationship or give out personal information too quickly. <br style="mso-special-character: line-break;"><!-- [if !supportLineBreakNewLine]--><br style="mso-special-character: line-break;"><!--[endif]--></p> <p style="margin-left: .5in; text-indent: -.25in; mso-list: l1 level1 lfo3; tab-stops: list .5in;"><!-- [if !supportLists]--><span style="mso-list: Ignore;">2.<span style="font: 7.0pt 'Times New Roman';"> </span></span><!--[endif]--><strong>Do your research</strong>: Look up the person using <a href="https://www.answerlady.com">people search to verify their identity</a>. By inputting their name, phone number or address, you can see if they’re a legitimate person by verifying the information they’ve given you. If you see any inconsistencies, there’s a chance you’re speaking to a bot!<br style="mso-special-character: line-break;"><!-- [if !supportLineBreakNewLine]--><br style="mso-special-character: line-break;"><!--[endif]--></p> <p style="margin-left: .5in; text-indent: -.25in; mso-list: l1 level1 lfo3; tab-stops: list .5in;"><!-- [if !supportLists]--><span style="mso-list: Ignore;">3.<span style="font: 7.0pt 'Times New Roman';"> </span></span><!--[endif]--><strong>Meet in person</strong>: If possible, try to meet in person early on in the relationship to confirm that the person is who they say they are.<br style="mso-special-character: line-break;"><!-- [if !supportLineBreakNewLine]--><br style="mso-special-character: line-break;"><!--[endif]--></p> <p style="margin-left: .5in; text-indent: -.25in; mso-list: l1 level1 lfo3; tab-stops: list .5in;"><!-- [if !supportLists]--><span style="mso-list: Ignore;">4.<span style="font: 7.0pt 'Times New Roman';"> </span></span><!--[endif]--><strong>Trust your instincts:</strong> If something doesn't feel right, trust your instincts and proceed with caution. Romance scam AI bots are designed to manipulate your emotions, so it's important to stay vigilant and not let your guard down.</p> <p style="margin-left: .5in;"> </p> <p class="MsoNormal"> </p>
|
BitanBiswas/mbert-bengali-ner-finetuned-ner
|
[
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] |
token-classification
|
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| 4 | null |
---
tags:
- generated_from_trainer
model-index:
- name: finetunedtopicscustomdata5
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. -->
# finetunedtopicscustomdata5
This model is a fine-tuned version of [fabiochiu/t5-base-medium-title-generation](https://huggingface.co/fabiochiu/t5-base-medium-title-generation) on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 2.0717
- eval_rouge1: 42.0048
- eval_rouge2: 30.2988
- eval_rougeL: 40.511
- eval_rougeLsum: 40.5566
- eval_gen_len: 11.1905
- eval_runtime: 634.4609
- eval_samples_per_second: 3.152
- eval_steps_per_second: 1.576
- epoch: 2.0
- step: 2000
## 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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
Blabla/Pipipopo
|
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| 0 | null |
---
license: mit
---
**PromptGPT - An NLP companion for ai Artists**
PromptGPT is a prompt generation model that has been designed to aid artists to expand their ideas. The model has been fine-tuned on a vast corpus of artistic and literary works to ensure that it can provide the most relevant and contextually appropriate prompts.
**How to Use PromptGPT**
Using PromptGPT is straightforward and intuitive. All you need to do is input a prompt or idea, and the model will generate a list of relevant prompts to help you develop your concept further.
While it is also possible to generate prompts without user input, it's best practice to define at least the main subject of the piece.
