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transformers
|
# Vision Transformer (base-sized model)
Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 224x224. It was introduced in the paper [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://arxiv.org/abs/2010.11929) by Dosovitskiy et al. and first released in [this repository](https://github.com/google-research/vision_transformer). However, the weights were converted from the [timm repository](https://github.com/rwightman/pytorch-image-models) by Ross Wightman, who already converted the weights from JAX to PyTorch. Credits go to him.
Disclaimer: The team releasing ViT did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels. Next, the model was fine-tuned on ImageNet (also referred to as ILSVRC2012), a dataset comprising 1 million images and 1,000 classes, also at resolution 224x224.
Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder.
By pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image.
## Intended uses & limitations
You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=google/vit) to look for
fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
```python
from transformers import ViTImageProcessor, ViTForImageClassification
from PIL import Image
import requests
url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
image = Image.open(requests.get(url, stream=True).raw)
processor = ViTImageProcessor.from_pretrained('google/vit-base-patch16-224')
model = ViTForImageClassification.from_pretrained('google/vit-base-patch16-224')
inputs = processor(images=image, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
# model predicts one of the 1000 ImageNet classes
predicted_class_idx = logits.argmax(-1).item()
print("Predicted class:", model.config.id2label[predicted_class_idx])
```
For more code examples, we refer to the [documentation](https://huggingface.co/transformers/model_doc/vit.html#).
## Training data
The ViT model was pretrained on [ImageNet-21k](http://www.image-net.org/), a dataset consisting of 14 million images and 21k classes, and fine-tuned on [ImageNet](http://www.image-net.org/challenges/LSVRC/2012/), a dataset consisting of 1 million images and 1k classes.
## Training procedure
### Preprocessing
The exact details of preprocessing of images during training/validation can be found [here](https://github.com/google-research/vision_transformer/blob/master/vit_jax/input_pipeline.py).
Images are resized/rescaled to the same resolution (224x224) and normalized across the RGB channels with mean (0.5, 0.5, 0.5) and standard deviation (0.5, 0.5, 0.5).
### Pretraining
The model was trained on TPUv3 hardware (8 cores). All model variants are trained with a batch size of 4096 and learning rate warmup of 10k steps. For ImageNet, the authors found it beneficial to additionally apply gradient clipping at global norm 1. Training resolution is 224.
## Evaluation results
For evaluation results on several image classification benchmarks, we refer to tables 2 and 5 of the original paper. Note that for fine-tuning, the best results are obtained with a higher resolution (384x384). Of course, increasing the model size will result in better performance.
### BibTeX entry and citation info
```bibtex
@misc{wu2020visual,
title={Visual Transformers: Token-based Image Representation and Processing for Computer Vision},
author={Bichen Wu and Chenfeng Xu and Xiaoliang Dai and Alvin Wan and Peizhao Zhang and Zhicheng Yan and Masayoshi Tomizuka and Joseph Gonzalez and Kurt Keutzer and Peter Vajda},
year={2020},
eprint={2006.03677},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
```bibtex
@inproceedings{deng2009imagenet,
title={Imagenet: A large-scale hierarchical image database},
author={Deng, Jia and Dong, Wei and Socher, Richard and Li, Li-Jia and Li, Kai and Fei-Fei, Li},
booktitle={2009 IEEE conference on computer vision and pattern recognition},
pages={248--255},
year={2009},
organization={Ieee}
}
```
|
{"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet-1k", "imagenet-21k"], "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"}]}
|
image-classification
|
google/vit-base-patch16-224
|
[
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"vit",
"image-classification",
"vision",
"dataset:imagenet-1k",
"dataset:imagenet-21k",
"arxiv:2010.11929",
"arxiv:2006.03677",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2010.11929",
"2006.03677"
] |
[] |
TAGS
#transformers #pytorch #tf #jax #safetensors #vit #image-classification #vision #dataset-imagenet-1k #dataset-imagenet-21k #arxiv-2010.11929 #arxiv-2006.03677 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Vision Transformer (base-sized model)
Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 224x224. It was introduced in the paper An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale by Dosovitskiy et al. and first released in this repository. However, the weights were converted from the timm repository by Ross Wightman, who already converted the weights from JAX to PyTorch. Credits go to him.
Disclaimer: The team releasing ViT did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels. Next, the model was fine-tuned on ImageNet (also referred to as ILSVRC2012), a dataset comprising 1 million images and 1,000 classes, also at resolution 224x224.
Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder.
By pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image.
## Intended uses & limitations
You can use the raw model for image classification. See the model hub to look for
fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
For more code examples, we refer to the documentation.
## Training data
The ViT model was pretrained on ImageNet-21k, a dataset consisting of 14 million images and 21k classes, and fine-tuned on ImageNet, a dataset consisting of 1 million images and 1k classes.
## Training procedure
### Preprocessing
The exact details of preprocessing of images during training/validation can be found here.
Images are resized/rescaled to the same resolution (224x224) and normalized across the RGB channels with mean (0.5, 0.5, 0.5) and standard deviation (0.5, 0.5, 0.5).
### Pretraining
The model was trained on TPUv3 hardware (8 cores). All model variants are trained with a batch size of 4096 and learning rate warmup of 10k steps. For ImageNet, the authors found it beneficial to additionally apply gradient clipping at global norm 1. Training resolution is 224.
## Evaluation results
For evaluation results on several image classification benchmarks, we refer to tables 2 and 5 of the original paper. Note that for fine-tuning, the best results are obtained with a higher resolution (384x384). Of course, increasing the model size will result in better performance.
### BibTeX entry and citation info
|
[
"# Vision Transformer (base-sized model) \n\nVision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 224x224. It was introduced in the paper An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale by Dosovitskiy et al. and first released in this repository. However, the weights were converted from the timm repository by Ross Wightman, who already converted the weights from JAX to PyTorch. Credits go to him. \n\nDisclaimer: The team releasing ViT did not write a model card for this model so this model card has been written by the Hugging Face team.",
"## Model description\n\nThe Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels. Next, the model was fine-tuned on ImageNet (also referred to as ILSVRC2012), a dataset comprising 1 million images and 1,000 classes, also at resolution 224x224.\n\nImages are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder.\n\nBy pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image.",
"## Intended uses & limitations\n\nYou can use the raw model for image classification. See the model hub to look for\nfine-tuned versions on a task that interests you.",
"### How to use\n\nHere is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:\n\n\n\nFor more code examples, we refer to the documentation.",
"## Training data\n\nThe ViT model was pretrained on ImageNet-21k, a dataset consisting of 14 million images and 21k classes, and fine-tuned on ImageNet, a dataset consisting of 1 million images and 1k classes.",
"## Training procedure",
"### Preprocessing\n\nThe exact details of preprocessing of images during training/validation can be found here. \n\nImages are resized/rescaled to the same resolution (224x224) and normalized across the RGB channels with mean (0.5, 0.5, 0.5) and standard deviation (0.5, 0.5, 0.5).",
"### Pretraining\n\nThe model was trained on TPUv3 hardware (8 cores). All model variants are trained with a batch size of 4096 and learning rate warmup of 10k steps. For ImageNet, the authors found it beneficial to additionally apply gradient clipping at global norm 1. Training resolution is 224.",
"## Evaluation results\n\nFor evaluation results on several image classification benchmarks, we refer to tables 2 and 5 of the original paper. Note that for fine-tuning, the best results are obtained with a higher resolution (384x384). Of course, increasing the model size will result in better performance.",
"### BibTeX entry and citation info"
] |
[
"TAGS\n#transformers #pytorch #tf #jax #safetensors #vit #image-classification #vision #dataset-imagenet-1k #dataset-imagenet-21k #arxiv-2010.11929 #arxiv-2006.03677 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Vision Transformer (base-sized model) \n\nVision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 224x224. It was introduced in the paper An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale by Dosovitskiy et al. and first released in this repository. However, the weights were converted from the timm repository by Ross Wightman, who already converted the weights from JAX to PyTorch. Credits go to him. \n\nDisclaimer: The team releasing ViT did not write a model card for this model so this model card has been written by the Hugging Face team.",
"## Model description\n\nThe Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels. Next, the model was fine-tuned on ImageNet (also referred to as ILSVRC2012), a dataset comprising 1 million images and 1,000 classes, also at resolution 224x224.\n\nImages are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder.\n\nBy pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image.",
"## Intended uses & limitations\n\nYou can use the raw model for image classification. See the model hub to look for\nfine-tuned versions on a task that interests you.",
"### How to use\n\nHere is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:\n\n\n\nFor more code examples, we refer to the documentation.",
"## Training data\n\nThe ViT model was pretrained on ImageNet-21k, a dataset consisting of 14 million images and 21k classes, and fine-tuned on ImageNet, a dataset consisting of 1 million images and 1k classes.",
"## Training procedure",
"### Preprocessing\n\nThe exact details of preprocessing of images during training/validation can be found here. \n\nImages are resized/rescaled to the same resolution (224x224) and normalized across the RGB channels with mean (0.5, 0.5, 0.5) and standard deviation (0.5, 0.5, 0.5).",
"### Pretraining\n\nThe model was trained on TPUv3 hardware (8 cores). All model variants are trained with a batch size of 4096 and learning rate warmup of 10k steps. For ImageNet, the authors found it beneficial to additionally apply gradient clipping at global norm 1. Training resolution is 224.",
"## Evaluation results\n\nFor evaluation results on several image classification benchmarks, we refer to tables 2 and 5 of the original paper. Note that for fine-tuning, the best results are obtained with a higher resolution (384x384). Of course, increasing the model size will result in better performance.",
"### BibTeX entry and citation info"
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] |
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] |
null | null |
transformers
|
# Vision Transformer (base-sized model)
Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 384x384. It was introduced in the paper [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://arxiv.org/abs/2010.11929) by Dosovitskiy et al. and first released in [this repository](https://github.com/google-research/vision_transformer). However, the weights were converted from the [timm repository](https://github.com/rwightman/pytorch-image-models) by Ross Wightman, who already converted the weights from JAX to PyTorch. Credits go to him.
Disclaimer: The team releasing ViT did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels. Next, the model was fine-tuned on ImageNet (also referred to as ILSVRC2012), a dataset comprising 1 million images and 1,000 classes, at a higher resolution of 384x384.
Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder.
By pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image.
## Intended uses & limitations
You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=google/vit) to look for
fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
```python
from transformers import ViTFeatureExtractor, ViTForImageClassification
from PIL import Image
import requests
url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
image = Image.open(requests.get(url, stream=True).raw)
feature_extractor = ViTFeatureExtractor.from_pretrained('google/vit-base-patch16-384')
model = ViTForImageClassification.from_pretrained('google/vit-base-patch16-384')
inputs = feature_extractor(images=image, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
# model predicts one of the 1000 ImageNet classes
predicted_class_idx = logits.argmax(-1).item()
print("Predicted class:", model.config.id2label[predicted_class_idx])
```
Currently, both the feature extractor and model support PyTorch. Tensorflow and JAX/FLAX are coming soon, and the API of ViTFeatureExtractor might change.
## Training data
The ViT model was pretrained on [ImageNet-21k](http://www.image-net.org/), a dataset consisting of 14 million images and 21k classes, and fine-tuned on [ImageNet](http://www.image-net.org/challenges/LSVRC/2012/), a dataset consisting of 1 million images and 1k classes.
## Training procedure
### Preprocessing
The exact details of preprocessing of images during training/validation can be found [here](https://github.com/google-research/vision_transformer/blob/master/vit_jax/input_pipeline.py).
Images are resized/rescaled to the same resolution (224x224 during pre-training, 384x384 during fine-tuning) and normalized across the RGB channels with mean (0.5, 0.5, 0.5) and standard deviation (0.5, 0.5, 0.5).
### Pretraining
The model was trained on TPUv3 hardware (8 cores). All model variants are trained with a batch size of 4096 and learning rate warmup of 10k steps. For ImageNet, the authors found it beneficial to additionally apply gradient clipping at global norm 1. Pre-training resolution is 224.
## Evaluation results
For evaluation results on several image classification benchmarks, we refer to tables 2 and 5 of the original paper. Note that for fine-tuning, the best results are obtained with a higher resolution (384x384). Of course, increasing the model size will result in better performance.
### BibTeX entry and citation info
```bibtex
@misc{wu2020visual,
title={Visual Transformers: Token-based Image Representation and Processing for Computer Vision},
author={Bichen Wu and Chenfeng Xu and Xiaoliang Dai and Alvin Wan and Peizhao Zhang and Zhicheng Yan and Masayoshi Tomizuka and Joseph Gonzalez and Kurt Keutzer and Peter Vajda},
year={2020},
eprint={2006.03677},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
```bibtex
@inproceedings{deng2009imagenet,
title={Imagenet: A large-scale hierarchical image database},
author={Deng, Jia and Dong, Wei and Socher, Richard and Li, Li-Jia and Li, Kai and Fei-Fei, Li},
booktitle={2009 IEEE conference on computer vision and pattern recognition},
pages={248--255},
year={2009},
organization={Ieee}
}
```
|
{"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet", "imagenet-21k"]}
|
image-classification
|
google/vit-base-patch16-384
|
[
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"vit",
"image-classification",
"vision",
"dataset:imagenet",
"dataset:imagenet-21k",
"arxiv:2010.11929",
"arxiv:2006.03677",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2010.11929",
"2006.03677"
] |
[] |
TAGS
#transformers #pytorch #tf #jax #safetensors #vit #image-classification #vision #dataset-imagenet #dataset-imagenet-21k #arxiv-2010.11929 #arxiv-2006.03677 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Vision Transformer (base-sized model)
Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 384x384. It was introduced in the paper An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale by Dosovitskiy et al. and first released in this repository. However, the weights were converted from the timm repository by Ross Wightman, who already converted the weights from JAX to PyTorch. Credits go to him.
Disclaimer: The team releasing ViT did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels. Next, the model was fine-tuned on ImageNet (also referred to as ILSVRC2012), a dataset comprising 1 million images and 1,000 classes, at a higher resolution of 384x384.
Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder.
By pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image.
## Intended uses & limitations
You can use the raw model for image classification. See the model hub to look for
fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
Currently, both the feature extractor and model support PyTorch. Tensorflow and JAX/FLAX are coming soon, and the API of ViTFeatureExtractor might change.
## Training data
The ViT model was pretrained on ImageNet-21k, a dataset consisting of 14 million images and 21k classes, and fine-tuned on ImageNet, a dataset consisting of 1 million images and 1k classes.
## Training procedure
### Preprocessing
The exact details of preprocessing of images during training/validation can be found here.
Images are resized/rescaled to the same resolution (224x224 during pre-training, 384x384 during fine-tuning) and normalized across the RGB channels with mean (0.5, 0.5, 0.5) and standard deviation (0.5, 0.5, 0.5).
### Pretraining
The model was trained on TPUv3 hardware (8 cores). All model variants are trained with a batch size of 4096 and learning rate warmup of 10k steps. For ImageNet, the authors found it beneficial to additionally apply gradient clipping at global norm 1. Pre-training resolution is 224.
## Evaluation results
For evaluation results on several image classification benchmarks, we refer to tables 2 and 5 of the original paper. Note that for fine-tuning, the best results are obtained with a higher resolution (384x384). Of course, increasing the model size will result in better performance.
### BibTeX entry and citation info
|
[
"# Vision Transformer (base-sized model) \n\nVision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 384x384. It was introduced in the paper An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale by Dosovitskiy et al. and first released in this repository. However, the weights were converted from the timm repository by Ross Wightman, who already converted the weights from JAX to PyTorch. Credits go to him. \n\nDisclaimer: The team releasing ViT did not write a model card for this model so this model card has been written by the Hugging Face team.",
"## Model description\n\nThe Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels. Next, the model was fine-tuned on ImageNet (also referred to as ILSVRC2012), a dataset comprising 1 million images and 1,000 classes, at a higher resolution of 384x384.\n\nImages are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder.\n\nBy pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image.",
"## Intended uses & limitations\n\nYou can use the raw model for image classification. See the model hub to look for\nfine-tuned versions on a task that interests you.",
"### How to use\n\nHere is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:\n\n\n\nCurrently, both the feature extractor and model support PyTorch. Tensorflow and JAX/FLAX are coming soon, and the API of ViTFeatureExtractor might change.",
"## Training data\n\nThe ViT model was pretrained on ImageNet-21k, a dataset consisting of 14 million images and 21k classes, and fine-tuned on ImageNet, a dataset consisting of 1 million images and 1k classes.",
"## Training procedure",
"### Preprocessing\n\nThe exact details of preprocessing of images during training/validation can be found here. \n\nImages are resized/rescaled to the same resolution (224x224 during pre-training, 384x384 during fine-tuning) and normalized across the RGB channels with mean (0.5, 0.5, 0.5) and standard deviation (0.5, 0.5, 0.5).",
"### Pretraining\n\nThe model was trained on TPUv3 hardware (8 cores). All model variants are trained with a batch size of 4096 and learning rate warmup of 10k steps. For ImageNet, the authors found it beneficial to additionally apply gradient clipping at global norm 1. Pre-training resolution is 224.",
"## Evaluation results\n\nFor evaluation results on several image classification benchmarks, we refer to tables 2 and 5 of the original paper. Note that for fine-tuning, the best results are obtained with a higher resolution (384x384). Of course, increasing the model size will result in better performance.",
"### BibTeX entry and citation info"
] |
[
"TAGS\n#transformers #pytorch #tf #jax #safetensors #vit #image-classification #vision #dataset-imagenet #dataset-imagenet-21k #arxiv-2010.11929 #arxiv-2006.03677 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Vision Transformer (base-sized model) \n\nVision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 384x384. It was introduced in the paper An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale by Dosovitskiy et al. and first released in this repository. However, the weights were converted from the timm repository by Ross Wightman, who already converted the weights from JAX to PyTorch. Credits go to him. \n\nDisclaimer: The team releasing ViT did not write a model card for this model so this model card has been written by the Hugging Face team.",
"## Model description\n\nThe Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels. Next, the model was fine-tuned on ImageNet (also referred to as ILSVRC2012), a dataset comprising 1 million images and 1,000 classes, at a higher resolution of 384x384.\n\nImages are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder.\n\nBy pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image.",
"## Intended uses & limitations\n\nYou can use the raw model for image classification. See the model hub to look for\nfine-tuned versions on a task that interests you.",
"### How to use\n\nHere is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:\n\n\n\nCurrently, both the feature extractor and model support PyTorch. Tensorflow and JAX/FLAX are coming soon, and the API of ViTFeatureExtractor might change.",
"## Training data\n\nThe ViT model was pretrained on ImageNet-21k, a dataset consisting of 14 million images and 21k classes, and fine-tuned on ImageNet, a dataset consisting of 1 million images and 1k classes.",
"## Training procedure",
"### Preprocessing\n\nThe exact details of preprocessing of images during training/validation can be found here. \n\nImages are resized/rescaled to the same resolution (224x224 during pre-training, 384x384 during fine-tuning) and normalized across the RGB channels with mean (0.5, 0.5, 0.5) and standard deviation (0.5, 0.5, 0.5).",
"### Pretraining\n\nThe model was trained on TPUv3 hardware (8 cores). All model variants are trained with a batch size of 4096 and learning rate warmup of 10k steps. For ImageNet, the authors found it beneficial to additionally apply gradient clipping at global norm 1. Pre-training resolution is 224.",
"## Evaluation results\n\nFor evaluation results on several image classification benchmarks, we refer to tables 2 and 5 of the original paper. Note that for fine-tuning, the best results are obtained with a higher resolution (384x384). Of course, increasing the model size will result in better performance.",
"### BibTeX entry and citation info"
] |
[
92,
188,
302,
41,
76,
54,
3,
85,
73,
64,
11
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[
"passage: TAGS\n#transformers #pytorch #tf #jax #safetensors #vit #image-classification #vision #dataset-imagenet #dataset-imagenet-21k #arxiv-2010.11929 #arxiv-2006.03677 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n# Vision Transformer (base-sized model) \n\nVision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 384x384. It was introduced in the paper An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale by Dosovitskiy et al. and first released in this repository. However, the weights were converted from the timm repository by Ross Wightman, who already converted the weights from JAX to PyTorch. Credits go to him. \n\nDisclaimer: The team releasing ViT did not write a model card for this model so this model card has been written by the Hugging Face team."
] |
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] |
null | null |
transformers
|
# Vision Transformer (base-sized model)
Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224. It was introduced in the paper [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://arxiv.org/abs/2010.11929) by Dosovitskiy et al. and first released in [this repository](https://github.com/google-research/vision_transformer). However, the weights were converted from the [timm repository](https://github.com/rwightman/pytorch-image-models) by Ross Wightman, who already converted the weights from JAX to PyTorch. Credits go to him.
Disclaimer: The team releasing ViT did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels.
Images are presented to the model as a sequence of fixed-size patches (resolution 32x32), which are linearly embedded. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder.
Note that this model does not provide any fine-tuned heads, as these were zero'd by Google researchers. However, the model does include the pre-trained pooler, which can be used for downstream tasks (such as image classification).
By pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image.
## Intended uses & limitations
You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=google/vit) to look for
fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model in PyTorch:
```python
from transformers import ViTImageProcessor, ViTModel
from PIL import Image
import requests
url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
image = Image.open(requests.get(url, stream=True).raw)
processor = ViTImageProcessor.from_pretrained('google/vit-base-patch32-224-in21k')
model = ViTModel.from_pretrained('google/vit-base-patch32-224-in21k')
inputs = processor(images=image, return_tensors="pt")
outputs = model(**inputs)
last_hidden_state = outputs.last_hidden_state
```
Refer to the [docs](https://huggingface.co/docs/transformers/model_doc/vit) for usage in TensorFlow and JAX/FLAX.
## Training data
The ViT model was pretrained on [ImageNet-21k](http://www.image-net.org/), a dataset consisting of 14 million images and 21k classes.
## Training procedure
### Preprocessing
The exact details of preprocessing of images during training/validation can be found [here](https://github.com/google-research/vision_transformer/blob/master/vit_jax/input_pipeline.py).
Images are resized/rescaled to the same resolution (224x224) and normalized across the RGB channels with mean (0.5, 0.5, 0.5) and standard deviation (0.5, 0.5, 0.5).
### Pretraining
The model was trained on TPUv3 hardware (8 cores). All model variants are trained with a batch size of 4096 and learning rate warmup of 10k steps. For ImageNet, the authors found it beneficial to additionally apply gradient clipping at global norm 1. Pre-training resolution is 224.
## Evaluation results
For evaluation results on several image classification benchmarks, we refer to tables 2 and 5 of the original paper. Note that for fine-tuning, the best results are obtained with a higher resolution (384x384). Of course, increasing the model size will result in better performance.
### BibTeX entry and citation info
```bibtex
@misc{wu2020visual,
title={Visual Transformers: Token-based Image Representation and Processing for Computer Vision},
author={Bichen Wu and Chenfeng Xu and Xiaoliang Dai and Alvin Wan and Peizhao Zhang and Zhicheng Yan and Masayoshi Tomizuka and Joseph Gonzalez and Kurt Keutzer and Peter Vajda},
year={2020},
eprint={2006.03677},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
```bibtex
@inproceedings{deng2009imagenet,
title={Imagenet: A large-scale hierarchical image database},
author={Deng, Jia and Dong, Wei and Socher, Richard and Li, Li-Jia and Li, Kai and Fei-Fei, Li},
booktitle={2009 IEEE conference on computer vision and pattern recognition},
pages={248--255},
year={2009},
organization={Ieee}
}
```
|
{"license": "apache-2.0", "tags": ["vision"], "datasets": ["imagenet-21k"], "inference": false}
|
feature-extraction
|
google/vit-base-patch32-224-in21k
|
[
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"vit",
"feature-extraction",
"vision",
"dataset:imagenet-21k",
"arxiv:2010.11929",
"arxiv:2006.03677",
"license:apache-2.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2010.11929",
"2006.03677"
] |
[] |
TAGS
#transformers #pytorch #tf #jax #safetensors #vit #feature-extraction #vision #dataset-imagenet-21k #arxiv-2010.11929 #arxiv-2006.03677 #license-apache-2.0 #region-us
|
# Vision Transformer (base-sized model)
Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224. It was introduced in the paper An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale by Dosovitskiy et al. and first released in this repository. However, the weights were converted from the timm repository by Ross Wightman, who already converted the weights from JAX to PyTorch. Credits go to him.
Disclaimer: The team releasing ViT did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels.
Images are presented to the model as a sequence of fixed-size patches (resolution 32x32), which are linearly embedded. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder.
Note that this model does not provide any fine-tuned heads, as these were zero'd by Google researchers. However, the model does include the pre-trained pooler, which can be used for downstream tasks (such as image classification).
By pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image.
## Intended uses & limitations
You can use the raw model for image classification. See the model hub to look for
fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model in PyTorch:
Refer to the docs for usage in TensorFlow and JAX/FLAX.
## Training data
The ViT model was pretrained on ImageNet-21k, a dataset consisting of 14 million images and 21k classes.
## Training procedure
### Preprocessing
The exact details of preprocessing of images during training/validation can be found here.
Images are resized/rescaled to the same resolution (224x224) and normalized across the RGB channels with mean (0.5, 0.5, 0.5) and standard deviation (0.5, 0.5, 0.5).
### Pretraining
The model was trained on TPUv3 hardware (8 cores). All model variants are trained with a batch size of 4096 and learning rate warmup of 10k steps. For ImageNet, the authors found it beneficial to additionally apply gradient clipping at global norm 1. Pre-training resolution is 224.
## Evaluation results
For evaluation results on several image classification benchmarks, we refer to tables 2 and 5 of the original paper. Note that for fine-tuning, the best results are obtained with a higher resolution (384x384). Of course, increasing the model size will result in better performance.