Try it out here: https://huggingface.co/spaces/Celestinian/Prompt-Generator
|
Blazeolmo/Scrabunzi
|
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| 0 | null |
---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### Robot--Style Dreambooth model trained by VuDucQuang with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Sample pictures of this concept:
|
Bloodwarrior/Chikfalay
|
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| 0 | null |
---
library_name: ml-agents
tags:
- SnowballTarget
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SnowballTarget
2. Step 1: Find your model_id: anilkumar2444/ppo-SnowballTargetTESTCOLAB
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
BlueGamerBeast/DialoGPT-small-joshua
|
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| 0 | null |
---
library_name: ml-agents
tags:
- SnowballTarget
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SnowballTarget
2. Step 1: Find your model_id: henrikho/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Bman/DialoGPT-medium-harrypotter
|
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| 0 | null |
---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
---
# andyP/sf-it-aug-01
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("andyP/sf-it-aug-01")
# 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}
}
```
|
BonjinKim/dst_kor_bert
|
[
"pytorch",
"jax",
"bert",
"pretraining",
"transformers"
] | null |
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| 5 | null |
---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
datasets:
- sroie
model-index:
- name: layoutlmv3-finetuned-sroie
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. -->
# layoutlmv3-finetuned-sroie
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the sroie dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 1000
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
Boondong/Wandee
|
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| 0 | null |
---
tags:
- LunarLander-v2
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: -143.41 +/- 79.89
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': 'unit8-part-sol'
'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': 50000
'learning_rate': 0.00025
'num_envs': 4
'num_steps': 128
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.95
'num_minibatches': 4
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'mojemai/ppo-LunarLander-v2'
'batch_size': 512
'minibatch_size': 128}
```
|
BossLee/t5-gec
|
[
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] |
text2text-generation
|
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| 6 | null |
---
license: apache-2.0
tags:
- classification
- generated_from_trainer
datasets:
- tweet_sentiment_multilingual
metrics:
- accuracy
model-index:
- name: clasificador-tweet-sentiment
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tweet_sentiment_multilingual
type: tweet_sentiment_multilingual
config: english
split: test
args: english
metrics:
- name: Accuracy
type: accuracy
value: 0.6632183908045977
---
<!-- 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. -->
# clasificador-tweet-sentiment
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the tweet_sentiment_multilingual dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2664
- Accuracy: 0.6632
## 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
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 230 | 0.7369 | 0.6713 |
| No log | 2.0 | 460 | 0.9109 | 0.6690 |
| 0.6916 | 3.0 | 690 | 1.2664 | 0.6632 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
Botslity/Bot
|
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| 0 | null |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: gptneo-txt2ARXMLv1.1
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. -->
# gptneo-txt2ARXMLv1.1
This model is a fine-tuned version of [EleutherAI/gpt-neo-125m](https://huggingface.co/EleutherAI/gpt-neo-125m) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3422
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.8106 | 0.99 | 45 | 0.8572 |
| 0.823 | 1.98 | 90 | 0.7944 |
| 0.783 | 3.0 | 136 | 0.7406 |
| 0.6243 | 3.99 | 181 | 0.6961 |
| 0.6158 | 4.98 | 226 | 0.6422 |
| 0.6009 | 5.99 | 272 | 0.5747 |
| 0.4856 | 6.99 | 317 | 0.5171 |
| 0.4512 | 8.0 | 363 | 0.4585 |
| 0.3926 | 8.99 | 408 | 0.3999 |
| 0.2923 | 9.92 | 450 | 0.3422 |
### Framework versions
- Transformers 4.27.4
- Pytorch 1.13.0
- Datasets 2.1.0
- Tokenizers 0.13.2
|
BotterHax/DialoGPT-small-harrypotter
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
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| 8 | null |
---
license: creativeml-openrail-m
---
✅ Use of this model for commercial image generation services
✅ Commercial use of images generated by this model
✅ Use or redistribution of merged models using this model
❌ The act of selling this model or a model merged with this model
❌ Intentionally using this model to produce illegal output
❌ Use of this model without crediting the model
❌ Have different permissions when sharing
❌ Violation of the following description
https://civitai.com/models/49567/calicomix
|
Brayan/CNN_Brain_Tumor
|
[] | null |
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}
| 0 | 2023-04-24T11:44:25Z |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -1.43 +/- 0.49
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Brendan/cse244b-hw2-roberta
|
[
"pytorch",
"roberta",
"text-classification",
"transformers"
] |
text-classification
|
{
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}
| 28 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
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: 0.0748
- Accuracy: 1.0
- F1: 1.0
## 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:
- 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 2.1253 | 1.0 | 21 | 1.8182 | 0.9391 | 0.9260 |
| 1.5009 | 2.0 | 42 | 1.0205 | 0.9652 | 0.9501 |
| 0.9143 | 3.0 | 63 | 0.5262 | 0.9957 | 0.9956 |
| 0.5215 | 4.0 | 84 | 0.2827 | 1.0 | 1.0 |
| 0.3069 | 5.0 | 105 | 0.1716 | 1.0 | 1.0 |
| 0.199 | 6.0 | 126 | 0.1194 | 1.0 | 1.0 |
| 0.147 | 7.0 | 147 | 0.0955 | 1.0 | 1.0 |
| 0.1229 | 8.0 | 168 | 0.0830 | 1.0 | 1.0 |
| 0.1076 | 9.0 | 189 | 0.0768 | 1.0 | 1.0 |
| 0.1002 | 10.0 | 210 | 0.0748 | 1.0 | 1.0 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
BrianTin/MTBERT
|
[
"pytorch",
"jax",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
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"BertForMaskedLM"
],
"model_type": "bert",
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}
}
| 11 | 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="LiamZ0302/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"])
```
|
Brinah/1
|
[] | null |
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| 0 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: mt5-base-finetuned-xsum
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. -->
# mt5-base-finetuned-xsum
This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) 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: 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: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| No log | 1.0 | 63 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 6.986 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
BritishLibraryLabs/bl-books-genre
|
[
"pytorch",
"distilbert",
"text-classification",
"multilingual",
"dataset:blbooksgenre",
"transformers",
"genre",
"books",
"library",
"historic",
"glam ",
"lam",
"license:mit",
"has_space"
] |
text-classification
|
{
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"DistilBertForSequenceClassification"
],
"model_type": "distilbert",
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}
| 76 | null |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.50 +/- 2.72
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="mojemai/q-Taxi-v1", 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"])
```
|
Broadus20/DialoGPT-small-harrypotter
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers"
] |
text-generation
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
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},
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}
| 9 | null |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="LiamZ0302/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"])
```
|
Brunomezenga/NN
|
[] | null |
{
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}
}
}
| 0 | null |
---
license: creativeml-openrail-m
tags:
- stablediffusionapi.com
- stable-diffusion-api
- text-to-image
- ultra-realistic
pinned: true
---
# Chillout Ni AF 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 "chillout-ni-af"
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/chillout-ni-af)
Credits: [View credits](https://civitai.com/?query=Chillout%20Ni%20AF)
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": "chillout-ni-af",
"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**
|
Bryanwong/wangchanberta-ner
|
[] | null |
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| 0 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: torgo_healthy_2
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. -->
# torgo_healthy_2
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.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 20
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
Brykee/BrykeeBot
|
[] | null |
{
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| 0 | null |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: distilbert-ft-test2
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. -->
# distilbert-ft-test2
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:
## 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': 5e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.28.1
- TensorFlow 2.12.0
- Datasets 2.11.0
- Tokenizers 0.13.3
|
Brykee/DialoGPT-medium-Morty
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
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},
"translation_en_to_fr": {
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}
| 10 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bert-base-uncased-finetuned-squad
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-base-uncased-finetuned-squad
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8657
## 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: 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 127 | 3.4034 |
| No log | 2.0 | 254 | 2.9517 |
| No log | 3.0 | 381 | 2.8657 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Buntan/bert-finetuned-ner
|
[
"pytorch",
"tensorboard",
"bert",
"token-classification",
"dataset:conll2003",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] |
token-classification
|
{
"architectures": [
"BertForTokenClassification"
],
"model_type": "bert",
"task_specific_params": {
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}
}
| 8 | null |
---
license: mit
tags:
- generated_from_trainer
datasets:
- imagefolder
model-index:
- name: Donut-for-Invoices
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. -->
# Donut-for-Invoices
This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on the imagefolder 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: 1
- 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
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0
- Datasets 2.11.0
- Tokenizers 0.11.0
|
CAMeL-Lab/bert-base-arabic-camelbert-mix-did-nadi
|
[
"pytorch",
"tf",
"bert",
"text-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0"
] |
text-classification
|
{
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"BertForSequenceClassification"
],
"model_type": "bert",
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},
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},
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},
"translation_en_to_fr": {
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}
| 63 | null |
This is an adapter for the Dolly-6B model that has been fine-tuned on Coq code from the CoqGym dataset.
|
CAMeL-Lab/bert-base-arabic-camelbert-mix-ner
|
[
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] |
token-classification
|
{
"architectures": [
"BertForTokenClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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"prefix": null
},
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"max_length": null
},
"translation_en_to_de": {
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"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
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}
}
}
| 1,860 | null |
---
license: bigscience-bloom-rail-1.0
datasets:
- tatoeba
- Abirate/french_book_reviews
language:
- fr
library_name: transformers
pipeline_tag: text-generation
---
[Bloom](https://huggingface.co/bigscience/bloom) model trained on French corpus.