### BibTeX entry and citation info
|
[
"# Vision Transformer (base-sized model) \n\nVision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224. It was introduced in the paper An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale by Dosovitskiy et al. and first released in this repository. However, the weights were converted from the timm repository by Ross Wightman, who already converted the weights from JAX to PyTorch. Credits go to him. \n\nDisclaimer: The team releasing ViT did not write a model card for this model so this model card has been written by the Hugging Face team.",
"## Model description\n\nThe Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels. \n\nImages are presented to the model as a sequence of fixed-size patches (resolution 32x32), which are linearly embedded. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder.\n\nNote that this model does not provide any fine-tuned heads, as these were zero'd by Google researchers. However, the model does include the pre-trained pooler, which can be used for downstream tasks (such as image classification).\n\nBy pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image.",
"## Intended uses & limitations\n\nYou can use the raw model for image classification. See the model hub to look for\nfine-tuned versions on a task that interests you.",
"### How to use\n\nHere is how to use this model in PyTorch:\n\n\n\nRefer to the docs for usage in TensorFlow and JAX/FLAX.",
"## Training data\n\nThe ViT model was pretrained on ImageNet-21k, a dataset consisting of 14 million images and 21k classes.",
"## Training procedure",
"### Preprocessing\n\nThe exact details of preprocessing of images during training/validation can be found here. \n\nImages are resized/rescaled to the same resolution (224x224) and normalized across the RGB channels with mean (0.5, 0.5, 0.5) and standard deviation (0.5, 0.5, 0.5).",
"### Pretraining\n\nThe model was trained on TPUv3 hardware (8 cores). All model variants are trained with a batch size of 4096 and learning rate warmup of 10k steps. For ImageNet, the authors found it beneficial to additionally apply gradient clipping at global norm 1. Pre-training resolution is 224.",
"## Evaluation results\n\nFor evaluation results on several image classification benchmarks, we refer to tables 2 and 5 of the original paper. Note that for fine-tuning, the best results are obtained with a higher resolution (384x384). Of course, increasing the model size will result in better performance.",
"### BibTeX entry and citation info"
] |
[
"TAGS\n#transformers #pytorch #tf #jax #safetensors #vit #feature-extraction #vision #dataset-imagenet-21k #arxiv-2010.11929 #arxiv-2006.03677 #license-apache-2.0 #region-us \n",
"# Vision Transformer (base-sized model) \n\nVision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224. It was introduced in the paper An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale by Dosovitskiy et al. and first released in this repository. However, the weights were converted from the timm repository by Ross Wightman, who already converted the weights from JAX to PyTorch. Credits go to him. \n\nDisclaimer: The team releasing ViT did not write a model card for this model so this model card has been written by the Hugging Face team.",
"## Model description\n\nThe Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels. \n\nImages are presented to the model as a sequence of fixed-size patches (resolution 32x32), which are linearly embedded. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder.\n\nNote that this model does not provide any fine-tuned heads, as these were zero'd by Google researchers. However, the model does include the pre-trained pooler, which can be used for downstream tasks (such as image classification).\n\nBy pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image.",
"## Intended uses & limitations\n\nYou can use the raw model for image classification. See the model hub to look for\nfine-tuned versions on a task that interests you.",
"### How to use\n\nHere is how to use this model in PyTorch:\n\n\n\nRefer to the docs for usage in TensorFlow and JAX/FLAX.",
"## Training data\n\nThe ViT model was pretrained on ImageNet-21k, a dataset consisting of 14 million images and 21k classes.",
"## Training procedure",
"### Preprocessing\n\nThe exact details of preprocessing of images during training/validation can be found here. \n\nImages are resized/rescaled to the same resolution (224x224) and normalized across the RGB channels with mean (0.5, 0.5, 0.5) and standard deviation (0.5, 0.5, 0.5).",
"### Pretraining\n\nThe model was trained on TPUv3 hardware (8 cores). All model variants are trained with a batch size of 4096 and learning rate warmup of 10k steps. For ImageNet, the authors found it beneficial to additionally apply gradient clipping at global norm 1. Pre-training resolution is 224.",
"## Evaluation results\n\nFor evaluation results on several image classification benchmarks, we refer to tables 2 and 5 of the original paper. Note that for fine-tuning, the best results are obtained with a higher resolution (384x384). Of course, increasing the model size will result in better performance.",
"### BibTeX entry and citation info"
] |
[
67,
164,
313,
41,
36,
31,
3,
70,
73,
64,
11
] |
[
"passage: TAGS\n#transformers #pytorch #tf #jax #safetensors #vit #feature-extraction #vision #dataset-imagenet-21k #arxiv-2010.11929 #arxiv-2006.03677 #license-apache-2.0 #region-us \n# Vision Transformer (base-sized model) \n\nVision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224. It was introduced in the paper An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale by Dosovitskiy et al. and first released in this repository. However, the weights were converted from the timm repository by Ross Wightman, who already converted the weights from JAX to PyTorch. Credits go to him. \n\nDisclaimer: The team releasing ViT did not write a model card for this model so this model card has been written by the Hugging Face team."
] |
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null | null |
transformers
|
# Vision Transformer (base-sized model)
Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 384x384. It was introduced in the paper [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://arxiv.org/abs/2010.11929) by Dosovitskiy et al. and first released in [this repository](https://github.com/google-research/vision_transformer). However, the weights were converted from the [timm repository](https://github.com/rwightman/pytorch-image-models) by Ross Wightman, who already converted the weights from JAX to PyTorch. Credits go to him.
Disclaimer: The team releasing ViT did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels. Next, the model was fine-tuned on ImageNet (also referred to as ILSVRC2012), a dataset comprising 1 million images and 1,000 classes, at a higher resolution of 384x384.
Images are presented to the model as a sequence of fixed-size patches (resolution 32x32), which are linearly embedded. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder.
By pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image.
## Intended uses & limitations
You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=google/vit) to look for
fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
```python
from transformers import ViTFeatureExtractor, ViTForImageClassification
from PIL import Image
import requests
url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
image = Image.open(requests.get(url, stream=True).raw)
feature_extractor = ViTFeatureExtractor.from_pretrained('google/vit-base-patch32-384')
model = ViTForImageClassification.from_pretrained('google/vit-base-patch32-384')
inputs = feature_extractor(images=image, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
# model predicts one of the 1000 ImageNet classes
predicted_class_idx = logits.argmax(-1).item()
print("Predicted class:", model.config.id2label[predicted_class_idx])
```
Currently, both the feature extractor and model support PyTorch. Tensorflow and JAX/FLAX are coming soon, and the API of ViTFeatureExtractor might change.
## Training data
The ViT model was pretrained on [ImageNet-21k](http://www.image-net.org/), a dataset consisting of 14 million images and 21k classes, and fine-tuned on [ImageNet](http://www.image-net.org/challenges/LSVRC/2012/), a dataset consisting of 1 million images and 1k classes.
## Training procedure
### Preprocessing
The exact details of preprocessing of images during training/validation can be found [here](https://github.com/google-research/vision_transformer/blob/master/vit_jax/input_pipeline.py).
Images are resized/rescaled to the same resolution (224x224 during pre-training, 384x384 during fine-tuning) and normalized across the RGB channels with mean (0.5, 0.5, 0.5) and standard deviation (0.5, 0.5, 0.5).
### Pretraining
The model was trained on TPUv3 hardware (8 cores). All model variants are trained with a batch size of 4096 and learning rate warmup of 10k steps. For ImageNet, the authors found it beneficial to additionally apply gradient clipping at global norm 1. Pre-training resolution is 224.
## Evaluation results
For evaluation results on several image classification benchmarks, we refer to tables 2 and 5 of the original paper. Note that for fine-tuning, the best results are obtained with a higher resolution (384x384). Of course, increasing the model size will result in better performance.
### BibTeX entry and citation info
```bibtex
@misc{https://doi.org/10.48550/arxiv.2010.11929,
doi = {10.48550/ARXIV.2010.11929},
url = {https://arxiv.org/abs/2010.11929},
author = {Dosovitskiy, Alexey and Beyer, Lucas and Kolesnikov, Alexander and Weissenborn, Dirk and Zhai, Xiaohua and Unterthiner, Thomas and Dehghani, Mostafa and Minderer, Matthias and Heigold, Georg and Gelly, Sylvain and Uszkoreit, Jakob and Houlsby, Neil},
keywords = {Computer Vision and Pattern Recognition (cs.CV), Artificial Intelligence (cs.AI), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale},
publisher = {arXiv},
year = {2020},
copyright = {arXiv.org perpetual, non-exclusive license}
}
```
```bibtex
@inproceedings{deng2009imagenet,
title={Imagenet: A large-scale hierarchical image database},
author={Deng, Jia and Dong, Wei and Socher, Richard and Li, Li-Jia and Li, Kai and Fei-Fei, Li},
booktitle={2009 IEEE conference on computer vision and pattern recognition},
pages={248--255},
year={2009},
organization={Ieee}
}
```
|
{"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet-1k", "imagenet-21k"]}
|
image-classification
|
google/vit-base-patch32-384
|
[
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"vit",
"image-classification",
"vision",
"dataset:imagenet-1k",
"dataset:imagenet-21k",
"arxiv:2010.11929",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2010.11929"
] |
[] |
TAGS
#transformers #pytorch #tf #jax #safetensors #vit #image-classification #vision #dataset-imagenet-1k #dataset-imagenet-21k #arxiv-2010.11929 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Vision Transformer (base-sized model)
Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 384x384. It was introduced in the paper An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale by Dosovitskiy et al. and first released in this repository. However, the weights were converted from the timm repository by Ross Wightman, who already converted the weights from JAX to PyTorch. Credits go to him.
Disclaimer: The team releasing ViT did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels. Next, the model was fine-tuned on ImageNet (also referred to as ILSVRC2012), a dataset comprising 1 million images and 1,000 classes, at a higher resolution of 384x384.
Images are presented to the model as a sequence of fixed-size patches (resolution 32x32), which are linearly embedded. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder.
By pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image.
## Intended uses & limitations
You can use the raw model for image classification. See the model hub to look for
fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
Currently, both the feature extractor and model support PyTorch. Tensorflow and JAX/FLAX are coming soon, and the API of ViTFeatureExtractor might change.
## Training data
The ViT model was pretrained on ImageNet-21k, a dataset consisting of 14 million images and 21k classes, and fine-tuned on ImageNet, a dataset consisting of 1 million images and 1k classes.
## Training procedure
### Preprocessing
The exact details of preprocessing of images during training/validation can be found here.
Images are resized/rescaled to the same resolution (224x224 during pre-training, 384x384 during fine-tuning) and normalized across the RGB channels with mean (0.5, 0.5, 0.5) and standard deviation (0.5, 0.5, 0.5).
### Pretraining
The model was trained on TPUv3 hardware (8 cores). All model variants are trained with a batch size of 4096 and learning rate warmup of 10k steps. For ImageNet, the authors found it beneficial to additionally apply gradient clipping at global norm 1. Pre-training resolution is 224.
## Evaluation results
For evaluation results on several image classification benchmarks, we refer to tables 2 and 5 of the original paper. Note that for fine-tuning, the best results are obtained with a higher resolution (384x384). Of course, increasing the model size will result in better performance.
### BibTeX entry and citation info
|
[
"# Vision Transformer (base-sized model) \n\nVision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 384x384. It was introduced in the paper An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale by Dosovitskiy et al. and first released in this repository. However, the weights were converted from the timm repository by Ross Wightman, who already converted the weights from JAX to PyTorch. Credits go to him. \n\nDisclaimer: The team releasing ViT did not write a model card for this model so this model card has been written by the Hugging Face team.",
"## Model description\n\nThe Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels. Next, the model was fine-tuned on ImageNet (also referred to as ILSVRC2012), a dataset comprising 1 million images and 1,000 classes, at a higher resolution of 384x384.\n\nImages are presented to the model as a sequence of fixed-size patches (resolution 32x32), which are linearly embedded. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder.\n\nBy pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image.",
"## Intended uses & limitations\n\nYou can use the raw model for image classification. See the model hub to look for\nfine-tuned versions on a task that interests you.",
"### How to use\n\nHere is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:\n\n\n\nCurrently, both the feature extractor and model support PyTorch. Tensorflow and JAX/FLAX are coming soon, and the API of ViTFeatureExtractor might change.",
"## Training data\n\nThe ViT model was pretrained on ImageNet-21k, a dataset consisting of 14 million images and 21k classes, and fine-tuned on ImageNet, a dataset consisting of 1 million images and 1k classes.",
"## Training procedure",
"### Preprocessing\n\nThe exact details of preprocessing of images during training/validation can be found here. \n\nImages are resized/rescaled to the same resolution (224x224 during pre-training, 384x384 during fine-tuning) and normalized across the RGB channels with mean (0.5, 0.5, 0.5) and standard deviation (0.5, 0.5, 0.5).",
"### Pretraining\n\nThe model was trained on TPUv3 hardware (8 cores). All model variants are trained with a batch size of 4096 and learning rate warmup of 10k steps. For ImageNet, the authors found it beneficial to additionally apply gradient clipping at global norm 1. Pre-training resolution is 224.",
"## Evaluation results\n\nFor evaluation results on several image classification benchmarks, we refer to tables 2 and 5 of the original paper. Note that for fine-tuning, the best results are obtained with a higher resolution (384x384). Of course, increasing the model size will result in better performance.",
"### BibTeX entry and citation info"
] |
[
"TAGS\n#transformers #pytorch #tf #jax #safetensors #vit #image-classification #vision #dataset-imagenet-1k #dataset-imagenet-21k #arxiv-2010.11929 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Vision Transformer (base-sized model) \n\nVision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 384x384. It was introduced in the paper An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale by Dosovitskiy et al. and first released in this repository. However, the weights were converted from the timm repository by Ross Wightman, who already converted the weights from JAX to PyTorch. Credits go to him. \n\nDisclaimer: The team releasing ViT did not write a model card for this model so this model card has been written by the Hugging Face team.",
"## Model description\n\nThe Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels. Next, the model was fine-tuned on ImageNet (also referred to as ILSVRC2012), a dataset comprising 1 million images and 1,000 classes, at a higher resolution of 384x384.\n\nImages are presented to the model as a sequence of fixed-size patches (resolution 32x32), which are linearly embedded. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder.\n\nBy pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image.",
"## Intended uses & limitations\n\nYou can use the raw model for image classification. See the model hub to look for\nfine-tuned versions on a task that interests you.",
"### How to use\n\nHere is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:\n\n\n\nCurrently, both the feature extractor and model support PyTorch. Tensorflow and JAX/FLAX are coming soon, and the API of ViTFeatureExtractor might change.",
"## Training data\n\nThe ViT model was pretrained on ImageNet-21k, a dataset consisting of 14 million images and 21k classes, and fine-tuned on ImageNet, a dataset consisting of 1 million images and 1k classes.",
"## Training procedure",
"### Preprocessing\n\nThe exact details of preprocessing of images during training/validation can be found here. \n\nImages are resized/rescaled to the same resolution (224x224 during pre-training, 384x384 during fine-tuning) and normalized across the RGB channels with mean (0.5, 0.5, 0.5) and standard deviation (0.5, 0.5, 0.5).",
"### Pretraining\n\nThe model was trained on TPUv3 hardware (8 cores). All model variants are trained with a batch size of 4096 and learning rate warmup of 10k steps. For ImageNet, the authors found it beneficial to additionally apply gradient clipping at global norm 1. Pre-training resolution is 224.",
"## Evaluation results\n\nFor evaluation results on several image classification benchmarks, we refer to tables 2 and 5 of the original paper. Note that for fine-tuning, the best results are obtained with a higher resolution (384x384). Of course, increasing the model size will result in better performance.",
"### BibTeX entry and citation info"
] |
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[
"passage: TAGS\n#transformers #pytorch #tf #jax #safetensors #vit #image-classification #vision #dataset-imagenet-1k #dataset-imagenet-21k #arxiv-2010.11929 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n# Vision Transformer (base-sized model) \n\nVision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 384x384. It was introduced in the paper An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale by Dosovitskiy et al. and first released in this repository. However, the weights were converted from the timm repository by Ross Wightman, who already converted the weights from JAX to PyTorch. Credits go to him. \n\nDisclaimer: The team releasing ViT did not write a model card for this model so this model card has been written by the Hugging Face team."
] |
[
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0.09123536944389343,
0.0008248608792200685,
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null | null |
transformers
|
# Vision Transformer (huge-sized model)
Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224. It was introduced in the paper [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://arxiv.org/abs/2010.11929) by Dosovitskiy et al. and first released in [this repository](https://github.com/google-research/vision_transformer). However, the weights were converted from the [timm repository](https://github.com/rwightman/pytorch-image-models) by Ross Wightman, who already converted the weights from JAX to PyTorch. Credits go to him.
Disclaimer: The team releasing ViT did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels.
Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder.
Note that this model does not provide any fine-tuned heads, as these were zero'd by Google researchers. However, the model does include the pre-trained pooler, which can be used for downstream tasks (such as image classification).
By pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image.
## Intended uses & limitations
You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=google/vit) to look for
fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model:
```python
from transformers import ViTFeatureExtractor, ViTModel
from PIL import Image
import requests
url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
image = Image.open(requests.get(url, stream=True).raw)
feature_extractor = ViTFeatureExtractor.from_pretrained('google/vit-huge-patch14-224-in21k')
model = ViTModel.from_pretrained('google/vit-huge-patch14-224-in21k')
inputs = feature_extractor(images=image, return_tensors="pt")
outputs = model(**inputs)
last_hidden_states = outputs.last_hidden_state
```
Currently, both the feature extractor and model support PyTorch. Tensorflow and JAX/FLAX are coming soon, and the API of ViTFeatureExtractor might change.
## Training data
The ViT model was pretrained on [ImageNet-21k](http://www.image-net.org/), a dataset consisting of 14 million images and 21k classes.
## Training procedure
### Preprocessing
The exact details of preprocessing of images during training/validation can be found [here](https://github.com/google-research/vision_transformer/blob/master/vit_jax/input_pipeline.py).
Images are resized/rescaled to the same resolution (224x224) and normalized across the RGB channels with mean (0.5, 0.5, 0.5) and standard deviation (0.5, 0.5, 0.5).
### Pretraining
The model was trained on TPUv3 hardware (8 cores). All model variants are trained with a batch size of 4096 and learning rate warmup of 10k steps. For ImageNet, the authors found it beneficial to additionally apply gradient clipping at global norm 1. Pre-training resolution is 224.
## Evaluation results
For evaluation results on several image classification benchmarks, we refer to tables 2 and 5 of the original paper. Note that for fine-tuning, the best results are obtained with a higher resolution (384x384). Of course, increasing the model size will result in better performance.
### BibTeX entry and citation info
```bibtex
@misc{wu2020visual,
title={Visual Transformers: Token-based Image Representation and Processing for Computer Vision},
author={Bichen Wu and Chenfeng Xu and Xiaoliang Dai and Alvin Wan and Peizhao Zhang and Zhicheng Yan and Masayoshi Tomizuka and Joseph Gonzalez and Kurt Keutzer and Peter Vajda},
year={2020},
eprint={2006.03677},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
```bibtex
@inproceedings{deng2009imagenet,
title={Imagenet: A large-scale hierarchical image database},
author={Deng, Jia and Dong, Wei and Socher, Richard and Li, Li-Jia and Li, Kai and Fei-Fei, Li},
booktitle={2009 IEEE conference on computer vision and pattern recognition},
pages={248--255},
year={2009},
organization={Ieee}
}
```
|
{"license": "apache-2.0", "tags": ["vision"], "datasets": ["imagenet-21k"], "inference": false}
|
feature-extraction
|
google/vit-huge-patch14-224-in21k
|
[
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"vit",
"feature-extraction",
"vision",
"dataset:imagenet-21k",
"arxiv:2010.11929",
"arxiv:2006.03677",
"license:apache-2.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2010.11929",
"2006.03677"
] |
[] |
TAGS
#transformers #pytorch #tf #jax #safetensors #vit #feature-extraction #vision #dataset-imagenet-21k #arxiv-2010.11929 #arxiv-2006.03677 #license-apache-2.0 #region-us
|
# Vision Transformer (huge-sized model)
Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224. It was introduced in the paper An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale by Dosovitskiy et al. and first released in this repository. However, the weights were converted from the timm repository by Ross Wightman, who already converted the weights from JAX to PyTorch. Credits go to him.
Disclaimer: The team releasing ViT did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels.
Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder.
Note that this model does not provide any fine-tuned heads, as these were zero'd by Google researchers. However, the model does include the pre-trained pooler, which can be used for downstream tasks (such as image classification).
By pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image.
## Intended uses & limitations
You can use the raw model for image classification. See the model hub to look for
fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model:
Currently, both the feature extractor and model support PyTorch. Tensorflow and JAX/FLAX are coming soon, and the API of ViTFeatureExtractor might change.
## Training data
The ViT model was pretrained on ImageNet-21k, a dataset consisting of 14 million images and 21k classes.
## Training procedure
### Preprocessing
The exact details of preprocessing of images during training/validation can be found here.
Images are resized/rescaled to the same resolution (224x224) and normalized across the RGB channels with mean (0.5, 0.5, 0.5) and standard deviation (0.5, 0.5, 0.5).
### Pretraining
The model was trained on TPUv3 hardware (8 cores). All model variants are trained with a batch size of 4096 and learning rate warmup of 10k steps. For ImageNet, the authors found it beneficial to additionally apply gradient clipping at global norm 1. Pre-training resolution is 224.
## Evaluation results
For evaluation results on several image classification benchmarks, we refer to tables 2 and 5 of the original paper. Note that for fine-tuning, the best results are obtained with a higher resolution (384x384). Of course, increasing the model size will result in better performance.
### BibTeX entry and citation info
|
[
"# Vision Transformer (huge-sized model) \n\nVision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224. It was introduced in the paper An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale by Dosovitskiy et al. and first released in this repository. However, the weights were converted from the timm repository by Ross Wightman, who already converted the weights from JAX to PyTorch. Credits go to him. \n\nDisclaimer: The team releasing ViT did not write a model card for this model so this model card has been written by the Hugging Face team.",
"## Model description\n\nThe Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels. \n\nImages are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder.\n\nNote that this model does not provide any fine-tuned heads, as these were zero'd by Google researchers. However, the model does include the pre-trained pooler, which can be used for downstream tasks (such as image classification).\n\nBy pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image.",
"## Intended uses & limitations\n\nYou can use the raw model for image classification. See the model hub to look for\nfine-tuned versions on a task that interests you.",
"### How to use\n\nHere is how to use this model:\n\n\n\nCurrently, both the feature extractor and model support PyTorch. Tensorflow and JAX/FLAX are coming soon, and the API of ViTFeatureExtractor might change.",
"## Training data\n\nThe ViT model was pretrained on ImageNet-21k, a dataset consisting of 14 million images and 21k classes.",
"## Training procedure",
"### Preprocessing\n\nThe exact details of preprocessing of images during training/validation can be found here. \n\nImages are resized/rescaled to the same resolution (224x224) and normalized across the RGB channels with mean (0.5, 0.5, 0.5) and standard deviation (0.5, 0.5, 0.5).",
"### Pretraining\n\nThe model was trained on TPUv3 hardware (8 cores). All model variants are trained with a batch size of 4096 and learning rate warmup of 10k steps. For ImageNet, the authors found it beneficial to additionally apply gradient clipping at global norm 1. Pre-training resolution is 224.",
"## Evaluation results\n\nFor evaluation results on several image classification benchmarks, we refer to tables 2 and 5 of the original paper. Note that for fine-tuning, the best results are obtained with a higher resolution (384x384). Of course, increasing the model size will result in better performance.",
"### BibTeX entry and citation info"
] |
[
"TAGS\n#transformers #pytorch #tf #jax #safetensors #vit #feature-extraction #vision #dataset-imagenet-21k #arxiv-2010.11929 #arxiv-2006.03677 #license-apache-2.0 #region-us \n",
"# Vision Transformer (huge-sized model) \n\nVision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224. It was introduced in the paper An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale by Dosovitskiy et al. and first released in this repository. However, the weights were converted from the timm repository by Ross Wightman, who already converted the weights from JAX to PyTorch. Credits go to him. \n\nDisclaimer: The team releasing ViT did not write a model card for this model so this model card has been written by the Hugging Face team.",
"## Model description\n\nThe Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels. \n\nImages are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder.\n\nNote that this model does not provide any fine-tuned heads, as these were zero'd by Google researchers. However, the model does include the pre-trained pooler, which can be used for downstream tasks (such as image classification).\n\nBy pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image.",
"## Intended uses & limitations\n\nYou can use the raw model for image classification. See the model hub to look for\nfine-tuned versions on a task that interests you.",
"### How to use\n\nHere is how to use this model:\n\n\n\nCurrently, both the feature extractor and model support PyTorch. Tensorflow and JAX/FLAX are coming soon, and the API of ViTFeatureExtractor might change.",
"## Training data\n\nThe ViT model was pretrained on ImageNet-21k, a dataset consisting of 14 million images and 21k classes.",
"## Training procedure",
"### Preprocessing\n\nThe exact details of preprocessing of images during training/validation can be found here. \n\nImages are resized/rescaled to the same resolution (224x224) and normalized across the RGB channels with mean (0.5, 0.5, 0.5) and standard deviation (0.5, 0.5, 0.5).",
"### Pretraining\n\nThe model was trained on TPUv3 hardware (8 cores). All model variants are trained with a batch size of 4096 and learning rate warmup of 10k steps. For ImageNet, the authors found it beneficial to additionally apply gradient clipping at global norm 1. Pre-training resolution is 224.",
"## Evaluation results\n\nFor evaluation results on several image classification benchmarks, we refer to tables 2 and 5 of the original paper. Note that for fine-tuning, the best results are obtained with a higher resolution (384x384). Of course, increasing the model size will result in better performance.",
"### BibTeX entry and citation info"
] |
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] |
null | null |
transformers
|
# Vision Transformer (large-sized model)
Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224. It was introduced in the paper [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://arxiv.org/abs/2010.11929) by Dosovitskiy et al. and first released in [this repository](https://github.com/google-research/vision_transformer). However, the weights were converted from the [timm repository](https://github.com/rwightman/pytorch-image-models) by Ross Wightman, who already converted the weights from JAX to PyTorch. Credits go to him.
Disclaimer: The team releasing ViT did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels.
Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder.
Note that this model does not provide any fine-tuned heads, as these were zero'd by Google researchers. However, the model does include the pre-trained pooler, which can be used for downstream tasks (such as image classification).
By pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image.
## Intended uses & limitations
You can use the raw model to embed images, but it's mostly intended to be fine-tuned on a downstream task.
### How to use
Here is how to use this model:
```python
from transformers import ViTImageProcessor, ViTModel
from PIL import Image
import requests
url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
image = Image.open(requests.get(url, stream=True).raw)
processor = ViTImageProcessor.from_pretrained('google/vit-large-patch16-224-in21k')
model = ViTModel.from_pretrained('google/vit-large-patch16-224-in21k')
inputs = processor(images=image, return_tensors="pt")
outputs = model(**inputs)
last_hidden_state = outputs.last_hidden_state
```
Currently, both the feature extractor and model support PyTorch. Tensorflow and JAX/FLAX are coming soon, and the API of ViTFeatureExtractor might change.
## Training data
The ViT model was pretrained on [ImageNet-21k](http://www.image-net.org/), a dataset consisting of 14 million images and 21k classes.
## Training procedure
### Preprocessing
The exact details of preprocessing of images during training/validation can be found [here](https://github.com/google-research/vision_transformer/blob/master/vit_jax/input_pipeline.py).
Images are resized/rescaled to the same resolution (224x224) and normalized across the RGB channels with mean (0.5, 0.5, 0.5) and standard deviation (0.5, 0.5, 0.5).
### Pretraining
The model was trained on TPUv3 hardware (8 cores). All model variants are trained with a batch size of 4096 and learning rate warmup of 10k steps. For ImageNet, the authors found it beneficial to additionally apply gradient clipping at global norm 1. Pre-training resolution is 224.
## Evaluation results
For evaluation results on several image classification benchmarks, we refer to tables 2 and 5 of the original paper. Note that for fine-tuning, the best results are obtained with a higher resolution (384x384). Of course, increasing the model size will result in better performance.
### BibTeX entry and citation info
```bibtex
@misc{wu2020visual,
title={Visual Transformers: Token-based Image Representation and Processing for Computer Vision},
author={Bichen Wu and Chenfeng Xu and Xiaoliang Dai and Alvin Wan and Peizhao Zhang and Zhicheng Yan and Masayoshi Tomizuka and Joseph Gonzalez and Kurt Keutzer and Peter Vajda},
year={2020},
eprint={2006.03677},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
```bibtex
@inproceedings{deng2009imagenet,
title={Imagenet: A large-scale hierarchical image database},
author={Deng, Jia and Dong, Wei and Socher, Richard and Li, Li-Jia and Li, Kai and Fei-Fei, Li},
booktitle={2009 IEEE conference on computer vision and pattern recognition},
pages={248--255},
year={2009},
organization={Ieee}
}
```
|
{"license": "apache-2.0", "tags": ["vision"], "datasets": ["imagenet-21k"], "inference": false}
|
feature-extraction
|
google/vit-large-patch16-224-in21k
|
[
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"vit",
"feature-extraction",
"vision",
"dataset:imagenet-21k",
"arxiv:2010.11929",
"arxiv:2006.03677",
"license:apache-2.0",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2010.11929",
"2006.03677"
] |
[] |
TAGS
#transformers #pytorch #tf #jax #safetensors #vit #feature-extraction #vision #dataset-imagenet-21k #arxiv-2010.11929 #arxiv-2006.03677 #license-apache-2.0 #has_space #region-us
|
# Vision Transformer (large-sized model)
Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224. It was introduced in the paper An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale by Dosovitskiy et al. and first released in this repository. However, the weights were converted from the timm repository by Ross Wightman, who already converted the weights from JAX to PyTorch. Credits go to him.
Disclaimer: The team releasing ViT did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels.
Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder.
Note that this model does not provide any fine-tuned heads, as these were zero'd by Google researchers. However, the model does include the pre-trained pooler, which can be used for downstream tasks (such as image classification).
By pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image.
## Intended uses & limitations
You can use the raw model to embed images, but it's mostly intended to be fine-tuned on a downstream task.
### How to use
Here is how to use this model:
Currently, both the feature extractor and model support PyTorch. Tensorflow and JAX/FLAX are coming soon, and the API of ViTFeatureExtractor might change.
## Training data
The ViT model was pretrained on ImageNet-21k, a dataset consisting of 14 million images and 21k classes.
## Training procedure
### Preprocessing
The exact details of preprocessing of images during training/validation can be found here.
Images are resized/rescaled to the same resolution (224x224) and normalized across the RGB channels with mean (0.5, 0.5, 0.5) and standard deviation (0.5, 0.5, 0.5).
### Pretraining
The model was trained on TPUv3 hardware (8 cores). All model variants are trained with a batch size of 4096 and learning rate warmup of 10k steps. For ImageNet, the authors found it beneficial to additionally apply gradient clipping at global norm 1. Pre-training resolution is 224.
## Evaluation results
For evaluation results on several image classification benchmarks, we refer to tables 2 and 5 of the original paper. Note that for fine-tuning, the best results are obtained with a higher resolution (384x384). Of course, increasing the model size will result in better performance.
### BibTeX entry and citation info
|
[
"# Vision Transformer (large-sized model) \n\nVision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224. It was introduced in the paper An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale by Dosovitskiy et al. and first released in this repository. However, the weights were converted from the timm repository by Ross Wightman, who already converted the weights from JAX to PyTorch. Credits go to him. \n\nDisclaimer: The team releasing ViT did not write a model card for this model so this model card has been written by the Hugging Face team.",
"## Model description\n\nThe Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels. \n\nImages are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder.\n\nNote that this model does not provide any fine-tuned heads, as these were zero'd by Google researchers. However, the model does include the pre-trained pooler, which can be used for downstream tasks (such as image classification).\n\nBy pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image.",
"## Intended uses & limitations\n\nYou can use the raw model to embed images, but it's mostly intended to be fine-tuned on a downstream task.",
"### How to use\n\nHere is how to use this model:\n\n\n\nCurrently, both the feature extractor and model support PyTorch. Tensorflow and JAX/FLAX are coming soon, and the API of ViTFeatureExtractor might change.",
"## Training data\n\nThe ViT model was pretrained on ImageNet-21k, a dataset consisting of 14 million images and 21k classes.",
"## Training procedure",
"### Preprocessing\n\nThe exact details of preprocessing of images during training/validation can be found here. \n\nImages are resized/rescaled to the same resolution (224x224) and normalized across the RGB channels with mean (0.5, 0.5, 0.5) and standard deviation (0.5, 0.5, 0.5).",
"### Pretraining\n\nThe model was trained on TPUv3 hardware (8 cores). All model variants are trained with a batch size of 4096 and learning rate warmup of 10k steps. For ImageNet, the authors found it beneficial to additionally apply gradient clipping at global norm 1. Pre-training resolution is 224.",
"## Evaluation results\n\nFor evaluation results on several image classification benchmarks, we refer to tables 2 and 5 of the original paper. Note that for fine-tuning, the best results are obtained with a higher resolution (384x384). Of course, increasing the model size will result in better performance.",
"### BibTeX entry and citation info"
] |
[
"TAGS\n#transformers #pytorch #tf #jax #safetensors #vit #feature-extraction #vision #dataset-imagenet-21k #arxiv-2010.11929 #arxiv-2006.03677 #license-apache-2.0 #has_space #region-us \n",
"# Vision Transformer (large-sized model) \n\nVision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224. It was introduced in the paper An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale by Dosovitskiy et al. and first released in this repository. However, the weights were converted from the timm repository by Ross Wightman, who already converted the weights from JAX to PyTorch. Credits go to him. \n\nDisclaimer: The team releasing ViT did not write a model card for this model so this model card has been written by the Hugging Face team.",
"## Model description\n\nThe Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels. \n\nImages are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder.\n\nNote that this model does not provide any fine-tuned heads, as these were zero'd by Google researchers. However, the model does include the pre-trained pooler, which can be used for downstream tasks (such as image classification).\n\nBy pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image.",
"## Intended uses & limitations\n\nYou can use the raw model to embed images, but it's mostly intended to be fine-tuned on a downstream task.",
"### How to use\n\nHere is how to use this model:\n\n\n\nCurrently, both the feature extractor and model support PyTorch. Tensorflow and JAX/FLAX are coming soon, and the API of ViTFeatureExtractor might change.",
"## Training data\n\nThe ViT model was pretrained on ImageNet-21k, a dataset consisting of 14 million images and 21k classes.",
"## Training procedure",
"### Preprocessing\n\nThe exact details of preprocessing of images during training/validation can be found here. \n\nImages are resized/rescaled to the same resolution (224x224) and normalized across the RGB channels with mean (0.5, 0.5, 0.5) and standard deviation (0.5, 0.5, 0.5).",
"### Pretraining\n\nThe model was trained on TPUv3 hardware (8 cores). All model variants are trained with a batch size of 4096 and learning rate warmup of 10k steps. For ImageNet, the authors found it beneficial to additionally apply gradient clipping at global norm 1. Pre-training resolution is 224.",
"## Evaluation results\n\nFor evaluation results on several image classification benchmarks, we refer to tables 2 and 5 of the original paper. Note that for fine-tuning, the best results are obtained with a higher resolution (384x384). Of course, increasing the model size will result in better performance.",
"### BibTeX entry and citation info"
] |
[
71,
165,
313,
38,
56,
31,
3,
70,
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64,
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[
"passage: TAGS\n#transformers #pytorch #tf #jax #safetensors #vit #feature-extraction #vision #dataset-imagenet-21k #arxiv-2010.11929 #arxiv-2006.03677 #license-apache-2.0 #has_space #region-us \n# Vision Transformer (large-sized model) \n\nVision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224. It was introduced in the paper An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale by Dosovitskiy et al. and first released in this repository. However, the weights were converted from the timm repository by Ross Wightman, who already converted the weights from JAX to PyTorch. Credits go to him. \n\nDisclaimer: The team releasing ViT did not write a model card for this model so this model card has been written by the Hugging Face team."
] |
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] |
null | null |
transformers
|
# Vision Transformer (large-sized model)
Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 224x224. It was introduced in the paper [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://arxiv.org/abs/2010.11929) by Dosovitskiy et al. and first released in [this repository](https://github.com/google-research/vision_transformer). However, the weights were converted from the [timm repository](https://github.com/rwightman/pytorch-image-models) by Ross Wightman, who already converted the weights from JAX to PyTorch. Credits go to him.
Disclaimer: The team releasing ViT did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels. Next, the model was fine-tuned on ImageNet (also referred to as ILSVRC2012), a dataset comprising 1 million images and 1,000 classes, at the same resolution, 224x224.
Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder.
By pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image.
## Intended uses & limitations
You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=google/vit) to look for
fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
```python
from transformers import ViTFeatureExtractor, ViTForImageClassification
from PIL import Image
import requests
url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
image = Image.open(requests.get(url, stream=True).raw)
feature_extractor = ViTFeatureExtractor.from_pretrained('google/vit-large-patch16-224')
model = ViTForImageClassification.from_pretrained('google/vit-large-patch16-224')
inputs = feature_extractor(images=image, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
# model predicts one of the 1000 ImageNet classes
predicted_class_idx = logits.argmax(-1).item()
print("Predicted class:", model.config.id2label[predicted_class_idx])
```
Currently, both the feature extractor and model support PyTorch. Tensorflow and JAX/FLAX are coming soon, and the API of ViTFeatureExtractor might change.
## Training data
The ViT model was pretrained on [ImageNet-21k](http://www.image-net.org/), a dataset consisting of 14 million images and 21k classes, and fine-tuned on [ImageNet](http://www.image-net.org/challenges/LSVRC/2012/), a dataset consisting of 1 million images and 1k classes.
## Training procedure
### Preprocessing
The exact details of preprocessing of images during training/validation can be found [here](https://github.com/google-research/vision_transformer/blob/master/vit_jax/input_pipeline.py).
Images are resized/rescaled to the same resolution (224x224) and normalized across the RGB channels with mean (0.5, 0.5, 0.5) and standard deviation (0.5, 0.5, 0.5).
### Pretraining
The model was trained on TPUv3 hardware (8 cores). All model variants are trained with a batch size of 4096 and learning rate warmup of 10k steps. For ImageNet, the authors found it beneficial to additionally apply gradient clipping at global norm 1. Pre-training resolution is 224.
## Evaluation results
For evaluation results on several image classification benchmarks, we refer to tables 2 and 5 of the original paper. Note that for fine-tuning, the best results are obtained with a higher resolution (384x384). Of course, increasing the model size will result in better performance.
### BibTeX entry and citation info
```bibtex
@misc{wu2020visual,
title={Visual Transformers: Token-based Image Representation and Processing for Computer Vision},
author={Bichen Wu and Chenfeng Xu and Xiaoliang Dai and Alvin Wan and Peizhao Zhang and Zhicheng Yan and Masayoshi Tomizuka and Joseph Gonzalez and Kurt Keutzer and Peter Vajda},
year={2020},
eprint={2006.03677},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
```bibtex
@inproceedings{deng2009imagenet,
title={Imagenet: A large-scale hierarchical image database},
author={Deng, Jia and Dong, Wei and Socher, Richard and Li, Li-Jia and Li, Kai and Fei-Fei, Li},
booktitle={2009 IEEE conference on computer vision and pattern recognition},
pages={248--255},
year={2009},
organization={Ieee}
}
```
|
{"license": "apache-2.0", "tags": ["image-classification", "vision"], "datasets": ["imagenet-1k", "imagenet-21k"]}
|
image-classification
|
google/vit-large-patch16-224
|
[
"transformers",
"pytorch",
"tf",
"jax",
"vit",
"image-classification",
"vision",
"dataset:imagenet-1k",
"dataset:imagenet-21k",
"arxiv:2010.11929",
"arxiv:2006.03677",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2010.11929",
"2006.03677"
] |
[] |
TAGS
#transformers #pytorch #tf #jax #vit #image-classification #vision #dataset-imagenet-1k #dataset-imagenet-21k #arxiv-2010.11929 #arxiv-2006.03677 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Vision Transformer (large-sized model)
Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 224x224. It was introduced in the paper An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale by Dosovitskiy et al. and first released in this repository. However, the weights were converted from the timm repository by Ross Wightman, who already converted the weights from JAX to PyTorch. Credits go to him.
Disclaimer: The team releasing ViT did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels. Next, the model was fine-tuned on ImageNet (also referred to as ILSVRC2012), a dataset comprising 1 million images and 1,000 classes, at the same resolution, 224x224.
Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder.
By pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image.
## Intended uses & limitations
You can use the raw model for image classification. See the model hub to look for
fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
Currently, both the feature extractor and model support PyTorch. Tensorflow and JAX/FLAX are coming soon, and the API of ViTFeatureExtractor might change.
## Training data
The ViT model was pretrained on ImageNet-21k, a dataset consisting of 14 million images and 21k classes, and fine-tuned on ImageNet, a dataset consisting of 1 million images and 1k classes.
## Training procedure
### Preprocessing
The exact details of preprocessing of images during training/validation can be found here.
Images are resized/rescaled to the same resolution (224x224) and normalized across the RGB channels with mean (0.5, 0.5, 0.5) and standard deviation (0.5, 0.5, 0.5).
### Pretraining
The model was trained on TPUv3 hardware (8 cores). All model variants are trained with a batch size of 4096 and learning rate warmup of 10k steps. For ImageNet, the authors found it beneficial to additionally apply gradient clipping at global norm 1. Pre-training resolution is 224.
## Evaluation results
For evaluation results on several image classification benchmarks, we refer to tables 2 and 5 of the original paper. Note that for fine-tuning, the best results are obtained with a higher resolution (384x384). Of course, increasing the model size will result in better performance.
### BibTeX entry and citation info
|
[
"# Vision Transformer (large-sized model) \n\nVision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 224x224. It was introduced in the paper An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale by Dosovitskiy et al. and first released in this repository. However, the weights were converted from the timm repository by Ross Wightman, who already converted the weights from JAX to PyTorch. Credits go to him. \n\nDisclaimer: The team releasing ViT did not write a model card for this model so this model card has been written by the Hugging Face team.",
"## Model description\n\nThe Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels. Next, the model was fine-tuned on ImageNet (also referred to as ILSVRC2012), a dataset comprising 1 million images and 1,000 classes, at the same resolution, 224x224.\n\nImages are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder.\n\nBy pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image.",
"## Intended uses & limitations\n\nYou can use the raw model for image classification. See the model hub to look for\nfine-tuned versions on a task that interests you.",
"### How to use\n\nHere is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:\n\n\n\nCurrently, both the feature extractor and model support PyTorch. Tensorflow and JAX/FLAX are coming soon, and the API of ViTFeatureExtractor might change.",
"## Training data\n\nThe ViT model was pretrained on ImageNet-21k, a dataset consisting of 14 million images and 21k classes, and fine-tuned on ImageNet, a dataset consisting of 1 million images and 1k classes.",
"## Training procedure",
"### Preprocessing\n\nThe exact details of preprocessing of images during training/validation can be found here. \n\nImages are resized/rescaled to the same resolution (224x224) and normalized across the RGB channels with mean (0.5, 0.5, 0.5) and standard deviation (0.5, 0.5, 0.5).",
"### Pretraining\n\nThe model was trained on TPUv3 hardware (8 cores). All model variants are trained with a batch size of 4096 and learning rate warmup of 10k steps. For ImageNet, the authors found it beneficial to additionally apply gradient clipping at global norm 1. Pre-training resolution is 224.",
"## Evaluation results\n\nFor evaluation results on several image classification benchmarks, we refer to tables 2 and 5 of the original paper. Note that for fine-tuning, the best results are obtained with a higher resolution (384x384). Of course, increasing the model size will result in better performance.",
"### BibTeX entry and citation info"
] |
[
"TAGS\n#transformers #pytorch #tf #jax #vit #image-classification #vision #dataset-imagenet-1k #dataset-imagenet-21k #arxiv-2010.11929 #arxiv-2006.03677 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Vision Transformer (large-sized model) \n\nVision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 224x224. It was introduced in the paper An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale by Dosovitskiy et al. and first released in this repository. However, the weights were converted from the timm repository by Ross Wightman, who already converted the weights from JAX to PyTorch. Credits go to him. \n\nDisclaimer: The team releasing ViT did not write a model card for this model so this model card has been written by the Hugging Face team.",
"## Model description\n\nThe Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels. Next, the model was fine-tuned on ImageNet (also referred to as ILSVRC2012), a dataset comprising 1 million images and 1,000 classes, at the same resolution, 224x224.\n\nImages are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder.\n\nBy pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image.",
"## Intended uses & limitations\n\nYou can use the raw model for image classification. See the model hub to look for\nfine-tuned versions on a task that interests you.",
"### How to use\n\nHere is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:\n\n\n\nCurrently, both the feature extractor and model support PyTorch. Tensorflow and JAX/FLAX are coming soon, and the API of ViTFeatureExtractor might change.",
"## Training data\n\nThe ViT model was pretrained on ImageNet-21k, a dataset consisting of 14 million images and 21k classes, and fine-tuned on ImageNet, a dataset consisting of 1 million images and 1k classes.",
"## Training procedure",
"### Preprocessing\n\nThe exact details of preprocessing of images during training/validation can be found here. \n\nImages are resized/rescaled to the same resolution (224x224) and normalized across the RGB channels with mean (0.5, 0.5, 0.5) and standard deviation (0.5, 0.5, 0.5).",
"### Pretraining\n\nThe model was trained on TPUv3 hardware (8 cores). All model variants are trained with a batch size of 4096 and learning rate warmup of 10k steps. For ImageNet, the authors found it beneficial to additionally apply gradient clipping at global norm 1. Pre-training resolution is 224.",
"## Evaluation results\n\nFor evaluation results on several image classification benchmarks, we refer to tables 2 and 5 of the original paper. Note that for fine-tuning, the best results are obtained with a higher resolution (384x384). Of course, increasing the model size will result in better performance.",
"### BibTeX entry and citation info"
] |
[
89,
188,
301,
41,
76,
54,
3,
70,
73,
64,
11
] |
[
"passage: TAGS\n#transformers #pytorch #tf #jax #vit #image-classification #vision #dataset-imagenet-1k #dataset-imagenet-21k #arxiv-2010.11929 #arxiv-2006.03677 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n# Vision Transformer (large-sized model) \n\nVision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 224x224. It was introduced in the paper An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale by Dosovitskiy et al. and first released in this repository. However, the weights were converted from the timm repository by Ross Wightman, who already converted the weights from JAX to PyTorch. Credits go to him. \n\nDisclaimer: The team releasing ViT did not write a model card for this model so this model card has been written by the Hugging Face team."
] |
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null | null |
transformers
|
# Vision Transformer (large-sized model)
Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 384x384. It was introduced in the paper [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://arxiv.org/abs/2010.11929) by Dosovitskiy et al. and first released in [this repository](https://github.com/google-research/vision_transformer). However, the weights were converted from the [timm repository](https://github.com/rwightman/pytorch-image-models) by Ross Wightman, who already converted the weights from JAX to PyTorch. Credits go to him.
Disclaimer: The team releasing ViT did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels. Next, the model was fine-tuned on ImageNet (also referred to as ILSVRC2012), a dataset comprising 1 million images and 1,000 classes, at a higher resolution of 384x384.
Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder.
By pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image.
## Intended uses & limitations
You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=google/vit) to look for
fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
```python
from transformers import ViTFeatureExtractor, ViTForImageClassification
from PIL import Image
import requests
url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
image = Image.open(requests.get(url, stream=True).raw)
feature_extractor = ViTFeatureExtractor.from_pretrained('google/vit-large-patch16-384')
model = ViTForImageClassification.from_pretrained('google/vit-large-patch16-384')
inputs = feature_extractor(images=image, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
# model predicts one of the 1000 ImageNet classes
predicted_class_idx = logits.argmax(-1).item()
print("Predicted class:", model.config.id2label[predicted_class_idx])
```
Currently, both the feature extractor and model support PyTorch. Tensorflow and JAX/FLAX are coming soon, and the API of ViTFeatureExtractor might change.
## Training data
The ViT model was pretrained on [ImageNet-21k](http://www.image-net.org/), a dataset consisting of 14 million images and 21k classes, and fine-tuned on [ImageNet](http://www.image-net.org/challenges/LSVRC/2012/), a dataset consisting of 1 million images and 1k classes.
## Training procedure
### Preprocessing
The exact details of preprocessing of images during training/validation can be found [here](https://github.com/google-research/vision_transformer/blob/master/vit_jax/input_pipeline.py).
Images are resized/rescaled to the same resolution (224x224 during pre-training, 384x384 during fine-tuning) and normalized across the RGB channels with mean (0.5, 0.5, 0.5) and standard deviation (0.5, 0.5, 0.5).
### Pretraining
The model was trained on TPUv3 hardware (8 cores). All model variants are trained with a batch size of 4096 and learning rate warmup of 10k steps. For ImageNet, the authors found it beneficial to additionally apply gradient clipping at global norm 1. Pre-training resolution is 224.
## Evaluation results
For evaluation results on several image classification benchmarks, we refer to tables 2 and 5 of the original paper. Note that for fine-tuning, the best results are obtained with a higher resolution (384x384). Of course, increasing the model size will result in better performance.
### BibTeX entry and citation info
```bibtex
@misc{wu2020visual,
title={Visual Transformers: Token-based Image Representation and Processing for Computer Vision},
author={Bichen Wu and Chenfeng Xu and Xiaoliang Dai and Alvin Wan and Peizhao Zhang and Zhicheng Yan and Masayoshi Tomizuka and Joseph Gonzalez and Kurt Keutzer and Peter Vajda},
year={2020},
eprint={2006.03677},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
```bibtex
@inproceedings{deng2009imagenet,
title={Imagenet: A large-scale hierarchical image database},
author={Deng, Jia and Dong, Wei and Socher, Richard and Li, Li-Jia and Li, Kai and Fei-Fei, Li},
booktitle={2009 IEEE conference on computer vision and pattern recognition},
pages={248--255},
year={2009},
organization={Ieee}
}
```
|
{"license": "apache-2.0", "tags": ["image-classification", "vision"], "datasets": ["imagenet", "imagenet-21k"]}
|
image-classification
|
google/vit-large-patch16-384
|
[
"transformers",
"pytorch",
"tf",
"jax",
"vit",
"image-classification",
"vision",
"dataset:imagenet",
"dataset:imagenet-21k",
"arxiv:2010.11929",
"arxiv:2006.03677",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2010.11929",
"2006.03677"
] |
[] |
TAGS
#transformers #pytorch #tf #jax #vit #image-classification #vision #dataset-imagenet #dataset-imagenet-21k #arxiv-2010.11929 #arxiv-2006.03677 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Vision Transformer (large-sized model)
Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 384x384. It was introduced in the paper An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale by Dosovitskiy et al. and first released in this repository. However, the weights were converted from the timm repository by Ross Wightman, who already converted the weights from JAX to PyTorch. Credits go to him.
Disclaimer: The team releasing ViT did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels. Next, the model was fine-tuned on ImageNet (also referred to as ILSVRC2012), a dataset comprising 1 million images and 1,000 classes, at a higher resolution of 384x384.
Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder.
By pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image.
## Intended uses & limitations
You can use the raw model for image classification. See the model hub to look for
fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
Currently, both the feature extractor and model support PyTorch. Tensorflow and JAX/FLAX are coming soon, and the API of ViTFeatureExtractor might change.
## Training data
The ViT model was pretrained on ImageNet-21k, a dataset consisting of 14 million images and 21k classes, and fine-tuned on ImageNet, a dataset consisting of 1 million images and 1k classes.
## Training procedure
### Preprocessing
The exact details of preprocessing of images during training/validation can be found here.
Images are resized/rescaled to the same resolution (224x224 during pre-training, 384x384 during fine-tuning) and normalized across the RGB channels with mean (0.5, 0.5, 0.5) and standard deviation (0.5, 0.5, 0.5).
### Pretraining
The model was trained on TPUv3 hardware (8 cores). All model variants are trained with a batch size of 4096 and learning rate warmup of 10k steps. For ImageNet, the authors found it beneficial to additionally apply gradient clipping at global norm 1. Pre-training resolution is 224.
## Evaluation results
For evaluation results on several image classification benchmarks, we refer to tables 2 and 5 of the original paper. Note that for fine-tuning, the best results are obtained with a higher resolution (384x384). Of course, increasing the model size will result in better performance.
### BibTeX entry and citation info
|
[
"# Vision Transformer (large-sized model) \n\nVision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 384x384. It was introduced in the paper An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale by Dosovitskiy et al. and first released in this repository. However, the weights were converted from the timm repository by Ross Wightman, who already converted the weights from JAX to PyTorch. Credits go to him. \n\nDisclaimer: The team releasing ViT did not write a model card for this model so this model card has been written by the Hugging Face team.",
"## Model description\n\nThe Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels. Next, the model was fine-tuned on ImageNet (also referred to as ILSVRC2012), a dataset comprising 1 million images and 1,000 classes, at a higher resolution of 384x384.\n\nImages are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder.\n\nBy pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image.",
"## Intended uses & limitations\n\nYou can use the raw model for image classification. See the model hub to look for\nfine-tuned versions on a task that interests you.",
"### How to use\n\nHere is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:\n\n\n\nCurrently, both the feature extractor and model support PyTorch. Tensorflow and JAX/FLAX are coming soon, and the API of ViTFeatureExtractor might change.",
"## Training data\n\nThe ViT model was pretrained on ImageNet-21k, a dataset consisting of 14 million images and 21k classes, and fine-tuned on ImageNet, a dataset consisting of 1 million images and 1k classes.",
"## Training procedure",
"### Preprocessing\n\nThe exact details of preprocessing of images during training/validation can be found here. \n\nImages are resized/rescaled to the same resolution (224x224 during pre-training, 384x384 during fine-tuning) and normalized across the RGB channels with mean (0.5, 0.5, 0.5) and standard deviation (0.5, 0.5, 0.5).",
"### Pretraining\n\nThe model was trained on TPUv3 hardware (8 cores). All model variants are trained with a batch size of 4096 and learning rate warmup of 10k steps. For ImageNet, the authors found it beneficial to additionally apply gradient clipping at global norm 1. Pre-training resolution is 224.",
"## Evaluation results\n\nFor evaluation results on several image classification benchmarks, we refer to tables 2 and 5 of the original paper. Note that for fine-tuning, the best results are obtained with a higher resolution (384x384). Of course, increasing the model size will result in better performance.",
"### BibTeX entry and citation info"
] |
[
"TAGS\n#transformers #pytorch #tf #jax #vit #image-classification #vision #dataset-imagenet #dataset-imagenet-21k #arxiv-2010.11929 #arxiv-2006.03677 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Vision Transformer (large-sized model) \n\nVision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 384x384. It was introduced in the paper An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale by Dosovitskiy et al. and first released in this repository. However, the weights were converted from the timm repository by Ross Wightman, who already converted the weights from JAX to PyTorch. Credits go to him. \n\nDisclaimer: The team releasing ViT did not write a model card for this model so this model card has been written by the Hugging Face team.",
"## Model description\n\nThe Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels. Next, the model was fine-tuned on ImageNet (also referred to as ILSVRC2012), a dataset comprising 1 million images and 1,000 classes, at a higher resolution of 384x384.\n\nImages are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder.\n\nBy pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image.",
"## Intended uses & limitations\n\nYou can use the raw model for image classification. See the model hub to look for\nfine-tuned versions on a task that interests you.",
"### How to use\n\nHere is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:\n\n\n\nCurrently, both the feature extractor and model support PyTorch. Tensorflow and JAX/FLAX are coming soon, and the API of ViTFeatureExtractor might change.",
"## Training data\n\nThe ViT model was pretrained on ImageNet-21k, a dataset consisting of 14 million images and 21k classes, and fine-tuned on ImageNet, a dataset consisting of 1 million images and 1k classes.",
"## Training procedure",
"### Preprocessing\n\nThe exact details of preprocessing of images during training/validation can be found here. \n\nImages are resized/rescaled to the same resolution (224x224 during pre-training, 384x384 during fine-tuning) and normalized across the RGB channels with mean (0.5, 0.5, 0.5) and standard deviation (0.5, 0.5, 0.5).",
"### Pretraining\n\nThe model was trained on TPUv3 hardware (8 cores). All model variants are trained with a batch size of 4096 and learning rate warmup of 10k steps. For ImageNet, the authors found it beneficial to additionally apply gradient clipping at global norm 1. Pre-training resolution is 224.",
"## Evaluation results\n\nFor evaluation results on several image classification benchmarks, we refer to tables 2 and 5 of the original paper. Note that for fine-tuning, the best results are obtained with a higher resolution (384x384). Of course, increasing the model size will result in better performance.",
"### BibTeX entry and citation info"
] |
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[
"passage: TAGS\n#transformers #pytorch #tf #jax #vit #image-classification #vision #dataset-imagenet #dataset-imagenet-21k #arxiv-2010.11929 #arxiv-2006.03677 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# Vision Transformer (large-sized model) \n\nVision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 384x384. It was introduced in the paper An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale by Dosovitskiy et al. and first released in this repository. However, the weights were converted from the timm repository by Ross Wightman, who already converted the weights from JAX to PyTorch. Credits go to him. \n\nDisclaimer: The team releasing ViT did not write a model card for this model so this model card has been written by the Hugging Face team."