# Config
* vocab_size=10000
* hidden_size=hidden_size
* n_head=8
* n_layer=12
|
dccuchile/albert-xlarge-spanish-finetuned-ner
|
[
"pytorch",
"albert",
"token-classification",
"transformers",
"autotrain_compatible"
] |
token-classification
|
{
"architectures": [
"AlbertForTokenClassification"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
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"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
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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="VcRlAgent/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"])
```
|
CennetOguz/distilbert-base-uncased-finetuned-recipe-accelerate-1
|
[
"pytorch",
"distilbert",
"fill-mask",
"transformers",
"autotrain_compatible"
] |
fill-mask
|
{
"architectures": [
"DistilBertForMaskedLM"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
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},
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},
"translation_en_to_fr": {
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},
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}
}
| 1 | null |
---
license: mit
pipeline_tag: text-classification
---
## Roberta for Justification analyst
This model is a fine-tuned version of the Roberta architecture that has been trained specifically for sequence classification. The fine-tuning process involved using the PyTorch deep learning framework and specific hyperparameters (2-4e, 1-8 epsilon) with Adagrad optimizer.
---
## Example Usage
To use the model, first load it in PyTorch:
```python
import torch
from transformers import RobertaForSequenceClassification, RobertaTokenizer
# Load the fine-tuned model
model = RobertaForSequenceClassification.from_pretrained('Dzeniks/justification-analyst')
# Load the tokenizer
tokenizer = RobertaTokenizer.from_pretrained('Dzeniks/justification-analyst')
# Tokenize the input sequence
input_text = "This is a sample input sequence"
input = tokenizer.encode_plus(claim, evidence, return_tensors="pt")
# Use the model to make a prediction
model.eval()
with torch.no_grad():
prediction = model(**x)
predictions = torch.argmax(outputs[0], dim=1).item()
```
## Classification Labels
The model was trained on a dataset consisting of claims and evidence, where the goal was to classify each claim as either supporting, refuting, or not having enough information to make a decision. The labels used for this task are as follows:
- Label 0: Supports
- Label 1: Refutes
- Label 2: Not enough information
|
CharlieChen/feedback-bigbird
|
[] | null |
{
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},
"translation_en_to_fr": {
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},
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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: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="ChSchweiger/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"])
```
|
ChaseBread/DialoGPT-small-harrypotter
|
[
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] |
conversational
|
{
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
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"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
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},
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}
}
}
| 9 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: torgo_healthy_2_40
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. -->
# torgo_healthy_2_40
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.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 40
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
Cheatham/xlm-roberta-base-finetuned
|
[
"pytorch",
"xlm-roberta",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"XLMRobertaForSequenceClassification"
],
"model_type": "xlm-roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
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},
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},
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"early_stopping": null,
"max_length": null,
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},
"translation_en_to_fr": {
"early_stopping": null,
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},
"translation_en_to_ro": {
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}
}
}
| 20 | null |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- beans
metrics:
- accuracy
model-index:
- name: platzi-vit-model-julio-garcia
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: beans
type: beans
config: default
split: validation
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9699248120300752
---
<!-- 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. -->
# platzi-vit-model-julio-garcia
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1061
- Accuracy: 0.9699
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 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: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1352 | 3.85 | 500 | 0.1061 | 0.9699 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
Cheatham/xlm-roberta-large-finetuned-d1
|
[
"pytorch",
"xlm-roberta",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"XLMRobertaForSequenceClassification"
],
"model_type": "xlm-roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
"translation_en_to_de": {
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},
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| 20 | null |
Access to model rajeeva703/autotrain-news_trans_01-52106122897 is restricted and you are not in the authorized list. Visit https://huggingface.co/rajeeva703/autotrain-news_trans_01-52106122897 to ask for access.
|
Cheatham/xlm-roberta-large-finetuned-d12
|
[
"pytorch",
"xlm-roberta",
"text-classification",
"transformers"
] |
text-classification
|
{
"architectures": [
"XLMRobertaForSequenceClassification"
],
"model_type": "xlm-roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
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},
"text-generation": {
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},
"translation_en_to_de": {
"early_stopping": null,
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},
"translation_en_to_fr": {
"early_stopping": null,
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"max_length": null,
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}
}
}
| 20 | 2023-04-24T15:59:18Z |
---
license: creativeml-openrail-m
tags:
- stablediffusionapi.com
- stable-diffusion-api
- text-to-image
- ultra-realistic
pinned: true
---
# Rev Anim 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 "rev-anim"
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/rev-anim)
Credits: [View credits](https://civitai.com/?query=Rev%20Anim)
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": "rev-anim",
"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**
|
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