] |
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null | null |
transformers
|
# Vision Transformer (large-sized model)
Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224. It was introduced in the paper [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://arxiv.org/abs/2010.11929) by Dosovitskiy et al. and first released in [this repository](https://github.com/google-research/vision_transformer). However, the weights were converted from the [timm repository](https://github.com/rwightman/pytorch-image-models) by Ross Wightman, who already converted the weights from JAX to PyTorch. Credits go to him.
Disclaimer: The team releasing ViT did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels.
Images are presented to the model as a sequence of fixed-size patches (resolution 32x32), which are linearly embedded. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder.
Note that this model does not provide any fine-tuned heads, as these were zero'd by Google researchers. However, the model does include the pre-trained pooler, which can be used for downstream tasks (such as image classification).
By pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image.
## Intended uses & limitations
You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=google/vit) to look for
fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model:
```python
from transformers import ViTFeatureExtractor, ViTModel
from PIL import Image
import requests
url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
image = Image.open(requests.get(url, stream=True).raw)
feature_extractor = ViTFeatureExtractor.from_pretrained('google/vit-base-patch16-224-in21k')
model = ViTModel.from_pretrained('google/vit-base-patch16-224-in21k')
inputs = feature_extractor(images=image, return_tensors="pt")
outputs = model(**inputs)
last_hidden_state = outputs.last_hidden_state
```
Currently, both the feature extractor and model support PyTorch. Tensorflow and JAX/FLAX are coming soon, and the API of ViTFeatureExtractor might change.
## Training data
The ViT model was pretrained on [ImageNet-21k](http://www.image-net.org/), a dataset consisting of 14 million images and 21k classes.
## Training procedure
### Preprocessing
The exact details of preprocessing of images during training/validation can be found [here](https://github.com/google-research/vision_transformer/blob/master/vit_jax/input_pipeline.py).
Images are resized/rescaled to the same resolution (224x224) and normalized across the RGB channels with mean (0.5, 0.5, 0.5) and standard deviation (0.5, 0.5, 0.5).
### Pretraining
The model was trained on TPUv3 hardware (8 cores). All model variants are trained with a batch size of 4096 and learning rate warmup of 10k steps. For ImageNet, the authors found it beneficial to additionally apply gradient clipping at global norm 1. Pre-training resolution is 224.
## Evaluation results
For evaluation results on several image classification benchmarks, we refer to tables 2 and 5 of the original paper. Note that for fine-tuning, the best results are obtained with a higher resolution (384x384). Of course, increasing the model size will result in better performance.
### BibTeX entry and citation info
```bibtex
@misc{wu2020visual,
title={Visual Transformers: Token-based Image Representation and Processing for Computer Vision},
author={Bichen Wu and Chenfeng Xu and Xiaoliang Dai and Alvin Wan and Peizhao Zhang and Zhicheng Yan and Masayoshi Tomizuka and Joseph Gonzalez and Kurt Keutzer and Peter Vajda},
year={2020},
eprint={2006.03677},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
```bibtex
@inproceedings{deng2009imagenet,
title={Imagenet: A large-scale hierarchical image database},
author={Deng, Jia and Dong, Wei and Socher, Richard and Li, Li-Jia and Li, Kai and Fei-Fei, Li},
booktitle={2009 IEEE conference on computer vision and pattern recognition},
pages={248--255},
year={2009},
organization={Ieee}
}
```
|
{"license": "apache-2.0", "tags": ["vision"], "datasets": ["imagenet-21k"], "inference": false}
|
feature-extraction
|
google/vit-large-patch32-224-in21k
|
[
"transformers",
"pytorch",
"tf",
"jax",
"vit",
"feature-extraction",
"vision",
"dataset:imagenet-21k",
"arxiv:2010.11929",
"arxiv:2006.03677",
"license:apache-2.0",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2010.11929",
"2006.03677"
] |
[] |
TAGS
#transformers #pytorch #tf #jax #vit #feature-extraction #vision #dataset-imagenet-21k #arxiv-2010.11929 #arxiv-2006.03677 #license-apache-2.0 #has_space #region-us
|
# Vision Transformer (large-sized model)
Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224. It was introduced in the paper An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale by Dosovitskiy et al. and first released in this repository. However, the weights were converted from the timm repository by Ross Wightman, who already converted the weights from JAX to PyTorch. Credits go to him.
Disclaimer: The team releasing ViT did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels.
Images are presented to the model as a sequence of fixed-size patches (resolution 32x32), which are linearly embedded. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder.
Note that this model does not provide any fine-tuned heads, as these were zero'd by Google researchers. However, the model does include the pre-trained pooler, which can be used for downstream tasks (such as image classification).
By pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image.
## Intended uses & limitations
You can use the raw model for image classification. See the model hub to look for
fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model:
Currently, both the feature extractor and model support PyTorch. Tensorflow and JAX/FLAX are coming soon, and the API of ViTFeatureExtractor might change.
## Training data
The ViT model was pretrained on ImageNet-21k, a dataset consisting of 14 million images and 21k classes.
## Training procedure
### Preprocessing
The exact details of preprocessing of images during training/validation can be found here.
Images are resized/rescaled to the same resolution (224x224) and normalized across the RGB channels with mean (0.5, 0.5, 0.5) and standard deviation (0.5, 0.5, 0.5).
### Pretraining
The model was trained on TPUv3 hardware (8 cores). All model variants are trained with a batch size of 4096 and learning rate warmup of 10k steps. For ImageNet, the authors found it beneficial to additionally apply gradient clipping at global norm 1. Pre-training resolution is 224.
## Evaluation results
For evaluation results on several image classification benchmarks, we refer to tables 2 and 5 of the original paper. Note that for fine-tuning, the best results are obtained with a higher resolution (384x384). Of course, increasing the model size will result in better performance.
### BibTeX entry and citation info
|
[
"# Vision Transformer (large-sized model) \n\nVision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224. It was introduced in the paper An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale by Dosovitskiy et al. and first released in this repository. However, the weights were converted from the timm repository by Ross Wightman, who already converted the weights from JAX to PyTorch. Credits go to him. \n\nDisclaimer: The team releasing ViT did not write a model card for this model so this model card has been written by the Hugging Face team.",
"## Model description\n\nThe Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels. \n\nImages are presented to the model as a sequence of fixed-size patches (resolution 32x32), which are linearly embedded. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder.\n\nNote that this model does not provide any fine-tuned heads, as these were zero'd by Google researchers. However, the model does include the pre-trained pooler, which can be used for downstream tasks (such as image classification).\n\nBy pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image.",
"## Intended uses & limitations\n\nYou can use the raw model for image classification. See the model hub to look for\nfine-tuned versions on a task that interests you.",
"### How to use\n\nHere is how to use this model:\n\n\n\nCurrently, both the feature extractor and model support PyTorch. Tensorflow and JAX/FLAX are coming soon, and the API of ViTFeatureExtractor might change.",
"## Training data\n\nThe ViT model was pretrained on ImageNet-21k, a dataset consisting of 14 million images and 21k classes.",
"## Training procedure",
"### Preprocessing\n\nThe exact details of preprocessing of images during training/validation can be found here. \n\nImages are resized/rescaled to the same resolution (224x224) and normalized across the RGB channels with mean (0.5, 0.5, 0.5) and standard deviation (0.5, 0.5, 0.5).",
"### Pretraining\n\nThe model was trained on TPUv3 hardware (8 cores). All model variants are trained with a batch size of 4096 and learning rate warmup of 10k steps. For ImageNet, the authors found it beneficial to additionally apply gradient clipping at global norm 1. Pre-training resolution is 224.",
"## Evaluation results\n\nFor evaluation results on several image classification benchmarks, we refer to tables 2 and 5 of the original paper. Note that for fine-tuning, the best results are obtained with a higher resolution (384x384). Of course, increasing the model size will result in better performance.",
"### BibTeX entry and citation info"
] |
[
"TAGS\n#transformers #pytorch #tf #jax #vit #feature-extraction #vision #dataset-imagenet-21k #arxiv-2010.11929 #arxiv-2006.03677 #license-apache-2.0 #has_space #region-us \n",
"# Vision Transformer (large-sized model) \n\nVision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224. It was introduced in the paper An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale by Dosovitskiy et al. and first released in this repository. However, the weights were converted from the timm repository by Ross Wightman, who already converted the weights from JAX to PyTorch. Credits go to him. \n\nDisclaimer: The team releasing ViT did not write a model card for this model so this model card has been written by the Hugging Face team.",
"## Model description\n\nThe Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels. \n\nImages are presented to the model as a sequence of fixed-size patches (resolution 32x32), which are linearly embedded. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder.\n\nNote that this model does not provide any fine-tuned heads, as these were zero'd by Google researchers. However, the model does include the pre-trained pooler, which can be used for downstream tasks (such as image classification).\n\nBy pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image.",
"## Intended uses & limitations\n\nYou can use the raw model for image classification. See the model hub to look for\nfine-tuned versions on a task that interests you.",
"### How to use\n\nHere is how to use this model:\n\n\n\nCurrently, both the feature extractor and model support PyTorch. Tensorflow and JAX/FLAX are coming soon, and the API of ViTFeatureExtractor might change.",
"## Training data\n\nThe ViT model was pretrained on ImageNet-21k, a dataset consisting of 14 million images and 21k classes.",
"## Training procedure",
"### Preprocessing\n\nThe exact details of preprocessing of images during training/validation can be found here. \n\nImages are resized/rescaled to the same resolution (224x224) and normalized across the RGB channels with mean (0.5, 0.5, 0.5) and standard deviation (0.5, 0.5, 0.5).",
"### Pretraining\n\nThe model was trained on TPUv3 hardware (8 cores). All model variants are trained with a batch size of 4096 and learning rate warmup of 10k steps. For ImageNet, the authors found it beneficial to additionally apply gradient clipping at global norm 1. Pre-training resolution is 224.",
"## Evaluation results\n\nFor evaluation results on several image classification benchmarks, we refer to tables 2 and 5 of the original paper. Note that for fine-tuning, the best results are obtained with a higher resolution (384x384). Of course, increasing the model size will result in better performance.",
"### BibTeX entry and citation info"
] |
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] |
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null | null |
transformers
|
# Vision Transformer (large-sized model)
Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 384x384. It was introduced in the paper [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://arxiv.org/abs/2010.11929) by Dosovitskiy et al. and first released in [this repository](https://github.com/google-research/vision_transformer). However, the weights were converted from the [timm repository](https://github.com/rwightman/pytorch-image-models) by Ross Wightman, who already converted the weights from JAX to PyTorch. Credits go to him.
Disclaimer: The team releasing ViT did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels. Next, the model was fine-tuned on ImageNet (also referred to as ILSVRC2012), a dataset comprising 1 million images and 1,000 classes, at a higher resolution of 384x384.
Images are presented to the model as a sequence of fixed-size patches (resolution 32x32), which are linearly embedded. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder.
By pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image.
## Intended uses & limitations
You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=google/vit) to look for
fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
```python
from transformers import ViTFeatureExtractor, ViTForImageClassification
from PIL import Image
import requests
url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
image = Image.open(requests.get(url, stream=True).raw)
feature_extractor = ViTFeatureExtractor.from_pretrained('google/vit-large-patch32-384')
model = ViTForImageClassification.from_pretrained('google/vit-large-patch32-384')
inputs = feature_extractor(images=image, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
# model predicts one of the 1000 ImageNet classes
predicted_class_idx = logits.argmax(-1).item()
print("Predicted class:", model.config.id2label[predicted_class_idx])
```
Currently, both the feature extractor and model support PyTorch. Tensorflow and JAX/FLAX are coming soon, and the API of ViTFeatureExtractor might change.
## Training data
The ViT model was pretrained on [ImageNet-21k](http://www.image-net.org/), a dataset consisting of 14 million images and 21k classes, and fine-tuned on [ImageNet](http://www.image-net.org/challenges/LSVRC/2012/), a dataset consisting of 1 million images and 1k classes.
## Training procedure
### Preprocessing
The exact details of preprocessing of images during training/validation can be found [here](https://github.com/google-research/vision_transformer/blob/master/vit_jax/input_pipeline.py).
Images are resized/rescaled to the same resolution (224x224 during pre-training, 384x384 during fine-tuning) and normalized across the RGB channels with mean (0.5, 0.5, 0.5) and standard deviation (0.5, 0.5, 0.5).
### Pretraining
The model was trained on TPUv3 hardware (8 cores). All model variants are trained with a batch size of 4096 and learning rate warmup of 10k steps. For ImageNet, the authors found it beneficial to additionally apply gradient clipping at global norm 1. Pre-training resolution is 224.
## Evaluation results
For evaluation results on several image classification benchmarks, we refer to tables 2 and 5 of the original paper. Note that for fine-tuning, the best results are obtained with a higher resolution (384x384). Of course, increasing the model size will result in better performance.
### BibTeX entry and citation info
```bibtex
@misc{wu2020visual,
title={Visual Transformers: Token-based Image Representation and Processing for Computer Vision},
author={Bichen Wu and Chenfeng Xu and Xiaoliang Dai and Alvin Wan and Peizhao Zhang and Zhicheng Yan and Masayoshi Tomizuka and Joseph Gonzalez and Kurt Keutzer and Peter Vajda},
year={2020},
eprint={2006.03677},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
```bibtex
@inproceedings{deng2009imagenet,
title={Imagenet: A large-scale hierarchical image database},
author={Deng, Jia and Dong, Wei and Socher, Richard and Li, Li-Jia and Li, Kai and Fei-Fei, Li},
booktitle={2009 IEEE conference on computer vision and pattern recognition},
pages={248--255},
year={2009},
organization={Ieee}
}
```
|
{"license": "apache-2.0", "tags": ["image-classification", "vision"], "datasets": ["imagenet", "imagenet-21k"]}
|
image-classification
|
google/vit-large-patch32-384
|
[
"transformers",
"pytorch",
"tf",
"jax",
"vit",
"image-classification",
"vision",
"dataset:imagenet",
"dataset:imagenet-21k",
"arxiv:2010.11929",
"arxiv:2006.03677",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2010.11929",
"2006.03677"
] |
[] |
TAGS
#transformers #pytorch #tf #jax #vit #image-classification #vision #dataset-imagenet #dataset-imagenet-21k #arxiv-2010.11929 #arxiv-2006.03677 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Vision Transformer (large-sized model)
Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 384x384. It was introduced in the paper An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale by Dosovitskiy et al. and first released in this repository. However, the weights were converted from the timm repository by Ross Wightman, who already converted the weights from JAX to PyTorch. Credits go to him.
Disclaimer: The team releasing ViT did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels. Next, the model was fine-tuned on ImageNet (also referred to as ILSVRC2012), a dataset comprising 1 million images and 1,000 classes, at a higher resolution of 384x384.
Images are presented to the model as a sequence of fixed-size patches (resolution 32x32), which are linearly embedded. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder.
By pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image.
## Intended uses & limitations
You can use the raw model for image classification. See the model hub to look for
fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
Currently, both the feature extractor and model support PyTorch. Tensorflow and JAX/FLAX are coming soon, and the API of ViTFeatureExtractor might change.
## Training data
The ViT model was pretrained on ImageNet-21k, a dataset consisting of 14 million images and 21k classes, and fine-tuned on ImageNet, a dataset consisting of 1 million images and 1k classes.
## Training procedure
### Preprocessing
The exact details of preprocessing of images during training/validation can be found here.
Images are resized/rescaled to the same resolution (224x224 during pre-training, 384x384 during fine-tuning) and normalized across the RGB channels with mean (0.5, 0.5, 0.5) and standard deviation (0.5, 0.5, 0.5).
### Pretraining
The model was trained on TPUv3 hardware (8 cores). All model variants are trained with a batch size of 4096 and learning rate warmup of 10k steps. For ImageNet, the authors found it beneficial to additionally apply gradient clipping at global norm 1. Pre-training resolution is 224.
## Evaluation results
For evaluation results on several image classification benchmarks, we refer to tables 2 and 5 of the original paper. Note that for fine-tuning, the best results are obtained with a higher resolution (384x384). Of course, increasing the model size will result in better performance.
### BibTeX entry and citation info
|
[
"# Vision Transformer (large-sized model) \n\nVision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 384x384. It was introduced in the paper An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale by Dosovitskiy et al. and first released in this repository. However, the weights were converted from the timm repository by Ross Wightman, who already converted the weights from JAX to PyTorch. Credits go to him. \n\nDisclaimer: The team releasing ViT did not write a model card for this model so this model card has been written by the Hugging Face team.",
"## Model description\n\nThe Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels. Next, the model was fine-tuned on ImageNet (also referred to as ILSVRC2012), a dataset comprising 1 million images and 1,000 classes, at a higher resolution of 384x384.\n\nImages are presented to the model as a sequence of fixed-size patches (resolution 32x32), which are linearly embedded. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder.\n\nBy pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image.",
"## Intended uses & limitations\n\nYou can use the raw model for image classification. See the model hub to look for\nfine-tuned versions on a task that interests you.",
"### How to use\n\nHere is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:\n\n\n\nCurrently, both the feature extractor and model support PyTorch. Tensorflow and JAX/FLAX are coming soon, and the API of ViTFeatureExtractor might change.",
"## Training data\n\nThe ViT model was pretrained on ImageNet-21k, a dataset consisting of 14 million images and 21k classes, and fine-tuned on ImageNet, a dataset consisting of 1 million images and 1k classes.",
"## Training procedure",
"### Preprocessing\n\nThe exact details of preprocessing of images during training/validation can be found here. \n\nImages are resized/rescaled to the same resolution (224x224 during pre-training, 384x384 during fine-tuning) and normalized across the RGB channels with mean (0.5, 0.5, 0.5) and standard deviation (0.5, 0.5, 0.5).",
"### Pretraining\n\nThe model was trained on TPUv3 hardware (8 cores). All model variants are trained with a batch size of 4096 and learning rate warmup of 10k steps. For ImageNet, the authors found it beneficial to additionally apply gradient clipping at global norm 1. Pre-training resolution is 224.",
"## Evaluation results\n\nFor evaluation results on several image classification benchmarks, we refer to tables 2 and 5 of the original paper. Note that for fine-tuning, the best results are obtained with a higher resolution (384x384). Of course, increasing the model size will result in better performance.",
"### BibTeX entry and citation info"
] |
[
"TAGS\n#transformers #pytorch #tf #jax #vit #image-classification #vision #dataset-imagenet #dataset-imagenet-21k #arxiv-2010.11929 #arxiv-2006.03677 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Vision Transformer (large-sized model) \n\nVision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 384x384. It was introduced in the paper An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale by Dosovitskiy et al. and first released in this repository. However, the weights were converted from the timm repository by Ross Wightman, who already converted the weights from JAX to PyTorch. Credits go to him. \n\nDisclaimer: The team releasing ViT did not write a model card for this model so this model card has been written by the Hugging Face team.",
"## Model description\n\nThe Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels. Next, the model was fine-tuned on ImageNet (also referred to as ILSVRC2012), a dataset comprising 1 million images and 1,000 classes, at a higher resolution of 384x384.\n\nImages are presented to the model as a sequence of fixed-size patches (resolution 32x32), which are linearly embedded. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder.\n\nBy pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image.",
"## Intended uses & limitations\n\nYou can use the raw model for image classification. See the model hub to look for\nfine-tuned versions on a task that interests you.",
"### How to use\n\nHere is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:\n\n\n\nCurrently, both the feature extractor and model support PyTorch. Tensorflow and JAX/FLAX are coming soon, and the API of ViTFeatureExtractor might change.",
"## Training data\n\nThe ViT model was pretrained on ImageNet-21k, a dataset consisting of 14 million images and 21k classes, and fine-tuned on ImageNet, a dataset consisting of 1 million images and 1k classes.",
"## Training procedure",
"### Preprocessing\n\nThe exact details of preprocessing of images during training/validation can be found here. \n\nImages are resized/rescaled to the same resolution (224x224 during pre-training, 384x384 during fine-tuning) and normalized across the RGB channels with mean (0.5, 0.5, 0.5) and standard deviation (0.5, 0.5, 0.5).",
"### Pretraining\n\nThe model was trained on TPUv3 hardware (8 cores). All model variants are trained with a batch size of 4096 and learning rate warmup of 10k steps. For ImageNet, the authors found it beneficial to additionally apply gradient clipping at global norm 1. Pre-training resolution is 224.",
"## Evaluation results\n\nFor evaluation results on several image classification benchmarks, we refer to tables 2 and 5 of the original paper. Note that for fine-tuning, the best results are obtained with a higher resolution (384x384). Of course, increasing the model size will result in better performance.",
"### BibTeX entry and citation info"
] |
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] |
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null | null |
transformers
|
# Suicidal-ELECTRA
This text classification model predicts whether a sequence of words are suicidal (1) or non-suicidal (0).
## Data
The model was trained on the [Suicide and Depression Dataset](https://www.kaggle.com/nikhileswarkomati/suicide-watch) obtained from Kaggle. The dataset was scraped from Reddit and consists of 232,074 rows equally distributed between 2 classes - suicide and non-suicide.
## Parameters
The model fine-tuning was conducted on 1 epoch, with batch size of 6, and learning rate of 0.00001. Due to limited computing resources and time, we were unable to scale up the number of epochs and batch size.
## Performance
The model has achieved the following results after fine-tuning on the aforementioned dataset:
- Accuracy: 0.9792
- Recall: 0.9788
- Precision: 0.9677
- F1 Score: 0.9732
## How to Use
Load the model via the transformers library:
```
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("gooohjy/suicidal-electra")
model = AutoModel.from_pretrained("gooohjy/suicidal-electra")
```
## Resources
For more resources, including the source code, please refer to the GitHub repository [gohjiayi/suicidal-text-detection](https://github.com/gohjiayi/suicidal-text-detection/).
|
{}
|
text-classification
|
gooohjy/suicidal-electra
|
[
"transformers",
"pytorch",
"electra",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #electra #text-classification #autotrain_compatible #endpoints_compatible #region-us
|
# Suicidal-ELECTRA
This text classification model predicts whether a sequence of words are suicidal (1) or non-suicidal (0).
## Data
The model was trained on the Suicide and Depression Dataset obtained from Kaggle. The dataset was scraped from Reddit and consists of 232,074 rows equally distributed between 2 classes - suicide and non-suicide.
## Parameters
The model fine-tuning was conducted on 1 epoch, with batch size of 6, and learning rate of 0.00001. Due to limited computing resources and time, we were unable to scale up the number of epochs and batch size.
## Performance
The model has achieved the following results after fine-tuning on the aforementioned dataset:
- Accuracy: 0.9792
- Recall: 0.9788
- Precision: 0.9677
- F1 Score: 0.9732
## How to Use
Load the model via the transformers library:
## Resources
For more resources, including the source code, please refer to the GitHub repository gohjiayi/suicidal-text-detection.
|
[
"# Suicidal-ELECTRA\nThis text classification model predicts whether a sequence of words are suicidal (1) or non-suicidal (0).",
"## Data\nThe model was trained on the Suicide and Depression Dataset obtained from Kaggle. The dataset was scraped from Reddit and consists of 232,074 rows equally distributed between 2 classes - suicide and non-suicide.",
"## Parameters\nThe model fine-tuning was conducted on 1 epoch, with batch size of 6, and learning rate of 0.00001. Due to limited computing resources and time, we were unable to scale up the number of epochs and batch size.",
"## Performance\nThe model has achieved the following results after fine-tuning on the aforementioned dataset:\n- Accuracy: 0.9792\n- Recall: 0.9788\n- Precision: 0.9677\n- F1 Score: 0.9732",
"## How to Use\nLoad the model via the transformers library:",
"## Resources\nFor more resources, including the source code, please refer to the GitHub repository gohjiayi/suicidal-text-detection."
] |
[
"TAGS\n#transformers #pytorch #electra #text-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# Suicidal-ELECTRA\nThis text classification model predicts whether a sequence of words are suicidal (1) or non-suicidal (0).",
"## Data\nThe model was trained on the Suicide and Depression Dataset obtained from Kaggle. The dataset was scraped from Reddit and consists of 232,074 rows equally distributed between 2 classes - suicide and non-suicide.",
"## Parameters\nThe model fine-tuning was conducted on 1 epoch, with batch size of 6, and learning rate of 0.00001. Due to limited computing resources and time, we were unable to scale up the number of epochs and batch size.",
"## Performance\nThe model has achieved the following results after fine-tuning on the aforementioned dataset:\n- Accuracy: 0.9792\n- Recall: 0.9788\n- Precision: 0.9677\n- F1 Score: 0.9732",
"## How to Use\nLoad the model via the transformers library:",
"## Resources\nFor more resources, including the source code, please refer to the GitHub repository gohjiayi/suicidal-text-detection."
] |
[
37,
34,
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36
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[
"passage: TAGS\n#transformers #pytorch #electra #text-classification #autotrain_compatible #endpoints_compatible #region-us \n# Suicidal-ELECTRA\nThis text classification model predicts whether a sequence of words are suicidal (1) or non-suicidal (0).## Data\nThe model was trained on the Suicide and Depression Dataset obtained from Kaggle. The dataset was scraped from Reddit and consists of 232,074 rows equally distributed between 2 classes - suicide and non-suicide.## Parameters\nThe model fine-tuning was conducted on 1 epoch, with batch size of 6, and learning rate of 0.00001. Due to limited computing resources and time, we were unable to scale up the number of epochs and batch size.## Performance\nThe model has achieved the following results after fine-tuning on the aforementioned dataset:\n- Accuracy: 0.9792\n- Recall: 0.9788\n- Precision: 0.9677\n- F1 Score: 0.9732## How to Use\nLoad the model via the transformers library:## Resources\nFor more resources, including the source code, please refer to the GitHub repository gohjiayi/suicidal-text-detection."
] |
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null | null | null |
https://www.geogebra.org/m/awcxgj4g
https://www.geogebra.org/m/tx9tme6s
https://www.geogebra.org/m/yx5yyjmx
|
{}
| null |
gorave/gorave
|
[
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2022-03-02T23:29:05+00:00
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[] |
[] |
TAGS
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|
URL
URL
URL
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[] |
[
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6
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[
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null | null |
transformers
|
# Turkish GPT2 Model Finetuned
# Türkçe GPT2 Modeli
## Model description
This is a GPT2-Small English based model finetuned and additionaly trainied with Wikipedia Articles in Turkish as of 28-10-2020
Live demo based on this work at : https://www.metayazar.com/
Fine tuned writer on this model: https://huggingface.co/gorkemgoknar/gpt2-turkish-writer
Work has been done on Pierre Guillou tutorial as on this page.
(https://github.com/piegu/fastai-projects/blob/master/finetuning-English-GPT2-any-language-Portuguese-HuggingFace-fastaiv2.ipynb)
Code is converted to work with Fastai 2.X .
Using Google Colab for training.
Additional tutorial and source will be in https://github.com/gorkemgoknar in later stage.
Current accuracy 33 % , Perplexity : 51.88
Models are available:
* [gpt2-small-tuned-tr] (https://huggingface.co/gorkemgoknar/gpt2-small-turkish)
* [gpt2-small-turkish-writer] (https://huggingface.co/gorkemgoknar/gpt2-turkish-writer)
## Intended uses & limitations
#### How to use
#### Install
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
import torch
tokenizer = AutoTokenizer.from_pretrained("gorkemgoknar/gpt2-small-turkish")
model = AutoModelWithLMHead.from_pretrained("gorkemgoknar/gpt2-small-turkish")
# Get sequence length max of 1024
tokenizer.model_max_length=1024
model.eval() # disable dropout (or leave in train mode to finetune)
```
#### Generate 1 word
```python
# input sequence
text = "Bu yazıyı bilgisayar yazdı."
inputs = tokenizer(text, return_tensors="pt")
# model output
outputs = model(**inputs, labels=inputs["input_ids"])
loss, logits = outputs[:2]
predicted_index = torch.argmax(logits[0, -1, :]).item()
predicted_text = tokenizer.decode([predicted_index])
# results
print('input text:', text)
print('predicted text:', predicted_text)
# input text:
# predicted text:
```
#### Generate Full Sequence
```python
# input sequence
text = "Bu yazıyı bilgisayar yazdı."
inputs = tokenizer(text, return_tensors="pt")
# model output using Top-k sampling text generation method
sample_outputs = model.generate(inputs.input_ids,
pad_token_id=50256,
do_sample=True,
max_length=50, # put the token number you want
top_k=40,
num_return_sequences=1)
# generated sequence
for i, sample_output in enumerate(sample_outputs):
print(">> Generated text {}\\\\
\\\\
{}".format(i+1, tokenizer.decode(sample_output.tolist())))
# >> Generated text
#
```
#### Limitations and bias
The training data used for this model come from Turkish Wikipedia. We know it contains a lot of unfiltered content from the internet, which is far from neutral.
## Training data
Wikipedia Turkish article dump as of 28-10-2020
## Training procedure
## Eval results
| epoch\\\\t|train_loss\\\\t|valid_loss\\\\t|accuracy\\\\t|perplexity\\\\t|time |
| ----- | -------- |--------- | ---------- | --------- | ----- |
|0\\\\t|4.777015\\\\t|4.621834\\\\t|0.292547\\\\t|101.680367\\\\t|2:42:05|
|1\\\\t|4.509412\\\\t|4.403999\\\\t|0.305574\\\\t|81.777267\\\\t|1:09:38|
|2\\\\t|4.169529\\\\t|4.120755\\\\t|0.324908\\\\t|61.605747\\\\t|1:07:45|
|3\\\\t|4.293973\\\\t|4.177899\\\\t|0.317211\\\\t|65.228653\\\\t|1:07:02|
|4\\\\t|4.049848\\\\t|3.949103\\\\t|0.338347\\\\t|51.888783\\\\t|1:05:53|
#Epoch 0 on Tesla T4, others on V100
```
|
{"language": ["tr"], "license": "apache-2.0", "tags": ["gpt2", "turkish"], "datasets": ["wikipedia-turkish"], "metrics": ["perplexity", "accuracy"], "widget": [{"text": "Bu yaz\u0131y\u0131 bir bilgisayar yazd\u0131. Yazarken", "context": ""}, {"text": "\u0130nternete kolay eri\u015fim sayesinde d\u00fcnya daha da k\u00fc\u00e7\u00fcld\u00fc. Bunun sonucunda", "context": ""}]}
|
text-generation
|
gorkemgoknar/gpt2-small-turkish
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"turkish",
"tr",
"dataset:wikipedia-turkish",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"tr"
] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #turkish #tr #dataset-wikipedia-turkish #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
Turkish GPT2 Model Finetuned
============================
Türkçe GPT2 Modeli
==================
Model description
-----------------
This is a GPT2-Small English based model finetuned and additionaly trainied with Wikipedia Articles in Turkish as of 28-10-2020
Live demo based on this work at : URL
Fine tuned writer on this model: URL
Work has been done on Pierre Guillou tutorial as on this page.
(URL
Code is converted to work with Fastai 2.X .
Using Google Colab for training.
Additional tutorial and source will be in URL in later stage.
Current accuracy 33 % , Perplexity : 51.88
Models are available:
* [gpt2-small-tuned-tr] (URL
* [gpt2-small-turkish-writer] (URL
Intended uses & limitations
---------------------------
#### How to use
#### Install
#### Generate 1 word
#### Generate Full Sequence
#### Limitations and bias
The training data used for this model come from Turkish Wikipedia. We know it contains a lot of unfiltered content from the internet, which is far from neutral.
Training data
-------------
Wikipedia Turkish article dump as of 28-10-2020
Training procedure
------------------
Eval results
------------
#Epoch 0 on Tesla T4, others on V100
'''
|
[
"#### How to use",
"#### Install",
"#### Generate 1 word",
"#### Generate Full Sequence",
"#### Limitations and bias\n\n\nThe training data used for this model come from Turkish Wikipedia. We know it contains a lot of unfiltered content from the internet, which is far from neutral.\n\n\nTraining data\n-------------\n\n\nWikipedia Turkish article dump as of 28-10-2020\n\n\nTraining procedure\n------------------\n\n\nEval results\n------------"
] |
[
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #turkish #tr #dataset-wikipedia-turkish #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"#### How to use",
"#### Install",
"#### Generate 1 word",
"#### Generate Full Sequence",
"#### Limitations and bias\n\n\nThe training data used for this model come from Turkish Wikipedia. We know it contains a lot of unfiltered content from the internet, which is far from neutral.\n\n\nTraining data\n-------------\n\n\nWikipedia Turkish article dump as of 28-10-2020\n\n\nTraining procedure\n------------------\n\n\nEval results\n------------"
] |
[
71,
5,
3,
6,
8,
66
] |
[
"passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #turkish #tr #dataset-wikipedia-turkish #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n#### How to use#### Install#### Generate 1 word#### Generate Full Sequence#### Limitations and bias\n\n\nThe training data used for this model come from Turkish Wikipedia. We know it contains a lot of unfiltered content from the internet, which is far from neutral.\n\n\nTraining data\n-------------\n\n\nWikipedia Turkish article dump as of 28-10-2020\n\n\nTraining procedure\n------------------\n\n\nEval results\n------------"
] |
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] |
null | null |
transformers
|
# Turkish AI Writer based on GPT2-Small
# Türkçe Yapay Zeka Yazarı
## Model description
This model is enhanced version of gpt2-small-turkish finetuned version. In addition to 28-10-2020 Wikipedia Turkish article dump this model is trained with more than 400 classic novels and plays in Turkish (Including Dostoyevski, Shaekspeare, Dumas)
Base work has been done on Pierre Guillou tutorial as on this page.
(https://github.com/piegu/fastai-projects/blob/master/finetuning-English-GPT2-any-language-Portuguese-HuggingFace-fastaiv2.ipynb)
Note that Since Turkish language is not close to English as in Porteguese instead of training last 2 layers, last 3 layers are trained.
Code is converted to work with Fastai 2.X .
Using Google Colab for training.
Current accuracy 36.3 % , Perplexity : 44.75
Demo (using CPU inference) is available on: http://www.metayazar.com
Models are available:
* [gpt2-small-tuned-tr] (https://huggingface.co/gorkemgoknar/gpt2-small-turkish)
* [gpt2-small-turkish-writer] (https://huggingface.co/gorkemgoknar/gpt2-turkish-writer)
## Intended uses & limitations
#### How to use
#### Install
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
import torch
tokenizer = AutoTokenizer.from_pretrained("gorkemgoknar/gpt2-turkish-writer")
model = AutoModelWithLMHead.from_pretrained("gorkemgoknar/gpt2-turkish-writer")
# Get sequence length max of 1024
tokenizer.model_max_length=1024
model.eval() # disable dropout (or leave in train mode to finetune)
```
#### Generate 1 word
```python
# input sequence
text = "Bu yazıyı bilgisayar yazdı."
inputs = tokenizer(text, return_tensors="pt")
# model output
outputs = model(**inputs, labels=inputs["input_ids"])
loss, logits = outputs[:2]
predicted_index = torch.argmax(logits[0, -1, :]).item()
predicted_text = tokenizer.decode([predicted_index])
# results
print('input text:', text)
print('predicted text:', predicted_text)
# input text:
# predicted text:
```
#### Generate Full Sequence
```python
# input sequence
text = "Bu yazıyı bilgisayar yazdı."
inputs = tokenizer(text, return_tensors="pt")
# model output using Top-k sampling text generation method
sample_outputs = model.generate(inputs.input_ids,
pad_token_id=50256,
do_sample=True,
max_length=50, # put the token number you want
top_k=40,
num_return_sequences=1)
# generated sequence
for i, sample_output in enumerate(sample_outputs):
print(">> Generated text {}\n\n{}".format(i+1, tokenizer.decode(sample_output.tolist())))
# >> Generated text
#
```
#### Limitations and bias
The training data used for this model come from Turkish Wikipedia and books. We know it contains a lot of unfiltered content from the internet, which is far from neutral. Also not much pre-processing was done on books hence chapter names and page numbers can be seen on some cases. This is a work in progress.
## Training data
Wikipedia Turkish article dump as of 28-10-2020
Turkish book dataset of >400 classic novels
## Training procedure
## Eval results
| epoch |train_loss |valid_loss |accuracy |perplexity |time |
| ----- | -------- |--------- | ---------- | --------- | ----- |
|0 |4.497828 |4.549605 |0.277328 |94.595070 |2:09:58|
|1 |4.503929 |4.519456 |0.275071 |91.785645 |2:04:30|
|2 |3.612716 |3.921146 |0.344802 |50.458256 |2:03:22|
|3 |3.777645 |4.072006 |0.326130 |58.674530 |1:56:14|
|4 |2.934462 |3.801303 |0.363719 |44.759476 |1:58:55|
Note: 1cycle rule training is used and epochs are at different times
```
|
{"language": ["tr"], "license": "apache-2.0", "tags": ["gpt2", "turkish", "aiwriter", "finetuned"], "datasets": ["wikipedia-turkish", "custom-book-corpus"], "metrics": ["perplexity", "accuracy"], "widget": [{"text": "Bir zaman topu olan ama k\u00f6pe\u011fi olmayan bir \u00e7ocuk vard\u0131. Parkta", "context": ""}, {"text": "Uzun uzun sahile do\u011fru bakt\u0131. D\u00fc\u015f\u00fcnd\u00fcklerinden ", "context": ""}, {"text": "\u00c7ok uzun zaman \u00f6nce galaksinin uzak bir k\u00f6\u015fesinde...", "context": ""}, {"text": "'Bug\u00fcn kendimi \u00e7ok hasta hissediyorum' dedi. Kar\u015f\u0131s\u0131nda ", "context": ""}]}
|
text-generation
|
gorkemgoknar/gpt2-turkish-writer
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"turkish",
"aiwriter",
"finetuned",
"tr",
"dataset:wikipedia-turkish",
"dataset:custom-book-corpus",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"tr"
] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #turkish #aiwriter #finetuned #tr #dataset-wikipedia-turkish #dataset-custom-book-corpus #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
Turkish AI Writer based on GPT2-Small
=====================================
Türkçe Yapay Zeka Yazarı
========================
Model description
-----------------
This model is enhanced version of gpt2-small-turkish finetuned version. In addition to 28-10-2020 Wikipedia Turkish article dump this model is trained with more than 400 classic novels and plays in Turkish (Including Dostoyevski, Shaekspeare, Dumas)
Base work has been done on Pierre Guillou tutorial as on this page.
(URL
Note that Since Turkish language is not close to English as in Porteguese instead of training last 2 layers, last 3 layers are trained.
Code is converted to work with Fastai 2.X .
Using Google Colab for training.
Current accuracy 36.3 % , Perplexity : 44.75
Demo (using CPU inference) is available on: URL
Models are available:
* [gpt2-small-tuned-tr] (URL
* [gpt2-small-turkish-writer] (URL
Intended uses & limitations
---------------------------
#### How to use
#### Install
#### Generate 1 word
#### Generate Full Sequence
#### Limitations and bias
The training data used for this model come from Turkish Wikipedia and books. We know it contains a lot of unfiltered content from the internet, which is far from neutral. Also not much pre-processing was done on books hence chapter names and page numbers can be seen on some cases. This is a work in progress.
Training data
-------------
Wikipedia Turkish article dump as of 28-10-2020
Turkish book dataset of >400 classic novels
Training procedure
------------------
Eval results
------------
Note: 1cycle rule training is used and epochs are at different times
'''
|
[
"#### How to use",
"#### Install",
"#### Generate 1 word",
"#### Generate Full Sequence",
"#### Limitations and bias\n\n\nThe training data used for this model come from Turkish Wikipedia and books. We know it contains a lot of unfiltered content from the internet, which is far from neutral. Also not much pre-processing was done on books hence chapter names and page numbers can be seen on some cases. This is a work in progress.\n\n\nTraining data\n-------------\n\n\nWikipedia Turkish article dump as of 28-10-2020\nTurkish book dataset of >400 classic novels\n\n\nTraining procedure\n------------------\n\n\nEval results\n------------\n\n\n\nNote: 1cycle rule training is used and epochs are at different times\n'''"
] |
[
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #turkish #aiwriter #finetuned #tr #dataset-wikipedia-turkish #dataset-custom-book-corpus #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"#### How to use",
"#### Install",
"#### Generate 1 word",
"#### Generate Full Sequence",
"#### Limitations and bias\n\n\nThe training data used for this model come from Turkish Wikipedia and books. We know it contains a lot of unfiltered content from the internet, which is far from neutral. Also not much pre-processing was done on books hence chapter names and page numbers can be seen on some cases. This is a work in progress.\n\n\nTraining data\n-------------\n\n\nWikipedia Turkish article dump as of 28-10-2020\nTurkish book dataset of >400 classic novels\n\n\nTraining procedure\n------------------\n\n\nEval results\n------------\n\n\n\nNote: 1cycle rule training is used and epochs are at different times\n'''"
] |
[
93,
5,
3,
6,
8,
130
] |
[
"passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #turkish #aiwriter #finetuned #tr #dataset-wikipedia-turkish #dataset-custom-book-corpus #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n#### How to use#### Install#### Generate 1 word#### Generate Full Sequence#### Limitations and bias\n\n\nThe training data used for this model come from Turkish Wikipedia and books. We know it contains a lot of unfiltered content from the internet, which is far from neutral. Also not much pre-processing was done on books hence chapter names and page numbers can be seen on some cases. This is a work in progress.\n\n\nTraining data\n-------------\n\n\nWikipedia Turkish article dump as of 28-10-2020\nTurkish book dataset of >400 classic novels\n\n\nTraining procedure\n------------------\n\n\nEval results\n------------\n\n\n\nNote: 1cycle rule training is used and epochs are at different times\n'''"
] |
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] |
null | null |
transformers
|
# GPT2 Persona Chatbot based on Movie Characters
Model used for https://www.metayazar.com/chatbot
GPT2 Small Trained on movie scripts (especially Sci-fi)
Usual HF api will not work see HF Spaces for demo usage https://huggingface.co/spaces/gorkemgoknar/moviechatbot
This work is based on Persona Chatbot originally done by Hugging Face team (https://medium.com/huggingface/how-to-build-a-state-of-the-art-conversational-ai-with-transfer-learning-2d818ac26313)
For cleaning movie scripts I also provide cleaner code
https://github.com/gorkemgoknar/moviescriptcleaner
Example persona how to:
https://gist.github.com/gorkemgoknar/ae29bf9d14fa814e6a64d0e57a4a4ed7
Tried a AI job interview over some characters here, details on this post
https://www.linkedin.com/pulse/ai-goes-job-interview-g%C3%B6rkem-g%C3%B6knar/
For obvious reasons I cannot share raw personafile but you can check above gist for example how to create it.
A working "full" demo can be seen in https://www.metayazar.com/chatbot
For Turkish version (with limited training) https://www.metayazar.com/chatbot_tr
Due to double LM head standart hugging face interface will not work. But if you follow huggingface tutorial should be same.
Except each persona is encoded as "My name is XXXX"
Use model, tokenizer and parameters within a class and call in below functions to trigger model.
Some of the available personas:
| Macleod | Moran | Brenda | Ramirez | Peter Parker | Quentin Beck | Andy
| Red | Norton | Willard | Chief | Chef | Kilgore | Kurtz | Westley | Buttercup
| Vizzini | Fezzik | Inigo | Man In Black | Taylor | Zira | Zaius | Cornelius
| Bud | Lindsey | Hippy | Erin | Ed | George | Donna | Trinity | Agent Smith
| Morpheus | Neo | Tank | Meryl | Truman | Marlon | Christof | Stromboli | Bumstead
| Schreber | Walker | Korben | Cornelius | Loc Rhod | Anakin | Obi-Wan | Palpatine
| Padme | Superman | Luthor | Dude | Walter | Donny | Maude | General | Starkiller
| Indiana | Willie | Short Round | John | Sarah | Terminator | Miller | Sarge | Reiben
| Jackson | Upham | Chuckie | Will | Lambeau | Sean | Skylar | Saavik | Spock
| Kirk | Bones | Khan | Kirk | Spock | Sybok | Scotty | Bourne | Pamela | Abbott
```python
def get_answer(self, input_text, personality, history, params=None):
##Check length of history (to save 1 computation!)
if len(history)>0:
#mostly it will be empty list so need a length check for performance
#would do string check also but just assume it is list of list of strings, as not public
new_hist = []
for ele in history:
new_hist.append( self.tokenizer.encode(ele) )
history = new_hist.copy()
history.append(self.tokenizer.encode(input_text))
with torch.no_grad():
out_ids = self.sample_sequence(personality, history, self.tokenizer, self.model, params=params)
history.append(out_ids)
history = history[-(2*self.parameters['max_history']+1):]
out_text = self.tokenizer.decode(out_ids, skip_special_tokens=True)
#print(out_text)
history_decoded = []
for ele in history:
history_decoded.append(self.tokenizer.decode(ele))
return out_text, history_decoded, self.parameters
```
|
{"language": ["en"], "license": "cc-by-4.0", "tags": ["gpt2", "conversational"], "widget": [{"text": "Hello there", "context": "Gandalf"}]}
|
text-generation
|
gorkemgoknar/gpt2chatbotenglish
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"en",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #en #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# GPT2 Persona Chatbot based on Movie Characters
Model used for URL
GPT2 Small Trained on movie scripts (especially Sci-fi)
Usual HF api will not work see HF Spaces for demo usage URL
This work is based on Persona Chatbot originally done by Hugging Face team (URL
For cleaning movie scripts I also provide cleaner code
URL
Example persona how to:
URL
Tried a AI job interview over some characters here, details on this post
URL
For obvious reasons I cannot share raw personafile but you can check above gist for example how to create it.
A working "full" demo can be seen in URL
For Turkish version (with limited training) URL
Due to double LM head standart hugging face interface will not work. But if you follow huggingface tutorial should be same.
Except each persona is encoded as "My name is XXXX"
Use model, tokenizer and parameters within a class and call in below functions to trigger model.
Some of the available personas:
| Macleod | Moran | Brenda | Ramirez | Peter Parker | Quentin Beck | Andy
| Red | Norton | Willard | Chief | Chef | Kilgore | Kurtz | Westley | Buttercup
| Vizzini | Fezzik | Inigo | Man In Black | Taylor | Zira | Zaius | Cornelius
| Bud | Lindsey | Hippy | Erin | Ed | George | Donna | Trinity | Agent Smith
| Morpheus | Neo | Tank | Meryl | Truman | Marlon | Christof | Stromboli | Bumstead
| Schreber | Walker | Korben | Cornelius | Loc Rhod | Anakin | Obi-Wan | Palpatine
| Padme | Superman | Luthor | Dude | Walter | Donny | Maude | General | Starkiller
| Indiana | Willie | Short Round | John | Sarah | Terminator | Miller | Sarge | Reiben
| Jackson | Upham | Chuckie | Will | Lambeau | Sean | Skylar | Saavik | Spock
| Kirk | Bones | Khan | Kirk | Spock | Sybok | Scotty | Bourne | Pamela | Abbott
|
[
"# GPT2 Persona Chatbot based on Movie Characters\nModel used for URL\n\nGPT2 Small Trained on movie scripts (especially Sci-fi) \n\nUsual HF api will not work see HF Spaces for demo usage URL\n\n\nThis work is based on Persona Chatbot originally done by Hugging Face team (URL\n\nFor cleaning movie scripts I also provide cleaner code\nURL\n\nExample persona how to:\nURL\n\nTried a AI job interview over some characters here, details on this post\nURL\n\nFor obvious reasons I cannot share raw personafile but you can check above gist for example how to create it.\n\nA working \"full\" demo can be seen in URL\n\nFor Turkish version (with limited training) URL\n\nDue to double LM head standart hugging face interface will not work. But if you follow huggingface tutorial should be same.\nExcept each persona is encoded as \"My name is XXXX\"\n\nUse model, tokenizer and parameters within a class and call in below functions to trigger model.\nSome of the available personas:\n\n| Macleod | Moran | Brenda | Ramirez | Peter Parker | Quentin Beck | Andy \n| Red | Norton | Willard | Chief | Chef | Kilgore | Kurtz | Westley | Buttercup \n| Vizzini | Fezzik | Inigo | Man In Black | Taylor | Zira | Zaius | Cornelius \n| Bud | Lindsey | Hippy | Erin | Ed | George | Donna | Trinity | Agent Smith \n| Morpheus | Neo | Tank | Meryl | Truman | Marlon | Christof | Stromboli | Bumstead \n| Schreber | Walker | Korben | Cornelius | Loc Rhod | Anakin | Obi-Wan | Palpatine \n| Padme | Superman | Luthor | Dude | Walter | Donny | Maude | General | Starkiller \n| Indiana | Willie | Short Round | John | Sarah | Terminator | Miller | Sarge | Reiben \n| Jackson | Upham | Chuckie | Will | Lambeau | Sean | Skylar | Saavik | Spock \n| Kirk | Bones | Khan | Kirk | Spock | Sybok | Scotty | Bourne | Pamela | Abbott"
] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #en #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# GPT2 Persona Chatbot based on Movie Characters\nModel used for URL\n\nGPT2 Small Trained on movie scripts (especially Sci-fi) \n\nUsual HF api will not work see HF Spaces for demo usage URL\n\n\nThis work is based on Persona Chatbot originally done by Hugging Face team (URL\n\nFor cleaning movie scripts I also provide cleaner code\nURL\n\nExample persona how to:\nURL\n\nTried a AI job interview over some characters here, details on this post\nURL\n\nFor obvious reasons I cannot share raw personafile but you can check above gist for example how to create it.\n\nA working \"full\" demo can be seen in URL\n\nFor Turkish version (with limited training) URL\n\nDue to double LM head standart hugging face interface will not work. But if you follow huggingface tutorial should be same.\nExcept each persona is encoded as \"My name is XXXX\"\n\nUse model, tokenizer and parameters within a class and call in below functions to trigger model.\nSome of the available personas:\n\n| Macleod | Moran | Brenda | Ramirez | Peter Parker | Quentin Beck | Andy \n| Red | Norton | Willard | Chief | Chef | Kilgore | Kurtz | Westley | Buttercup \n| Vizzini | Fezzik | Inigo | Man In Black | Taylor | Zira | Zaius | Cornelius \n| Bud | Lindsey | Hippy | Erin | Ed | George | Donna | Trinity | Agent Smith \n| Morpheus | Neo | Tank | Meryl | Truman | Marlon | Christof | Stromboli | Bumstead \n| Schreber | Walker | Korben | Cornelius | Loc Rhod | Anakin | Obi-Wan | Palpatine \n| Padme | Superman | Luthor | Dude | Walter | Donny | Maude | General | Starkiller \n| Indiana | Willie | Short Round | John | Sarah | Terminator | Miller | Sarge | Reiben \n| Jackson | Upham | Chuckie | Will | Lambeau | Sean | Skylar | Saavik | Spock \n| Kirk | Bones | Khan | Kirk | Spock | Sybok | Scotty | Bourne | Pamela | Abbott"
] |
[
66,
556
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #en #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
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null | null |
transformers
|
# Wav2Vec2-Large-XLSR-53-Turkish
Note: This model is trained with 5 Turkish movies additional to common voice dataset.
Although WER is high (50%) per common voice test dataset, performance from "other sources " seems pretty good.
Disclaimer: Please use another wav2vec2-tr model in hub for "clean environment" dialogues as they tend to do better in clean sounds with less background noise.
Dataset building from csv and merging code can be found on below of this Readme.
Please try speech yourself on the right side to see its performance.
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Turkish using the [Common Voice](https://huggingface.co/datasets/common_voice) and 5 Turkish movies that include background noise/talkers .
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
```python
import torch
import torchaudio
import pydub
from pydub.utils import mediainfo
import array
from pydub import AudioSegment
from pydub.utils import get_array_type
import numpy as np
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
test_dataset = load_dataset("common_voice", "tr", split="test[:2%]")
processor = Wav2Vec2Processor.from_pretrained("gorkemgoknar/wav2vec2-large-xlsr-53-turkish")
model = Wav2Vec2ForCTC.from_pretrained("gorkemgoknar/wav2vec2-large-xlsr-53-turkish")
new_sample_rate = 16000
def audio_resampler(batch, new_sample_rate = 16000):
#not working without complex library compilation in windows for mp3
#speech_array, sampling_rate = torchaudio.load(batch["path"])
#speech_array, sampling_rate = librosa.load(batch["path"])
#sampling_rate = pydub.utils.info['sample_rate'] ##gets current samplerate
sound = pydub.AudioSegment.from_file(file=batch["path"])
sampling_rate = new_sample_rate
sound = sound.set_frame_rate(new_sample_rate)
left = sound.split_to_mono()[0]
bit_depth = left.sample_width * 8
array_type = pydub.utils.get_array_type(bit_depth)
numeric_array = np.array(array.array(array_type, left._data) )
speech_array = torch.FloatTensor(numeric_array)
batch["speech"] = numeric_array
batch["sampling_rate"] = sampling_rate
#batch["target_text"] = batch["sentence"]
return batch
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
batch = audio_resampler(batch, new_sample_rate = new_sample_rate)
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
predicted_ids = torch.argmax(logits, dim=-1)
print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset["sentence"][:2])
```
## Evaluation
The model can be evaluated as follows on the Turkish test data of Common Voice.
```python
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re
import pydub
import array
import numpy as np
test_dataset = load_dataset("common_voice", "tr", split="test")
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("gorkemgoknar/wav2vec2-large-xlsr-53-turkish")
model = Wav2Vec2ForCTC.from_pretrained("gorkemgoknar/wav2vec2-large-xlsr-53-turkish")
model.to("cuda")
#Note: Not ignoring "'" on this one
#Note: Not ignoring "'" on this one
chars_to_ignore_regex = '[\\,\\?\\.\\!\\-\\;\\:\\"\\“\\%\\‘\\”\\�\\#\\>\\<\\_\\’\\[\\]\\{\\}]'
#resampler = torchaudio.transforms.Resample(48_000, 16_000)
#using custom load and transformer for audio -> see audio_resampler
new_sample_rate = 16000
def audio_resampler(batch, new_sample_rate = 16000):
#not working without complex library compilation in windows for mp3
#speech_array, sampling_rate = torchaudio.load(batch["path"])
#speech_array, sampling_rate = librosa.load(batch["path"])
#sampling_rate = pydub.utils.info['sample_rate'] ##gets current samplerate
sound = pydub.AudioSegment.from_file(file=batch["path"])
sound = sound.set_frame_rate(new_sample_rate)
left = sound.split_to_mono()[0]
bit_depth = left.sample_width * 8
array_type = pydub.utils.get_array_type(bit_depth)
numeric_array = np.array(array.array(array_type, left._data) )
speech_array = torch.FloatTensor(numeric_array)
return speech_array, new_sample_rate
def remove_special_characters(batch):
##this one comes from subtitles if additional timestamps not processed -> 00:01:01 00:01:01,33
batch["sentence"] = re.sub('\\b\\d{2}:\\d{2}:\\d{2}(,+\\d{2})?\\b', ' ', batch["sentence"])
##remove all caps in text [AÇIKLAMA] etc, do it before..
batch["sentence"] = re.sub('\\[(\\b[A-Z]+\\])', '', batch["sentence"])
##replace three dots (that are inside string with single)
batch["sentence"] = re.sub("([a-zA-Z]+)\\.\\.\\.", r"\\1.", batch["sentence"])
#standart ignore list
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() + " "
return batch
# Preprocessing the datasets.
# We need to read the aduio files as arrays
new_sample_rate = 16000
def speech_file_to_array_fn(batch):
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
##speech_array, sampling_rate = torchaudio.load(batch["path"])
##load and conversion done in resampler , takes and returns batch
speech_array, sampling_rate = audio_resampler(batch, new_sample_rate = new_sample_rate)
batch["speech"] = speech_array
batch["sampling_rate"] = sampling_rate
batch["target_text"] = batch["sentence"]
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def evaluate(batch):
inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
pred_ids = torch.argmax(logits, dim=-1)
batch["pred_strings"] = processor.batch_decode(pred_ids)
return batch
print("EVALUATING:")
##for 8GB RAM on GPU best is batch_size 2 for windows, 4 may fit in linux only
result = test_dataset.map(evaluate, batched=True, batch_size=2)
print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
```
**Test Result**: 50.41 %
## Training
The Common Voice `train` and `validation` datasets were used for training. Additional 5 Turkish movies with subtitles also used for training.
Similar training model used as base fine-tuning, additional audio resampler is on above code.
Putting model building and merging code below for reference
```python
import pandas as pd
from datasets import load_dataset, load_metric
import os
from pathlib import Path
from datasets import Dataset
import csv
#Walk all subdirectories of base_set_path and find csv files
base_set_path = r'C:\\dataset_extracts'
csv_files = []
for path, subdirs, files in os.walk(base_set_path):
for name in files:
if name.endswith(".csv"):
deckfile= os.path.join(path, name)
csv_files.append(deckfile)
def get_dataset_from_csv_file(csvfilename,names=['sentence', 'path']):
path = Path(csvfilename)
csv_delimiter="\\t" ##tab seperated, change if something else
##Pandas has bug reading non-ascii file names, make sure use open with encoding
df=pd.read_csv(open(path, 'r', encoding='utf-8'), delimiter=csv_delimiter,header=None , names=names, encoding='utf8')
return Dataset.from_pandas(df)
custom_datasets= []
for csv_file in csv_files:
this_dataset=get_dataset_from_csv_file(csv_file)
custom_datasets.append(this_dataset)
from datasets import concatenate_datasets, load_dataset
from datasets import load_from_disk
# Merge datasets together (from csv files)
dataset_file_path = ".\\dataset_file"
custom_datasets_concat = concatenate_datasets( [dset for dset in custom_datasets] )
#save this one to disk
custom_datasets_concat.save_to_disk( dataset_file_path )
#load back from disk
custom_datasets_from_disk = load_from_disk(dataset_file_path)
```
|
{"language": ["tr"], "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice", "movies"], "metrics": ["wer"], "model-index": [{"name": "XLSR Wav2Vec2 Large Turkish with extended dataset by Gorkem Goknar", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Common Voice tr", "type": "common_voice", "args": "tr"}, "metrics": [{"type": "wer", "value": 50.41, "name": "Test WER"}]}]}]}
|
automatic-speech-recognition
|
gorkemgoknar/wav2vec2-large-xlsr-53-turkish
|
[
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"tr",
"dataset:common_voice",
"dataset:movies",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"tr"
] |
TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #tr #dataset-common_voice #dataset-movies #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53-Turkish
Note: This model is trained with 5 Turkish movies additional to common voice dataset.
Although WER is high (50%) per common voice test dataset, performance from "other sources " seems pretty good.
Disclaimer: Please use another wav2vec2-tr model in hub for "clean environment" dialogues as they tend to do better in clean sounds with less background noise.
Dataset building from csv and merging code can be found on below of this Readme.
Please try speech yourself on the right side to see its performance.
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Turkish using the Common Voice and 5 Turkish movies that include background noise/talkers .
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
## Evaluation
The model can be evaluated as follows on the Turkish test data of Common Voice.
Test Result: 50.41 %
## Training
The Common Voice 'train' and 'validation' datasets were used for training. Additional 5 Turkish movies with subtitles also used for training.
Similar training model used as base fine-tuning, additional audio resampler is on above code.
Putting model building and merging code below for reference
|
[
"# Wav2Vec2-Large-XLSR-53-Turkish\n\nNote: This model is trained with 5 Turkish movies additional to common voice dataset.\nAlthough WER is high (50%) per common voice test dataset, performance from \"other sources \" seems pretty good.\n\nDisclaimer: Please use another wav2vec2-tr model in hub for \"clean environment\" dialogues as they tend to do better in clean sounds with less background noise.\n\nDataset building from csv and merging code can be found on below of this Readme.\n\nPlease try speech yourself on the right side to see its performance.\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Turkish using the Common Voice and 5 Turkish movies that include background noise/talkers .\n\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\nThe model can be evaluated as follows on the Turkish test data of Common Voice. \n\n\nTest Result: 50.41 %",
"## Training\n\n\nThe Common Voice 'train' and 'validation' datasets were used for training. Additional 5 Turkish movies with subtitles also used for training.\nSimilar training model used as base fine-tuning, additional audio resampler is on above code.\n\nPutting model building and merging code below for reference"
] |
[
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #tr #dataset-common_voice #dataset-movies #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Turkish\n\nNote: This model is trained with 5 Turkish movies additional to common voice dataset.\nAlthough WER is high (50%) per common voice test dataset, performance from \"other sources \" seems pretty good.\n\nDisclaimer: Please use another wav2vec2-tr model in hub for \"clean environment\" dialogues as they tend to do better in clean sounds with less background noise.\n\nDataset building from csv and merging code can be found on below of this Readme.\n\nPlease try speech yourself on the right side to see its performance.\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Turkish using the Common Voice and 5 Turkish movies that include background noise/talkers .\n\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\nThe model can be evaluated as follows on the Turkish test data of Common Voice. \n\n\nTest Result: 50.41 %",
"## Training\n\n\nThe Common Voice 'train' and 'validation' datasets were used for training. Additional 5 Turkish movies with subtitles also used for training.\nSimilar training model used as base fine-tuning, additional audio resampler is on above code.\n\nPutting model building and merging code below for reference"
] |
[
86,
192,
20,
29,
71
] |
[
"passage: TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #tr #dataset-common_voice #dataset-movies #license-apache-2.0 #model-index #endpoints_compatible #region-us \n# Wav2Vec2-Large-XLSR-53-Turkish\n\nNote: This model is trained with 5 Turkish movies additional to common voice dataset.\nAlthough WER is high (50%) per common voice test dataset, performance from \"other sources \" seems pretty good.\n\nDisclaimer: Please use another wav2vec2-tr model in hub for \"clean environment\" dialogues as they tend to do better in clean sounds with less background noise.\n\nDataset building from csv and merging code can be found on below of this Readme.\n\nPlease try speech yourself on the right side to see its performance.\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Turkish using the Common Voice and 5 Turkish movies that include background noise/talkers .\n\nWhen using this model, make sure that your speech input is sampled at 16kHz.## Usage\nThe model can be used directly (without a language model) as follows:## Evaluation\nThe model can be evaluated as follows on the Turkish test data of Common Voice. \n\n\nTest Result: 50.41 %## Training\n\n\nThe Common Voice 'train' and 'validation' datasets were used for training. Additional 5 Turkish movies with subtitles also used for training.\nSimilar training model used as base fine-tuning, additional audio resampler is on above code.\n\nPutting model building and merging code below for reference"
] |
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] |
null | null | null |
test
|
{}
| null |
gottaegbert/nolibox
|
[
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#region-us
|
test
|
[] |
[
"TAGS\n#region-us \n"
] |
[
6
] |
[
"passage: TAGS\n#region-us \n"
] |
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] |
null | null |
transformers
|
# Introduction
This model checkpoint is obtained by first fine-tuning the sshleifer/distilbart-cnn-6-6 summarization checkpoint on the SQuAD dataset. After this, the 6-6 fine-tuned model is distilled down to a 3-3 model which gives us the final checkpoint. [GitHub Link for training scripts.](https://github.com/darth-c0d3r/bart-question-generation)
# Usage
The input format is as follows: `[answer] <s> [passage]`. The model will predict the question that corresponds to the answer from the passage.
# Plot
# Dataset
The goal of Question Generation is to generate a valid and fluent question according to a given passage and the target answer. Hence, the input to the model will be a passage context and an answer, and the output / target will be the question for the given answer. Question Generation can be used in many scenarios, such as automatic tutoring systems, improving the performance of Question Answering models and enabling chat-bots to lead a conversation. The final dataset is created by taking the union of the following Question Answering Datasets. The dataset must have the following three columns: context, question, answer.
## [SQuAD](https://rajpurkar.github.io/SQuAD-explorer/)
Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowd-workers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable. We use the SQuAD 1.1 variant which does not have unanswerable questions. So, every question will have a corresponding answer and vice-versa.
### Preprocessing
The first step is to remove questions which don't have answers. After that, we split the train set into Train and Eval sets and treat the dev set as the test set.
### Stats
**Original Dataset**
| Split | Num Docs | Num Contexts | Ques w/ Ans | Ques w/o Ans | Num Unique Ans |
| ----- | -------- | ------------ | ----------- | ------------ | -------------- |
| Train | 442 | 19035 | 86821 | 43498 | 86821 |
| Dev | 35 | 1204 | 5928 | 5945 | 10279 |
**After Preprocessing**
| Split | Num Rows | Context | Answer | Question |
| ----- | -------- | ---------- | ------ | -------- |
| Train | 80995 | 653,120,20 | 43,3,1 | 40,10,1 |
| Eval | 5826 | 445,123,67 | 28,3,1 | 29,10,3 |
| Test | 10297 | 629,129,25 | 29,4,1 | 31,10,3 |
The numbers in the columns indicate max, avg, min number of words.
|
{"language": "en", "license": "apache-2.0", "tags": ["question-generation", "summarization"], "datasets": ["squad"]}
|
summarization
|
gpssohi/distilbart-qgen-3-3
|
[
"transformers",
"pytorch",
"bart",
"text2text-generation",
"question-generation",
"summarization",
"en",
"dataset:squad",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #bart #text2text-generation #question-generation #summarization #en #dataset-squad #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
Introduction
============
This model checkpoint is obtained by first fine-tuning the sshleifer/distilbart-cnn-6-6 summarization checkpoint on the SQuAD dataset. After this, the 6-6 fine-tuned model is distilled down to a 3-3 model which gives us the final checkpoint. GitHub Link for training scripts.
Usage
=====
The input format is as follows: '[answer] ~~[passage]'. The model will predict the question that corresponds to the answer from the passage.~~
Plot
====
!Distillation Run
Dataset
=======
The goal of Question Generation is to generate a valid and fluent question according to a given passage and the target answer. Hence, the input to the model will be a passage context and an answer, and the output / target will be the question for the given answer. Question Generation can be used in many scenarios, such as automatic tutoring systems, improving the performance of Question Answering models and enabling chat-bots to lead a conversation. The final dataset is created by taking the union of the following Question Answering Datasets. The dataset must have the following three columns: context, question, answer.
SQuAD
-----
Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowd-workers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable. We use the SQuAD 1.1 variant which does not have unanswerable questions. So, every question will have a corresponding answer and vice-versa.
### Preprocessing
The first step is to remove questions which don't have answers. After that, we split the train set into Train and Eval sets and treat the dev set as the test set.
### Stats
Original Dataset
After Preprocessing
The numbers in the columns indicate max, avg, min number of words.
|
[
"### Preprocessing\n\n\nThe first step is to remove questions which don't have answers. After that, we split the train set into Train and Eval sets and treat the dev set as the test set.",
"### Stats\n\n\nOriginal Dataset\n\n\n\nAfter Preprocessing\n\n\n\nThe numbers in the columns indicate max, avg, min number of words."
] |
[
"TAGS\n#transformers #pytorch #bart #text2text-generation #question-generation #summarization #en #dataset-squad #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Preprocessing\n\n\nThe first step is to remove questions which don't have answers. After that, we split the train set into Train and Eval sets and treat the dev set as the test set.",
"### Stats\n\n\nOriginal Dataset\n\n\n\nAfter Preprocessing\n\n\n\nThe numbers in the columns indicate max, avg, min number of words."
] |
[
64,
45,
29
] |
[
"passage: TAGS\n#transformers #pytorch #bart #text2text-generation #question-generation #summarization #en #dataset-squad #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Preprocessing\n\n\nThe first step is to remove questions which don't have answers. After that, we split the train set into Train and Eval sets and treat the dev set as the test set.### Stats\n\n\nOriginal Dataset\n\n\n\nAfter Preprocessing\n\n\n\nThe numbers in the columns indicate max, avg, min number of words."
] |
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] |
null | null |
transformers
|
# Introduction
This model checkpoint is obtained by fine-tuning the `sshleifer/distilbart-cnn-6-6` summarization checkpoint on the SQuAD dataset. [GitHub Link for training scripts.](https://github.com/darth-c0d3r/bart-question-generation)
# Usage
The input format is as follows: `[answer] <s> [passage]`. The model will predict the question that corresponds to the answer from the passage.
# Plot
# Dataset
The goal of Question Generation is to generate a valid and fluent question according to a given passage and the target answer. Hence, the input to the model will be a passage context and an answer, and the output / target will be the question for the given answer. Question Generation can be used in many scenarios, such as automatic tutoring systems, improving the performance of Question Answering models and enabling chat-bots to lead a conversation. The final dataset is created by taking the union of the following Question Answering Datasets. The dataset must have the following three columns: context, question, answer.
## [SQuAD](https://rajpurkar.github.io/SQuAD-explorer/)
Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowd-workers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable. We use the SQuAD 1.1 variant which does not have unanswerable questions. So, every question will have a corresponding answer and vice-versa.
### Preprocessing
The first step is to remove questions that don't have answers. After that, we split the train set into Train and Eval sets and treat the dev set as the test set.
### Stats
**Original Dataset**
| Split | Num Docs | Num Contexts | Ques w/ Ans | Ques w/o Ans | Num Unique Ans |
| ----- | -------- | ------------ | ----------- | ------------ | -------------- |
| Train | 442 | 19035 | 86821 | 43498 | 86821 |
| Dev | 35 | 1204 | 5928 | 5945 | 10279 |
**After Preprocessing**
| Split | Num Rows | Context | Answer | Question |
| ----- | -------- | ---------- | ------ | -------- |
| Train | 80995 | 653,120,20 | 43,3,1 | 40,10,1 |
| Eval | 5826 | 445,123,67 | 28,3,1 | 29,10,3 |
| Test | 10297 | 629,129,25 | 29,4,1 | 31,10,3 |
The numbers in the columns indicate max, avg, min number of words.
|
{"language": "en", "license": "apache-2.0", "tags": ["summarization", "question-generation"], "datasets": ["squad"]}
|
summarization
|
gpssohi/distilbart-qgen-6-6
|
[
"transformers",
"pytorch",
"safetensors",
"bart",
"text2text-generation",
"summarization",
"question-generation",
"en",
"dataset:squad",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #safetensors #bart #text2text-generation #summarization #question-generation #en #dataset-squad #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
Introduction
============
This model checkpoint is obtained by fine-tuning the 'sshleifer/distilbart-cnn-6-6' summarization checkpoint on the SQuAD dataset. GitHub Link for training scripts.
Usage
=====
The input format is as follows: '[answer] ~~[passage]'. The model will predict the question that corresponds to the answer from the passage.~~
Plot
====
!Training Run
Dataset
=======
The goal of Question Generation is to generate a valid and fluent question according to a given passage and the target answer. Hence, the input to the model will be a passage context and an answer, and the output / target will be the question for the given answer. Question Generation can be used in many scenarios, such as automatic tutoring systems, improving the performance of Question Answering models and enabling chat-bots to lead a conversation. The final dataset is created by taking the union of the following Question Answering Datasets. The dataset must have the following three columns: context, question, answer.
SQuAD
-----
Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowd-workers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable. We use the SQuAD 1.1 variant which does not have unanswerable questions. So, every question will have a corresponding answer and vice-versa.
### Preprocessing
The first step is to remove questions that don't have answers. After that, we split the train set into Train and Eval sets and treat the dev set as the test set.
### Stats
Original Dataset
After Preprocessing
The numbers in the columns indicate max, avg, min number of words.
|
[
"### Preprocessing\n\n\nThe first step is to remove questions that don't have answers. After that, we split the train set into Train and Eval sets and treat the dev set as the test set.",
"### Stats\n\n\nOriginal Dataset\n\n\n\nAfter Preprocessing\n\n\n\nThe numbers in the columns indicate max, avg, min number of words."
] |
[
"TAGS\n#transformers #pytorch #safetensors #bart #text2text-generation #summarization #question-generation #en #dataset-squad #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Preprocessing\n\n\nThe first step is to remove questions that don't have answers. After that, we split the train set into Train and Eval sets and treat the dev set as the test set.",
"### Stats\n\n\nOriginal Dataset\n\n\n\nAfter Preprocessing\n\n\n\nThe numbers in the columns indicate max, avg, min number of words."
] |
[
69,
45,
29
] |
[
"passage: TAGS\n#transformers #pytorch #safetensors #bart #text2text-generation #summarization #question-generation #en #dataset-squad #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Preprocessing\n\n\nThe first step is to remove questions that don't have answers. After that, we split the train set into Train and Eval sets and treat the dev set as the test set.### Stats\n\n\nOriginal Dataset\n\n\n\nAfter Preprocessing\n\n\n\nThe numbers in the columns indicate max, avg, min number of words."
] |
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] |
null | null |
transformers
|
#waifu bot
|
{"tags": ["conversational"]}
|
text-generation
|
grayson124/chatbotwaifu
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#waifu bot
|
[] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
[
51
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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] |
null | null |
asteroid
|
## Asteroid model `groadabike/ConvTasNet_DAMP-VSEP_enhboth`
Imported from [Zenodo](https://zenodo.org/record/3994193)
### Description:
This model was trained by Gerardo Roa Dabike using Asteroid. It was trained on the enh_both task of the DAMP-VSEP dataset.
### Training config:
```yaml
data:
channels: 1
n_src: 2
root_path: data
sample_rate: 16000
samples_per_track: 10
segment: 3.0
task: enh_both
filterbank:
kernel_size: 20
n_filters: 256
stride: 10
main_args:
exp_dir: exp/train_convtasnet
help: None
masknet:
bn_chan: 256
conv_kernel_size: 3
hid_chan: 512
mask_act: relu
n_blocks: 8
n_repeats: 4
n_src: 2
norm_type: gLN
skip_chan: 256
optim:
lr: 0.0003
optimizer: adam
weight_decay: 0.0
positional arguments:
training:
batch_size: 12
early_stop: True
epochs: 50
half_lr: True
num_workers: 12
```
### Results:
```yaml
si_sdr: 14.018196157142519
si_sdr_imp: 14.017103133809577
sdr: 14.498517291333885
sdr_imp: 14.463389151567865
sir: 24.149634529133372
sir_imp: 24.11450638936735
sar: 15.338597389045935
sar_imp: -137.30634122401517
stoi: 0.7639416744417206
stoi_imp: 0.1843383526963759
```
### License notice:
This work "ConvTasNet_DAMP-VSEP_enhboth" is a derivative of DAMP-VSEP: Smule Digital Archive of Mobile Performances - Vocal Separation (Version 1.0.1) by Smule, Inc, used under Smule's Research Data License Agreement (Research only). "ConvTasNet_DAMP-VSEP_enhboth" is licensed under Attribution-ShareAlike 3.0 Unported by Gerardo Roa Dabike.
|
{"license": "cc-by-sa-4.0", "tags": ["asteroid", "audio", "ConvTasNet", "audio-to-audio"], "datasets": ["DAMP-VSEP"]}
|
audio-to-audio
|
groadabike/ConvTasNet_DAMP-VSEP_enhboth
|
[
"asteroid",
"pytorch",
"audio",
"ConvTasNet",
"audio-to-audio",
"dataset:DAMP-VSEP",
"license:cc-by-sa-4.0",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#asteroid #pytorch #audio #ConvTasNet #audio-to-audio #dataset-DAMP-VSEP #license-cc-by-sa-4.0 #has_space #region-us
|
## Asteroid model 'groadabike/ConvTasNet_DAMP-VSEP_enhboth'
Imported from Zenodo
### Description:
This model was trained by Gerardo Roa Dabike using Asteroid. It was trained on the enh_both task of the DAMP-VSEP dataset.
### Training config:
### Results:
### License notice:
This work "ConvTasNet_DAMP-VSEP_enhboth" is a derivative of DAMP-VSEP: Smule Digital Archive of Mobile Performances - Vocal Separation (Version 1.0.1) by Smule, Inc, used under Smule's Research Data License Agreement (Research only). "ConvTasNet_DAMP-VSEP_enhboth" is licensed under Attribution-ShareAlike 3.0 Unported by Gerardo Roa Dabike.
|
[
"## Asteroid model 'groadabike/ConvTasNet_DAMP-VSEP_enhboth'\nImported from Zenodo",
"### Description:\nThis model was trained by Gerardo Roa Dabike using Asteroid. It was trained on the enh_both task of the DAMP-VSEP dataset.",
"### Training config:",
"### Results:",
"### License notice:\nThis work \"ConvTasNet_DAMP-VSEP_enhboth\" is a derivative of DAMP-VSEP: Smule Digital Archive of Mobile Performances - Vocal Separation (Version 1.0.1) by Smule, Inc, used under Smule's Research Data License Agreement (Research only). \"ConvTasNet_DAMP-VSEP_enhboth\" is licensed under Attribution-ShareAlike 3.0 Unported by Gerardo Roa Dabike."
] |
[
"TAGS\n#asteroid #pytorch #audio #ConvTasNet #audio-to-audio #dataset-DAMP-VSEP #license-cc-by-sa-4.0 #has_space #region-us \n",
"## Asteroid model 'groadabike/ConvTasNet_DAMP-VSEP_enhboth'\nImported from Zenodo",
"### Description:\nThis model was trained by Gerardo Roa Dabike using Asteroid. It was trained on the enh_both task of the DAMP-VSEP dataset.",
"### Training config:",
"### Results:",
"### License notice:\nThis work \"ConvTasNet_DAMP-VSEP_enhboth\" is a derivative of DAMP-VSEP: Smule Digital Archive of Mobile Performances - Vocal Separation (Version 1.0.1) by Smule, Inc, used under Smule's Research Data License Agreement (Research only). \"ConvTasNet_DAMP-VSEP_enhboth\" is licensed under Attribution-ShareAlike 3.0 Unported by Gerardo Roa Dabike."
] |
[
54,
30,
42,
6,
4,
109
] |
[
"passage: TAGS\n#asteroid #pytorch #audio #ConvTasNet #audio-to-audio #dataset-DAMP-VSEP #license-cc-by-sa-4.0 #has_space #region-us \n## Asteroid model 'groadabike/ConvTasNet_DAMP-VSEP_enhboth'\nImported from Zenodo### Description:\nThis model was trained by Gerardo Roa Dabike using Asteroid. It was trained on the enh_both task of the DAMP-VSEP dataset.### Training config:### Results:### License notice:\nThis work \"ConvTasNet_DAMP-VSEP_enhboth\" is a derivative of DAMP-VSEP: Smule Digital Archive of Mobile Performances - Vocal Separation (Version 1.0.1) by Smule, Inc, used under Smule's Research Data License Agreement (Research only). \"ConvTasNet_DAMP-VSEP_enhboth\" is licensed under Attribution-ShareAlike 3.0 Unported by Gerardo Roa Dabike."
] |
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] |
null | null |
asteroid
|
## Description:
This model was trained by Gerardo Roa using the dampvsep recipe in Asteroid.
It was trained on the `singing/accompaniment` task of the `DAMP-VSEP` dataset.
## Training config:
```yaml
data:
channels: 1
emb_model: 'no'
metadata_path: metadata
mixture: remix
root_path: /fastdata/acp13gr/DAMP/DAMP-VSEP
sample_rate: 16000
train_set: english_nonenglish
filterbank:
kernel_size: 20
n_filters: 256
stride: 10
main_args:
exp_dir: exp/train_convtasnet_remix-no-0.0-english_nonenglish-0.0005-jade
help: null
masknet:
bn_chan: 256
conv_kernel_size: 3
hid_chan: 512
mask_act: relu
n_blocks: 10
n_repeats: 4
n_src: 2
norm_type: gLN
skip_chan: 256
optim:
lr: 0.0005
optimizer: adam
weight_decay: 0.0
positional arguments: {}
training:
batch_size: 7
early_stop: true
epochs: 50
half_lr: true
loss_alpha: 0.0
num_workers: 10
```
## Results:
```yaml
"si_sdr": 15.111802516750586,
"si_sdr_imp": 15.178209807687663,
"si_sdr_s0": 12.160261214703553,
"si_sdr_s0_imp": 17.434593619085675,
"si_sdr_s1": 18.063343818797623,
"si_sdr_s1_imp": 12.92182599628965,
"sdr": 15.959722569460281,
"sdr_imp": 14.927002467087567,
"sdr_s0": 13.270412028426595,
"sdr_s0_imp": 16.45867572657551,
"sdr_s1": 18.64903311049397,
"sdr_s1_imp": 13.39532920759962,
"sir": 23.935932341084754,
"sir_imp": 22.903212238712012,
"sir_s0": 22.30777879911744,
"sir_s0_imp": 25.49604249726635,
"sir_s1": 25.56408588305207,
"sir_s1_imp": 20.310381980157665,
"sar": 17.174899162445882,
"sar_imp": -134.47377304178818,
"sar_s0": 14.268071153965913,
"sar_s0_imp": -137.38060105026818,
"sar_s1": 20.081727170925856,
"sar_s1_imp": -131.56694503330817,
"stoi": 0.7746496376326059,
"stoi_imp": 0.19613735629114643,
"stoi_s0": 0.6611376621212413,
"stoi_s0_imp": 0.21162695175464794,
"stoi_s1": 0.8881616131439705,
"stoi_s1_imp": 0.1806477608276449
```
## License notice:
** This is important, please fill it, if you need help, you can ask on Asteroid's slack.**
This work "ConvTasNet_DAMPVSEP_EnglishNonEnglish_baseline"
is a derivative of [DAMP-VSEP corpus](https://zenodo.org/record/3553059) by
[Smule, Inc](https://www.smule.com/),
used under [Restricted License](https://zenodo.org/record/3553059)(Research only).
"ConvTasNet_DAMPVSEP_EnglishNonEnglish_baseline"
is licensed under [Attribution-ShareAlike 3.0 Unported](https://creativecommons.org/licenses/by-sa/3.0/)
by Gerardo Roa.
|
{"license": "cc-by-sa-4.0", "tags": ["asteroid", "audio", "ConvTasNet", "audio-to-audio"], "datasets": ["DAMP-VSEP", "Singing/Accompaniment Separation"]}
|
audio-to-audio
|
groadabike/ConvTasNet_DAMPVSEP_EnglishNonEnglish_baseline
|
[
"asteroid",
"pytorch",
"audio",
"ConvTasNet",
"audio-to-audio",
"license:cc-by-sa-4.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#asteroid #pytorch #audio #ConvTasNet #audio-to-audio #license-cc-by-sa-4.0 #region-us
|
## Description:
This model was trained by Gerardo Roa using the dampvsep recipe in Asteroid.
It was trained on the 'singing/accompaniment' task of the 'DAMP-VSEP' dataset.
## Training config:
## Results:
## License notice:
This is important, please fill it, if you need help, you can ask on Asteroid's slack.
This work "ConvTasNet_DAMPVSEP_EnglishNonEnglish_baseline"
is a derivative of DAMP-VSEP corpus by
Smule, Inc,
used under Restricted License(Research only).
"ConvTasNet_DAMPVSEP_EnglishNonEnglish_baseline"
is licensed under Attribution-ShareAlike 3.0 Unported
by Gerardo Roa.
|
[
"## Description:\nThis model was trained by Gerardo Roa using the dampvsep recipe in Asteroid.\nIt was trained on the 'singing/accompaniment' task of the 'DAMP-VSEP' dataset.",
"## Training config:",
"## Results:",
"## License notice:\n\n This is important, please fill it, if you need help, you can ask on Asteroid's slack.\n\nThis work \"ConvTasNet_DAMPVSEP_EnglishNonEnglish_baseline\"\nis a derivative of DAMP-VSEP corpus by\nSmule, Inc,\nused under Restricted License(Research only).\n\"ConvTasNet_DAMPVSEP_EnglishNonEnglish_baseline\"\nis licensed under Attribution-ShareAlike 3.0 Unported\nby Gerardo Roa."
] |
[
"TAGS\n#asteroid #pytorch #audio #ConvTasNet #audio-to-audio #license-cc-by-sa-4.0 #region-us \n",
"## Description:\nThis model was trained by Gerardo Roa using the dampvsep recipe in Asteroid.\nIt was trained on the 'singing/accompaniment' task of the 'DAMP-VSEP' dataset.",
"## Training config:",
"## Results:",
"## License notice:\n\n This is important, please fill it, if you need help, you can ask on Asteroid's slack.\n\nThis work \"ConvTasNet_DAMPVSEP_EnglishNonEnglish_baseline\"\nis a derivative of DAMP-VSEP corpus by\nSmule, Inc,\nused under Restricted License(Research only).\n\"ConvTasNet_DAMPVSEP_EnglishNonEnglish_baseline\"\nis licensed under Attribution-ShareAlike 3.0 Unported\nby Gerardo Roa."
] |
[
41,
52,
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111
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[
"passage: TAGS\n#asteroid #pytorch #audio #ConvTasNet #audio-to-audio #license-cc-by-sa-4.0 #region-us \n## Description:\nThis model was trained by Gerardo Roa using the dampvsep recipe in Asteroid.\nIt was trained on the 'singing/accompaniment' task of the 'DAMP-VSEP' dataset.## Training config:## Results:## License notice:\n\n This is important, please fill it, if you need help, you can ask on Asteroid's slack.\n\nThis work \"ConvTasNet_DAMPVSEP_EnglishNonEnglish_baseline\"\nis a derivative of DAMP-VSEP corpus by\nSmule, Inc,\nused under Restricted License(Research only).\n\"ConvTasNet_DAMPVSEP_EnglishNonEnglish_baseline\"\nis licensed under Attribution-ShareAlike 3.0 Unported\nby Gerardo Roa."
] |
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] |
null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilgpt2-finetuned-escape
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) 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: 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: 100
### Training results
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilgpt2-finetuned-escape", "results": []}]}
|
text-generation
|
groar/distilgpt2-finetuned-escape
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# distilgpt2-finetuned-escape
This model is a fine-tuned version of distilgpt2 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: 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: 100
### Training results
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
|
[
"# distilgpt2-finetuned-escape\n\nThis model is a fine-tuned version of distilgpt2 on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 100",
"### Training results",
"### Framework versions\n\n- Transformers 4.16.2\n- Pytorch 1.10.0+cu111\n- Datasets 1.18.3\n- Tokenizers 0.11.0"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# distilgpt2-finetuned-escape\n\nThis model is a fine-tuned version of distilgpt2 on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 100",
"### Training results",
"### Framework versions\n\n- Transformers 4.16.2\n- Pytorch 1.10.0+cu111\n- Datasets 1.18.3\n- Tokenizers 0.11.0"
] |
[
66,
34,
6,
12,
8,
3,
90,
4,
35
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# distilgpt2-finetuned-escape\n\nThis model is a fine-tuned version of distilgpt2 on the None dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 100### Training results### Framework versions\n\n- Transformers 4.16.2\n- Pytorch 1.10.0+cu111\n- Datasets 1.18.3\n- Tokenizers 0.11.0"
] |
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] |
null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilgpt2-finetuned-wikitext2
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.6895
## 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: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.7852 | 1.0 | 2334 | 3.6895 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilgpt2-finetuned-wikitext2", "results": []}]}
|
text-generation
|
groar/distilgpt2-finetuned-wikitext2
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
distilgpt2-finetuned-wikitext2
==============================
This model is a fine-tuned version of distilgpt2 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.6895
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: 1
### Training results
### Framework versions
* Transformers 4.16.2
* Pytorch 1.10.0+cu111
* Datasets 1.18.3
* Tokenizers 0.11.0
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.16.2\n* Pytorch 1.10.0+cu111\n* Datasets 1.18.3\n* Tokenizers 0.11.0"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.16.2\n* Pytorch 1.10.0+cu111\n* Datasets 1.18.3\n* Tokenizers 0.11.0"
] |
[
66,
98,
4,
35
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1### Training results### Framework versions\n\n\n* Transformers 4.16.2\n* Pytorch 1.10.0+cu111\n* Datasets 1.18.3\n* Tokenizers 0.11.0"
] |
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] |
null | null |
transformers
|
<!-- 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-neo-1.3B-finetuned-escape
This model is a fine-tuned version of [EleutherAI/gpt-neo-1.3B](https://huggingface.co/EleutherAI/gpt-neo-1.3B) 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: 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: 40
### Training results
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "gpt-neo-1.3B-finetuned-escape", "results": []}]}
|
text-generation
|
groar/gpt-neo-1.3B-finetuned-escape
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt_neo",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #gpt_neo #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# gpt-neo-1.3B-finetuned-escape
This model is a fine-tuned version of EleutherAI/gpt-neo-1.3B 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: 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: 40
### Training results
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
|
[
"# gpt-neo-1.3B-finetuned-escape\n\nThis model is a fine-tuned version of EleutherAI/gpt-neo-1.3B on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 40",
"### Training results",
"### Framework versions\n\n- Transformers 4.16.2\n- Pytorch 1.10.0+cu111\n- Datasets 1.18.3\n- Tokenizers 0.11.0"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #gpt_neo #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# gpt-neo-1.3B-finetuned-escape\n\nThis model is a fine-tuned version of EleutherAI/gpt-neo-1.3B on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 40",
"### Training results",
"### Framework versions\n\n- Transformers 4.16.2\n- Pytorch 1.10.0+cu111\n- Datasets 1.18.3\n- Tokenizers 0.11.0"
] |
[
58,
44,
6,
12,
8,
3,
90,
4,
35
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #gpt_neo #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# gpt-neo-1.3B-finetuned-escape\n\nThis model is a fine-tuned version of EleutherAI/gpt-neo-1.3B on the None dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 40### Training results### Framework versions\n\n- Transformers 4.16.2\n- Pytorch 1.10.0+cu111\n- Datasets 1.18.3\n- Tokenizers 0.11.0"
] |
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null | null |
transformers
|
<!-- 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-neo-1.3B-finetuned-escape2
This model is a fine-tuned version of [EleutherAI/gpt-neo-1.3B](https://huggingface.co/EleutherAI/gpt-neo-1.3B) 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: 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: 10
### Training results
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "gpt-neo-1.3B-finetuned-escape2", "results": []}]}
|
text-generation
|
groar/gpt-neo-1.3B-finetuned-escape2
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt_neo",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #gpt_neo #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# gpt-neo-1.3B-finetuned-escape2
This model is a fine-tuned version of EleutherAI/gpt-neo-1.3B 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: 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: 10
### Training results
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
|
[
"# gpt-neo-1.3B-finetuned-escape2\n\nThis model is a fine-tuned version of EleutherAI/gpt-neo-1.3B on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 10",
"### Training results",
"### Framework versions\n\n- Transformers 4.16.2\n- Pytorch 1.10.0+cu111\n- Datasets 1.18.3\n- Tokenizers 0.11.0"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #gpt_neo #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# gpt-neo-1.3B-finetuned-escape2\n\nThis model is a fine-tuned version of EleutherAI/gpt-neo-1.3B on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 10",
"### Training results",
"### Framework versions\n\n- Transformers 4.16.2\n- Pytorch 1.10.0+cu111\n- Datasets 1.18.3\n- Tokenizers 0.11.0"
] |
[
58,
45,
6,
12,
8,
3,
90,
4,
35
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #gpt_neo #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# gpt-neo-1.3B-finetuned-escape2\n\nThis model is a fine-tuned version of EleutherAI/gpt-neo-1.3B on the None dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 10### Training results### Framework versions\n\n- Transformers 4.16.2\n- Pytorch 1.10.0+cu111\n- Datasets 1.18.3\n- Tokenizers 0.11.0"
] |
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] |
null | null |
transformers
|
<!-- 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-neo-1.3B-finetuned-escape3
This model is a fine-tuned version of [EleutherAI/gpt-neo-1.3B](https://huggingface.co/EleutherAI/gpt-neo-1.3B) 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: 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: 30
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "gpt-neo-1.3B-finetuned-escape3", "results": []}]}
|
text-generation
|
groar/gpt-neo-1.3B-finetuned-escape3
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt_neo",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #gpt_neo #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# gpt-neo-1.3B-finetuned-escape3
This model is a fine-tuned version of EleutherAI/gpt-neo-1.3B 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: 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: 30
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
|
[
"# gpt-neo-1.3B-finetuned-escape3\n\nThis model is a fine-tuned version of EleutherAI/gpt-neo-1.3B on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 30",
"### Framework versions\n\n- Transformers 4.16.2\n- Pytorch 1.10.0+cu111\n- Datasets 1.18.3\n- Tokenizers 0.11.0"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #gpt_neo #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# gpt-neo-1.3B-finetuned-escape3\n\nThis model is a fine-tuned version of EleutherAI/gpt-neo-1.3B on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 30",
"### Framework versions\n\n- Transformers 4.16.2\n- Pytorch 1.10.0+cu111\n- Datasets 1.18.3\n- Tokenizers 0.11.0"
] |
[
58,
45,
6,
12,
8,
3,
90,
35
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #gpt_neo #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# gpt-neo-1.3B-finetuned-escape3\n\nThis model is a fine-tuned version of EleutherAI/gpt-neo-1.3B on the None dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 30### Framework versions\n\n- Transformers 4.16.2\n- Pytorch 1.10.0+cu111\n- Datasets 1.18.3\n- Tokenizers 0.11.0"
] |
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] |
null | null |
transformers
|
<!-- 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-neo-1.3B-finetuned-escape5
This model is a fine-tuned version of [EleutherAI/gpt-neo-1.3B](https://huggingface.co/EleutherAI/gpt-neo-1.3B) 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: 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: 30
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "gpt-neo-1.3B-finetuned-escape5", "results": []}]}
|
text-generation
|
groar/gpt-neo-1.3B-finetuned-escape5
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt_neo",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #gpt_neo #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# gpt-neo-1.3B-finetuned-escape5
This model is a fine-tuned version of EleutherAI/gpt-neo-1.3B 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: 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: 30
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
|
[
"# gpt-neo-1.3B-finetuned-escape5\n\nThis model is a fine-tuned version of EleutherAI/gpt-neo-1.3B on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 30",
"### Framework versions\n\n- Transformers 4.16.2\n- Pytorch 1.10.0+cu111\n- Datasets 1.18.3\n- Tokenizers 0.11.0"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #gpt_neo #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# gpt-neo-1.3B-finetuned-escape5\n\nThis model is a fine-tuned version of EleutherAI/gpt-neo-1.3B on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 30",
"### Framework versions\n\n- Transformers 4.16.2\n- Pytorch 1.10.0+cu111\n- Datasets 1.18.3\n- Tokenizers 0.11.0"
] |
[
58,
45,
6,
12,
8,
3,
90,
35
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #gpt_neo #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# gpt-neo-1.3B-finetuned-escape5\n\nThis model is a fine-tuned version of EleutherAI/gpt-neo-1.3B on the None dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 30### Framework versions\n\n- Transformers 4.16.2\n- Pytorch 1.10.0+cu111\n- Datasets 1.18.3\n- Tokenizers 0.11.0"
] |
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] |
null | null |
transformers
|
#Rick DialoGPT Model
|
{"tags": ["conversational"]}
|
text-generation
|
grounddominator/DialoGPT-lar-Rick
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#Rick DialoGPT Model
|
[] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
[
51
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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] |
null | null |
transformers
|
# BioBERT-NLI
This is the model [BioBERT](https://github.com/dmis-lab/biobert) [1] fine-tuned on the [SNLI](https://nlp.stanford.edu/projects/snli/) and the [MultiNLI](https://www.nyu.edu/projects/bowman/multinli/) datasets using the [`sentence-transformers` library](https://github.com/UKPLab/sentence-transformers/) to produce universal sentence embeddings [2].
The model uses the original BERT wordpiece vocabulary and was trained using the **average pooling strategy** and a **softmax loss**.
**Base model**: `monologg/biobert_v1.1_pubmed` from HuggingFace's `AutoModel`.
**Training time**: ~6 hours on the NVIDIA Tesla P100 GPU provided in Kaggle Notebooks.
**Parameters**:
| Parameter | Value |
|------------------|-------|
| Batch size | 64 |
| Training steps | 30000 |
| Warmup steps | 1450 |
| Lowercasing | False |
| Max. Seq. Length | 128 |
**Performances**: The performance was evaluated on the test portion of the [STS dataset](http://ixa2.si.ehu.es/stswiki/index.php/STSbenchmark) using Spearman rank correlation and compared to the performances of a general BERT base model obtained with the same procedure to verify their similarity.
| Model | Score |
|-------------------------------|-------------|
| `biobert-nli` (this) | 73.40 |
| `gsarti/scibert-nli` | 74.50 |
| `bert-base-nli-mean-tokens`[3]| 77.12 |
An example usage for similarity-based scientific paper retrieval is provided in the [Covid Papers Browser](https://github.com/gsarti/covid-papers-browser) repository.
**References:**
[1] J. Lee et al, [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://academic.oup.com/bioinformatics/article/36/4/1234/5566506)
[2] A. Conneau et al., [Supervised Learning of Universal Sentence Representations from Natural Language Inference Data](https://www.aclweb.org/anthology/D17-1070/)
[3] N. Reimers et I. Gurevych, [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://www.aclweb.org/anthology/D19-1410/)
|
{}
|
feature-extraction
|
gsarti/biobert-nli
|
[
"transformers",
"pytorch",
"jax",
"bert",
"feature-extraction",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #jax #bert #feature-extraction #endpoints_compatible #region-us
|
BioBERT-NLI
===========
This is the model BioBERT [1] fine-tuned on the SNLI and the MultiNLI datasets using the 'sentence-transformers' library to produce universal sentence embeddings [2].
The model uses the original BERT wordpiece vocabulary and was trained using the average pooling strategy and a softmax loss.
Base model: 'monologg/biobert\_v1.1\_pubmed' from HuggingFace's 'AutoModel'.
Training time: ~6 hours on the NVIDIA Tesla P100 GPU provided in Kaggle Notebooks.
Parameters:
Performances: The performance was evaluated on the test portion of the STS dataset using Spearman rank correlation and compared to the performances of a general BERT base model obtained with the same procedure to verify their similarity.
An example usage for similarity-based scientific paper retrieval is provided in the Covid Papers Browser repository.
References:
[1] J. Lee et al, BioBERT: a pre-trained biomedical language representation model for biomedical text mining
[2] A. Conneau et al., Supervised Learning of Universal Sentence Representations from Natural Language Inference Data
[3] N. Reimers et I. Gurevych, Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks
|
[] |
[
"TAGS\n#transformers #pytorch #jax #bert #feature-extraction #endpoints_compatible #region-us \n"
] |
[
32
] |
[
"passage: TAGS\n#transformers #pytorch #jax #bert #feature-extraction #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
# CovidBERT-NLI
This is the model **CovidBERT** trained by DeepSet on AllenAI's [CORD19 Dataset](https://pages.semanticscholar.org/coronavirus-research) of scientific articles about coronaviruses.
The model uses the original BERT wordpiece vocabulary and was subsequently fine-tuned on the [SNLI](https://nlp.stanford.edu/projects/snli/) and the [MultiNLI](https://www.nyu.edu/projects/bowman/multinli/) datasets using the [`sentence-transformers` library](https://github.com/UKPLab/sentence-transformers/) to produce universal sentence embeddings [1] using the **average pooling strategy** and a **softmax loss**.
Parameter details for the original training on CORD-19 are available on [DeepSet's MLFlow](https://public-mlflow.deepset.ai/#/experiments/2/runs/ba27d00c30044ef6a33b1d307b4a6cba)
**Base model**: `deepset/covid_bert_base` from HuggingFace's `AutoModel`.
**Training time**: ~6 hours on the NVIDIA Tesla P100 GPU provided in Kaggle Notebooks.
**Parameters**:
| Parameter | Value |
|------------------|-------|
| Batch size | 64 |
| Training steps | 23000 |
| Warmup steps | 1450 |
| Lowercasing | True |
| Max. Seq. Length | 128 |
**Performances**: The performance was evaluated on the test portion of the [STS dataset](http://ixa2.si.ehu.es/stswiki/index.php/STSbenchmark) using Spearman rank correlation and compared to the performances of similar models obtained with the same procedure to verify its performances.
| Model | Score |
|-------------------------------|-------------|
| `covidbert-nli` (this) | 67.52 |
| `gsarti/biobert-nli` | 73.40 |
| `gsarti/scibert-nli` | 74.50 |
| `bert-base-nli-mean-tokens`[2]| 77.12 |
An example usage for similarity-based scientific paper retrieval is provided in the [Covid-19 Semantic Browser](https://github.com/gsarti/covid-papers-browser) repository.
**References:**
[1] A. Conneau et al., [Supervised Learning of Universal Sentence Representations from Natural Language Inference Data](https://www.aclweb.org/anthology/D17-1070/)
[2] N. Reimers et I. Gurevych, [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://www.aclweb.org/anthology/D19-1410/)
|
{}
|
feature-extraction
|
gsarti/covidbert-nli
|
[
"transformers",
"pytorch",
"jax",
"bert",
"feature-extraction",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #jax #bert #feature-extraction #endpoints_compatible #region-us
|
CovidBERT-NLI
=============
This is the model CovidBERT trained by DeepSet on AllenAI's CORD19 Dataset of scientific articles about coronaviruses.
The model uses the original BERT wordpiece vocabulary and was subsequently fine-tuned on the SNLI and the MultiNLI datasets using the 'sentence-transformers' library to produce universal sentence embeddings [1] using the average pooling strategy and a softmax loss.
Parameter details for the original training on CORD-19 are available on DeepSet's MLFlow
Base model: 'deepset/covid\_bert\_base' from HuggingFace's 'AutoModel'.
Training time: ~6 hours on the NVIDIA Tesla P100 GPU provided in Kaggle Notebooks.
Parameters:
Performances: The performance was evaluated on the test portion of the STS dataset using Spearman rank correlation and compared to the performances of similar models obtained with the same procedure to verify its performances.
An example usage for similarity-based scientific paper retrieval is provided in the Covid-19 Semantic Browser repository.
References:
[1] A. Conneau et al., Supervised Learning of Universal Sentence Representations from Natural Language Inference Data
[2] N. Reimers et I. Gurevych, Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks
|
[] |
[
"TAGS\n#transformers #pytorch #jax #bert #feature-extraction #endpoints_compatible #region-us \n"
] |
[
32
] |
[
"passage: TAGS\n#transformers #pytorch #jax #bert #feature-extraction #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
# Italian T5 Base (Oscar) 🇮🇹
*This repository contains the model formerly known as `gsarti/t5-base-it`*
The [IT5](https://huggingface.co/models?search=it5) model family represents the first effort in pretraining large-scale sequence-to-sequence transformer models for the Italian language, following the approach adopted by the original [T5 model](https://github.com/google-research/text-to-text-transfer-transformer).
This model is released as part of the project ["IT5: Large-Scale Text-to-Text Pretraining for Italian Language Understanding and Generation"](https://gsarti.com) (to be released), by [Gabriele Sarti](https://gsarti.com/) with the support of [Huggingface](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104) and with TPU usage sponsored by Google's [TPU Research Cloud](https://sites.research.google/trc/). All the training was conducted on a single TPU3v8-VM machine on Google Cloud. Refer to the Tensorboard tab of the repository for an overview of the training process.
*The inference widget is deactivated because the model needs a task-specific seq2seq fine-tuning on a downstream task to be useful in practice. The model [`gsarti/it5-base-nli`](https://huggingface.co/gsarti/it5-base-nli) provides an example of this model fine-tuned on a downstream NLI task.*
## Model variants
This repository contains the checkpoints for a `base` version of the model trained on the [OSCAR corpus](https://oscar-corpus.com/) using 🤗 Datasets. The original configuration for the model `t5-base` was adopted, with the exception of the parameter `dropout_rate` that was set at `0` instead of `0.1` during pre-training, following the implementation of [`t5-v1.1`](https://huggingface.co/google/t5-v1_1-base). The tokenizer is a `SentencePieceUnigramTokenizer` trained on the first 2M sentences of the Italian portion of the [`mC4`](https://huggingface.co/datasets/mc4) corpus. An improved version of the model trained on the [Thoroughly Cleaned Italian mC4 Corpus](https://huggingface.co/datasets/gsarti/clean_mc4_it) (~41B words, ~275GB) is also available under the name [`gsarti/it5-base`](https://huggingface.co/gsarti/it5-base). The training procedure is made available [on Github](https://github.com/gsarti/t5-flax-gcp).
The following table summarizes the parameters for all available models
| |`it5-small` |`it5-base` |`it5-large` |`it5-base-oscar` (this one) |
|-----------------------|-----------------------|----------------------|-----------------------|----------------------------------|
|`dataset` |`gsarti/clean_mc4_it` |`gsarti/clean_mc4_it` |`gsarti/clean_mc4_it` |`oscar/unshuffled_deduplicated_it`|
|`architecture` |`google/t5-v1_1-small` |`google/t5-v1_1-base` |`google/t5-v1_1-large` |`t5-base` |
|`learning rate` | 5e-3 | 5e-3 | 5e-3 | 1e-2 |
|`steps` | 1'050'000 | 1'050'000 | 2'100'000 | 258'000 |
|`training time` | 36 hours | 101 hours | 370 hours | 98 hours |
|`ff projection` |`gated-gelu` |`gated-gelu` |`gated-gelu` |`relu` |
|`tie embeds` |`false` |`false` |`false` |`true` |
|`optimizer` | adafactor | adafactor | adafactor | adafactor |
|`max seq. length` | 512 | 512 | 512 | 512 |
|`per-device batch size`| 16 | 16 | 8 | 16 |
|`tot. batch size` | 128 | 128 | 64 | 128 |
|`weigth decay` | 1e-3 | 1e-3 | 1e-2 | 1e-3 |
|`validation split size`| 15K examples | 15K examples | 15K examples | 15K examples |
The high training time of `it5-base-oscar` was due to [a bug](https://github.com/huggingface/transformers/pull/13012) in the training script.
For a list of individual model parameters, refer to the `config.json` file in the respective repositories.
## Using the models
```python
from transformers import T5Tokenizer, T5ForConditionalGeneration
tokenizer = T5Tokenizer.from_pretrained("gsarti/it5-base-oscar")
model = T5ForConditionalGeneration.from_pretrained("gsarti/it5-base-oscar")
```
*Note: You will need to fine-tune the model on your downstream seq2seq task to use it. See an example [here](https://huggingface.co/gsarti/it5-base-nli).*
Flax and Tensorflow versions of the model are also available:
```python
from transformers import FlaxT5ForConditionalGeneration, TFT5ForConditionalGeneration
model_flax = FlaxT5ForConditionalGeneration.from_pretrained("gsarti/it5-base-oscar")
model_tf = TFT5ForConditionalGeneration.from_pretrained("gsarti/it5-base-oscar")
```
## Limitations
Due to the nature of the web-scraped corpus on which IT5 models were trained, it is likely that their usage could reproduce and amplify pre-existing biases in the data, resulting in potentially harmful content such as racial or gender stereotypes and conspiracist views. For this reason, the study of such biases is explicitly encouraged, and model usage should ideally be restricted to research-oriented and non-user-facing endeavors.
## Model curators
For problems or updates on this model, please contact [[email protected]](mailto:[email protected]).
## Citation Information
```bibtex
@article{sarti-nissim-2022-it5,
title={IT5: Large-scale Text-to-text Pretraining for Italian Language Understanding and Generation},
author={Sarti, Gabriele and Nissim, Malvina},
journal={ArXiv preprint 2203.03759},
url={https://arxiv.org/abs/2203.03759},
year={2022},
month={mar}
}
```
|
{"language": ["it"], "license": "apache-2.0", "tags": ["seq2seq", "lm-head"], "datasets": ["oscar"], "inference": false}
|
text2text-generation
|
gsarti/it5-base-oscar
|
[
"transformers",
"pytorch",
"tf",
"jax",
"tensorboard",
"t5",
"text2text-generation",
"seq2seq",
"lm-head",
"it",
"dataset:oscar",
"arxiv:2203.03759",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2203.03759"
] |
[
"it"
] |
TAGS
#transformers #pytorch #tf #jax #tensorboard #t5 #text2text-generation #seq2seq #lm-head #it #dataset-oscar #arxiv-2203.03759 #license-apache-2.0 #autotrain_compatible #text-generation-inference #region-us
|
Italian T5 Base (Oscar) 🇮🇹
==========================
*This repository contains the model formerly known as 'gsarti/t5-base-it'*
The IT5 model family represents the first effort in pretraining large-scale sequence-to-sequence transformer models for the Italian language, following the approach adopted by the original T5 model.
This model is released as part of the project "IT5: Large-Scale Text-to-Text Pretraining for Italian Language Understanding and Generation" (to be released), by Gabriele Sarti with the support of Huggingface and with TPU usage sponsored by Google's TPU Research Cloud. All the training was conducted on a single TPU3v8-VM machine on Google Cloud. Refer to the Tensorboard tab of the repository for an overview of the training process.
*The inference widget is deactivated because the model needs a task-specific seq2seq fine-tuning on a downstream task to be useful in practice. The model 'gsarti/it5-base-nli' provides an example of this model fine-tuned on a downstream NLI task.*
Model variants
--------------
This repository contains the checkpoints for a 'base' version of the model trained on the OSCAR corpus using Datasets. The original configuration for the model 't5-base' was adopted, with the exception of the parameter 'dropout\_rate' that was set at '0' instead of '0.1' during pre-training, following the implementation of 't5-v1.1'. The tokenizer is a 'SentencePieceUnigramTokenizer' trained on the first 2M sentences of the Italian portion of the 'mC4' corpus. An improved version of the model trained on the Thoroughly Cleaned Italian mC4 Corpus (~41B words, ~275GB) is also available under the name 'gsarti/it5-base'. The training procedure is made available on Github.
The following table summarizes the parameters for all available models
The high training time of 'it5-base-oscar' was due to a bug in the training script.
For a list of individual model parameters, refer to the 'URL' file in the respective repositories.
Using the models
----------------
*Note: You will need to fine-tune the model on your downstream seq2seq task to use it. See an example here.*
Flax and Tensorflow versions of the model are also available:
Limitations
-----------
Due to the nature of the web-scraped corpus on which IT5 models were trained, it is likely that their usage could reproduce and amplify pre-existing biases in the data, resulting in potentially harmful content such as racial or gender stereotypes and conspiracist views. For this reason, the study of such biases is explicitly encouraged, and model usage should ideally be restricted to research-oriented and non-user-facing endeavors.
Model curators
--------------
For problems or updates on this model, please contact gabriele.sarti996@URL.
|
[] |
[
"TAGS\n#transformers #pytorch #tf #jax #tensorboard #t5 #text2text-generation #seq2seq #lm-head #it #dataset-oscar #arxiv-2203.03759 #license-apache-2.0 #autotrain_compatible #text-generation-inference #region-us \n"
] |
[
85
] |
[
"passage: TAGS\n#transformers #pytorch #tf #jax #tensorboard #t5 #text2text-generation #seq2seq #lm-head #it #dataset-oscar #arxiv-2203.03759 #license-apache-2.0 #autotrain_compatible #text-generation-inference #region-us \n"
] |
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null | null |
transformers
|
# Italian T5 Base 🇮🇹
The [IT5](https://huggingface.co/models?search=it5) model family represents the first effort in pretraining large-scale sequence-to-sequence transformer models for the Italian language, following the approach adopted by the original [T5 model](https://github.com/google-research/text-to-text-transfer-transformer).
This model is released as part of the project ["IT5: Large-Scale Text-to-Text Pretraining for Italian Language Understanding and Generation"](https://arxiv.org/abs/2203.03759), by [Gabriele Sarti](https://gsarti.com/) and [Malvina Nissim](https://malvinanissim.github.io/) with the support of [Huggingface](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104) and with TPU usage sponsored by Google's [TPU Research Cloud](https://sites.research.google/trc/). All the training was conducted on a single TPU3v8-VM machine on Google Cloud. Refer to the Tensorboard tab of the repository for an overview of the training process.
*TThe inference widget is deactivated because the model needs a task-specific seq2seq fine-tuning on a downstream task to be useful in practice. The models in the [`it5`](https://huggingface.co/it5) organization provide some examples of this model fine-tuned on various downstream task.*
## Model variants
This repository contains the checkpoints for the `base` version of the model. The model was trained for one epoch (1.05M steps) on the [Thoroughly Cleaned Italian mC4 Corpus](https://huggingface.co/datasets/gsarti/clean_mc4_it) (~41B words, ~275GB) using 🤗 Datasets and the `google/t5-v1_1-base` improved configuration. Another version of this model trained on the [OSCAR corpus](https://oscar-corpus.com/) is also available under the name [`gsarti/it5-base-oscar`](https://huggingface.co/gsartiit5-base-oscar). The training procedure is made available [on Github](https://github.com/gsarti/t5-flax-gcp).
The following table summarizes the parameters for all available models
| |`it5-small` |`it5-base` (this one) |`it5-large` |`it5-base-oscar` |
|-----------------------|-----------------------|----------------------|-----------------------|----------------------------------|
|`dataset` |`gsarti/clean_mc4_it` |`gsarti/clean_mc4_it` |`gsarti/clean_mc4_it` |`oscar/unshuffled_deduplicated_it`|
|`architecture` |`google/t5-v1_1-small` |`google/t5-v1_1-base` |`google/t5-v1_1-large` |`t5-base` |
|`learning rate` | 5e-3 | 5e-3 | 5e-3 | 1e-2 |
|`steps` | 1'050'000 | 1'050'000 | 2'100'000 | 258'000 |
|`training time` | 36 hours | 101 hours | 370 hours | 98 hours |
|`ff projection` |`gated-gelu` |`gated-gelu` |`gated-gelu` |`relu` |
|`tie embeds` |`false` |`false` |`false` |`true` |
|`optimizer` | adafactor | adafactor | adafactor | adafactor |
|`max seq. length` | 512 | 512 | 512 | 512 |
|`per-device batch size`| 16 | 16 | 8 | 16 |
|`tot. batch size` | 128 | 128 | 64 | 128 |
|`weigth decay` | 1e-3 | 1e-3 | 1e-2 | 1e-3 |
|`validation split size`| 15K examples | 15K examples | 15K examples | 15K examples |
The high training time of `it5-base-oscar` was due to [a bug](https://github.com/huggingface/transformers/pull/13012) in the training script.
For a list of individual model parameters, refer to the `config.json` file in the respective repositories.
## Using the models
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("gsarti/it5-base")
model = AutoModelForSeq2SeqLM.from_pretrained("gsarti/it5-base")
```
*Note: You will need to fine-tune the model on your downstream seq2seq task to use it. See an example [here](https://huggingface.co/it5/it5-base-news-summarization).*
Flax and Tensorflow versions of the model are also available:
```python
from transformers import FlaxT5ForConditionalGeneration, TFT5ForConditionalGeneration
model_flax = FlaxT5ForConditionalGeneration.from_pretrained("gsarti/it5-base")
model_tf = TFT5ForConditionalGeneration.from_pretrained("gsarti/it5-base")
```
## Limitations
Due to the nature of the web-scraped corpus on which IT5 models were trained, it is likely that their usage could reproduce and amplify pre-existing biases in the data, resulting in potentially harmful content such as racial or gender stereotypes and conspiracist views. For this reason, the study of such biases is explicitly encouraged, and model usage should ideally be restricted to research-oriented and non-user-facing endeavors.
## Model curators
For problems or updates on this model, please contact [[email protected]](mailto:[email protected]).
## Citation Information
```bibtex
@article{sarti-nissim-2022-it5,
title={IT5: Large-scale Text-to-text Pretraining for Italian Language Understanding and Generation},
author={Sarti, Gabriele and Nissim, Malvina},
journal={ArXiv preprint 2203.03759},
url={https://arxiv.org/abs/2203.03759},
year={2022},
month={mar}
}
```
|
{"language": ["it"], "license": "apache-2.0", "tags": ["seq2seq", "lm-head"], "datasets": ["gsarti/clean_mc4_it"], "inference": false, "thumbnail": "https://gsarti.com/publication/it5/featured.png"}
|
text2text-generation
|
gsarti/it5-base
|
[
"transformers",
"pytorch",
"tf",
"jax",
"tensorboard",
"t5",
"text2text-generation",
"seq2seq",
"lm-head",
"it",
"dataset:gsarti/clean_mc4_it",
"arxiv:2203.03759",
"license:apache-2.0",
"autotrain_compatible",
"has_space",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2203.03759"
] |
[
"it"
] |
TAGS
#transformers #pytorch #tf #jax #tensorboard #t5 #text2text-generation #seq2seq #lm-head #it #dataset-gsarti/clean_mc4_it #arxiv-2203.03759 #license-apache-2.0 #autotrain_compatible #has_space #text-generation-inference #region-us
|
Italian T5 Base 🇮🇹
==================
The IT5 model family represents the first effort in pretraining large-scale sequence-to-sequence transformer models for the Italian language, following the approach adopted by the original T5 model.
This model is released as part of the project "IT5: Large-Scale Text-to-Text Pretraining for Italian Language Understanding and Generation", by Gabriele Sarti and Malvina Nissim with the support of Huggingface and with TPU usage sponsored by Google's TPU Research Cloud. All the training was conducted on a single TPU3v8-VM machine on Google Cloud. Refer to the Tensorboard tab of the repository for an overview of the training process.
*TThe inference widget is deactivated because the model needs a task-specific seq2seq fine-tuning on a downstream task to be useful in practice. The models in the 'it5' organization provide some examples of this model fine-tuned on various downstream task.*
Model variants
--------------
This repository contains the checkpoints for the 'base' version of the model. The model was trained for one epoch (1.05M steps) on the Thoroughly Cleaned Italian mC4 Corpus (~41B words, ~275GB) using Datasets and the 'google/t5-v1\_1-base' improved configuration. Another version of this model trained on the OSCAR corpus is also available under the name 'gsarti/it5-base-oscar'. The training procedure is made available on Github.
The following table summarizes the parameters for all available models
The high training time of 'it5-base-oscar' was due to a bug in the training script.
For a list of individual model parameters, refer to the 'URL' file in the respective repositories.
Using the models
----------------
*Note: You will need to fine-tune the model on your downstream seq2seq task to use it. See an example here.*
Flax and Tensorflow versions of the model are also available:
Limitations
-----------
Due to the nature of the web-scraped corpus on which IT5 models were trained, it is likely that their usage could reproduce and amplify pre-existing biases in the data, resulting in potentially harmful content such as racial or gender stereotypes and conspiracist views. For this reason, the study of such biases is explicitly encouraged, and model usage should ideally be restricted to research-oriented and non-user-facing endeavors.
Model curators
--------------
For problems or updates on this model, please contact gabriele.sarti996@URL.
|
[] |
[
"TAGS\n#transformers #pytorch #tf #jax #tensorboard #t5 #text2text-generation #seq2seq #lm-head #it #dataset-gsarti/clean_mc4_it #arxiv-2203.03759 #license-apache-2.0 #autotrain_compatible #has_space #text-generation-inference #region-us \n"
] |
[
98
] |
[
"passage: TAGS\n#transformers #pytorch #tf #jax #tensorboard #t5 #text2text-generation #seq2seq #lm-head #it #dataset-gsarti/clean_mc4_it #arxiv-2203.03759 #license-apache-2.0 #autotrain_compatible #has_space #text-generation-inference #region-us \n"
] |
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null | null |
transformers
|
# Italian T5 Large 🇮🇹
The [IT5](https://huggingface.co/models?search=it5) model family represents the first effort in pretraining large-scale sequence-to-sequence transformer models for the Italian language, following the approach adopted by the original [T5 model](https://github.com/google-research/text-to-text-transfer-transformer).
This model is released as part of the project ["IT5: Large-Scale Text-to-Text Pretraining for Italian Language Understanding and Generation"](https://arxiv.org/abs/2203.03759) (to be released), by [Gabriele Sarti](https://gsarti.com/) and [Malvina Nissim](https://malvinanissim.github.io/) with the support of [Huggingface](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104) and with TPU usage sponsored by Google's [TPU Research Cloud](https://sites.research.google/trc/). All the training was conducted on a single TPU3v8-VM machine on Google Cloud. Refer to the Tensorboard tab of the repository for an overview of the training process.
*The inference widget is deactivated because the model needs a task-specific seq2seq fine-tuning on a downstream task to be useful in practice. The models in the [`it5`](https://huggingface.co/it5) organization provide some examples of this model fine-tuned on various downstream task.*
## Model variants
This repository contains the checkpoints for the `base` version of the model. The model was trained for one epoch (1.05M steps) on the [Thoroughly Cleaned Italian mC4 Corpus](https://huggingface.co/datasets/gsarti/clean_mc4_it) (~41B words, ~275GB) using 🤗 Datasets and the `google/t5-v1_1-large` improved configuration. The training procedure is made available [on Github](https://github.com/gsarti/t5-flax-gcp).
The following table summarizes the parameters for all available models
| |`it5-small` |`it5-base` |`it5-large` (this one) |`it5-base-oscar` |
|-----------------------|-----------------------|----------------------|-----------------------|----------------------------------|
|`dataset` |`gsarti/clean_mc4_it` |`gsarti/clean_mc4_it` |`gsarti/clean_mc4_it` |`oscar/unshuffled_deduplicated_it`|
|`architecture` |`google/t5-v1_1-small` |`google/t5-v1_1-base` |`google/t5-v1_1-large` |`t5-base` |
|`learning rate` | 5e-3 | 5e-3 | 5e-3 | 1e-2 |
|`steps` | 1'050'000 | 1'050'000 | 2'100'000 | 258'000 |
|`training time` | 36 hours | 101 hours | 370 hours | 98 hours |
|`ff projection` |`gated-gelu` |`gated-gelu` |`gated-gelu` |`relu` |
|`tie embeds` |`false` |`false` |`false` |`true` |
|`optimizer` | adafactor | adafactor | adafactor | adafactor |
|`max seq. length` | 512 | 512 | 512 | 512 |
|`per-device batch size`| 16 | 16 | 8 | 16 |
|`tot. batch size` | 128 | 128 | 64 | 128 |
|`weigth decay` | 1e-3 | 1e-3 | 1e-2 | 1e-3 |
|`validation split size`| 15K examples | 15K examples | 15K examples | 15K examples |
The high training time of `it5-base-oscar` was due to [a bug](https://github.com/huggingface/transformers/pull/13012) in the training script.
For a list of individual model parameters, refer to the `config.json` file in the respective repositories.
## Using the models
```python
from transformers import AutoTokenzier, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("gsarti/it5-large")
model = AutoModelForSeq2SeqLM.from_pretrained("gsarti/it5-large")
```
*Note: You will need to fine-tune the model on your downstream seq2seq task to use it. See an example [here](https://huggingface.co/gsarti/it5-base-nli).*
Flax and Tensorflow versions of the model are also available:
```python
from transformers import FlaxT5ForConditionalGeneration, TFT5ForConditionalGeneration
model_flax = FlaxT5ForConditionalGeneration.from_pretrained("gsarti/it5-large")
model_tf = TFT5ForConditionalGeneration.from_pretrained("gsarti/it5-large")
```
## Limitations
Due to the nature of the web-scraped corpus on which IT5 models were trained, it is likely that their usage could reproduce and amplify pre-existing biases in the data, resulting in potentially harmful content such as racial or gender stereotypes and conspiracist views. For this reason, the study of such biases is explicitly encouraged, and model usage should ideally be restricted to research-oriented and non-user-facing endeavors.
## Model curators
For problems or updates on this model, please contact [[email protected]](mailto:[email protected]).
## Citation Information
```bibtex
@article{sarti-nissim-2022-it5,
title={IT5: Large-scale Text-to-text Pretraining for Italian Language Understanding and Generation},
author={Sarti, Gabriele and Nissim, Malvina},
journal={ArXiv preprint 2203.03759},
url={https://arxiv.org/abs/2203.03759},
year={2022},
month={mar}
}
```
|
{"language": ["it"], "license": "apache-2.0", "tags": ["seq2seq", "lm-head"], "datasets": ["gsarti/clean_mc4_it"], "inference": false, "thumbnail": "https://gsarti.com/publication/it5/featured.png"}
|
text2text-generation
|
gsarti/it5-large
|
[
"transformers",
"pytorch",
"tf",
"jax",
"tensorboard",
"t5",
"text2text-generation",
"seq2seq",
"lm-head",
"it",
"dataset:gsarti/clean_mc4_it",
"arxiv:2203.03759",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2203.03759"
] |
[
"it"
] |
TAGS
#transformers #pytorch #tf #jax #tensorboard #t5 #text2text-generation #seq2seq #lm-head #it #dataset-gsarti/clean_mc4_it #arxiv-2203.03759 #license-apache-2.0 #autotrain_compatible #text-generation-inference #region-us
|
Italian T5 Large 🇮🇹
===================
The IT5 model family represents the first effort in pretraining large-scale sequence-to-sequence transformer models for the Italian language, following the approach adopted by the original T5 model.
This model is released as part of the project "IT5: Large-Scale Text-to-Text Pretraining for Italian Language Understanding and Generation" (to be released), by Gabriele Sarti and Malvina Nissim with the support of Huggingface and with TPU usage sponsored by Google's TPU Research Cloud. All the training was conducted on a single TPU3v8-VM machine on Google Cloud. Refer to the Tensorboard tab of the repository for an overview of the training process.
*The inference widget is deactivated because the model needs a task-specific seq2seq fine-tuning on a downstream task to be useful in practice. The models in the 'it5' organization provide some examples of this model fine-tuned on various downstream task.*
Model variants
--------------
This repository contains the checkpoints for the 'base' version of the model. The model was trained for one epoch (1.05M steps) on the Thoroughly Cleaned Italian mC4 Corpus (~41B words, ~275GB) using Datasets and the 'google/t5-v1\_1-large' improved configuration. The training procedure is made available on Github.
The following table summarizes the parameters for all available models
The high training time of 'it5-base-oscar' was due to a bug in the training script.
For a list of individual model parameters, refer to the 'URL' file in the respective repositories.
Using the models
----------------
*Note: You will need to fine-tune the model on your downstream seq2seq task to use it. See an example here.*
Flax and Tensorflow versions of the model are also available:
Limitations
-----------
Due to the nature of the web-scraped corpus on which IT5 models were trained, it is likely that their usage could reproduce and amplify pre-existing biases in the data, resulting in potentially harmful content such as racial or gender stereotypes and conspiracist views. For this reason, the study of such biases is explicitly encouraged, and model usage should ideally be restricted to research-oriented and non-user-facing endeavors.
Model curators
--------------
For problems or updates on this model, please contact gabriele.sarti996@URL.
|
[] |
[
"TAGS\n#transformers #pytorch #tf #jax #tensorboard #t5 #text2text-generation #seq2seq #lm-head #it #dataset-gsarti/clean_mc4_it #arxiv-2203.03759 #license-apache-2.0 #autotrain_compatible #text-generation-inference #region-us \n"
] |
[
94
] |
[
"passage: TAGS\n#transformers #pytorch #tf #jax #tensorboard #t5 #text2text-generation #seq2seq #lm-head #it #dataset-gsarti/clean_mc4_it #arxiv-2203.03759 #license-apache-2.0 #autotrain_compatible #text-generation-inference #region-us \n"
] |
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null | null |
transformers
|
# Italian T5 Small 🇮🇹
The [IT5](https://huggingface.co/models?search=it5) model family represents the first effort in pretraining large-scale sequence-to-sequence transformer models for the Italian language, following the approach adopted by the original [T5 model](https://github.com/google-research/text-to-text-transfer-transformer).
This model is released as part of the project ["IT5: Large-Scale Text-to-Text Pretraining for Italian Language Understanding and Generation"](https://arxiv.org/abs/2203.03759), by [Gabriele Sarti](https://gsarti.com/) and [Malvina Nissim](https://malvinanissim.github.io/) with the support of [Huggingface](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104) and with TPU usage sponsored by Google's [TPU Research Cloud](https://sites.research.google/trc/). All the training was conducted on a single TPU3v8-VM machine on Google Cloud. Refer to the Tensorboard tab of the repository for an overview of the training process.
*The inference widget is deactivated because the model needs a task-specific seq2seq fine-tuning on a downstream task to be useful in practice. The models in the [`it5`](https://huggingface.co/it5) organization provide some examples of this model fine-tuned on various downstream task.*
## Model variants
This repository contains the checkpoints for the `base` version of the model. The model was trained for one epoch (1.05M steps) on the [Thoroughly Cleaned Italian mC4 Corpus](https://huggingface.co/datasets/gsarti/clean_mc4_it) (~41B words, ~275GB) using 🤗 Datasets and the `google/t5-v1_1-small` improved configuration. The training procedure is made available [on Github](https://github.com/gsarti/t5-flax-gcp).
The following table summarizes the parameters for all available models
| |`it5-small` (this one) |`it5-base` |`it5-large` |`it5-base-oscar` |
|-----------------------|-----------------------|----------------------|-----------------------|----------------------------------|
|`dataset` |`gsarti/clean_mc4_it` |`gsarti/clean_mc4_it` |`gsarti/clean_mc4_it` |`oscar/unshuffled_deduplicated_it`|
|`architecture` |`google/t5-v1_1-small` |`google/t5-v1_1-base` |`google/t5-v1_1-large` |`t5-base` |
|`learning rate` | 5e-3 | 5e-3 | 5e-3 | 1e-2 |
|`steps` | 1'050'000 | 1'050'000 | 2'100'000 | 258'000 |
|`training time` | 36 hours | 101 hours | 370 hours | 98 hours |
|`ff projection` |`gated-gelu` |`gated-gelu` |`gated-gelu` |`relu` |
|`tie embeds` |`false` |`false` |`false` |`true` |
|`optimizer` | adafactor | adafactor | adafactor | adafactor |
|`max seq. length` | 512 | 512 | 512 | 512 |
|`per-device batch size`| 16 | 16 | 8 | 16 |
|`tot. batch size` | 128 | 128 | 64 | 128 |
|`weigth decay` | 1e-3 | 1e-3 | 1e-2 | 1e-3 |
|`validation split size`| 15K examples | 15K examples | 15K examples | 15K examples |
The high training time of `it5-base-oscar` was due to [a bug](https://github.com/huggingface/transformers/pull/13012) in the training script.
For a list of individual model parameters, refer to the `config.json` file in the respective repositories.
## Using the models
```python
from transformers import AutoTokenzier, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("gsarti/it5-small")
model = AutoModelForSeq2SeqLM.from_pretrained("gsarti/it5-small")
```
*Note: You will need to fine-tune the model on your downstream seq2seq task to use it. See an example [here](https://huggingface.co/it5/it5-base-question-answering).*
Flax and Tensorflow versions of the model are also available:
```python
from transformers import FlaxT5ForConditionalGeneration, TFT5ForConditionalGeneration
model_flax = FlaxT5ForConditionalGeneration.from_pretrained("gsarti/it5-small")
model_tf = TFT5ForConditionalGeneration.from_pretrained("gsarti/it5-small")
```
## Limitations
Due to the nature of the web-scraped corpus on which IT5 models were trained, it is likely that their usage could reproduce and amplify pre-existing biases in the data, resulting in potentially harmful content such as racial or gender stereotypes and conspiracist views. For this reason, the study of such biases is explicitly encouraged, and model usage should ideally be restricted to research-oriented and non-user-facing endeavors.
## Model curators
For problems or updates on this model, please contact [[email protected]](mailto:[email protected]).
## Citation Information
```bibtex
@article{sarti-nissim-2022-it5,
title={IT5: Large-scale Text-to-text Pretraining for Italian Language Understanding and Generation},
author={Sarti, Gabriele and Nissim, Malvina},
journal={ArXiv preprint 2203.03759},
url={https://arxiv.org/abs/2203.03759},
year={2022},
month={mar}
}
```
|
{"language": ["it"], "license": "apache-2.0", "tags": ["seq2seq", "lm-head"], "datasets": ["gsarti/clean_mc4_it"], "inference": false, "thumbnail": "https://gsarti.com/publication/it5/featured.png"}
|
text2text-generation
|
gsarti/it5-small
|
[
"transformers",
"pytorch",
"tf",
"jax",
"tensorboard",
"t5",
"text2text-generation",
"seq2seq",
"lm-head",
"it",
"dataset:gsarti/clean_mc4_it",
"arxiv:2203.03759",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2203.03759"
] |
[
"it"
] |
TAGS
#transformers #pytorch #tf #jax #tensorboard #t5 #text2text-generation #seq2seq #lm-head #it #dataset-gsarti/clean_mc4_it #arxiv-2203.03759 #license-apache-2.0 #autotrain_compatible #text-generation-inference #region-us
|
Italian T5 Small 🇮🇹
===================
The IT5 model family represents the first effort in pretraining large-scale sequence-to-sequence transformer models for the Italian language, following the approach adopted by the original T5 model.
This model is released as part of the project "IT5: Large-Scale Text-to-Text Pretraining for Italian Language Understanding and Generation", by Gabriele Sarti and Malvina Nissim with the support of Huggingface and with TPU usage sponsored by Google's TPU Research Cloud. All the training was conducted on a single TPU3v8-VM machine on Google Cloud. Refer to the Tensorboard tab of the repository for an overview of the training process.
*The inference widget is deactivated because the model needs a task-specific seq2seq fine-tuning on a downstream task to be useful in practice. The models in the 'it5' organization provide some examples of this model fine-tuned on various downstream task.*
Model variants
--------------
This repository contains the checkpoints for the 'base' version of the model. The model was trained for one epoch (1.05M steps) on the Thoroughly Cleaned Italian mC4 Corpus (~41B words, ~275GB) using Datasets and the 'google/t5-v1\_1-small' improved configuration. The training procedure is made available on Github.
The following table summarizes the parameters for all available models
The high training time of 'it5-base-oscar' was due to a bug in the training script.
For a list of individual model parameters, refer to the 'URL' file in the respective repositories.
Using the models
----------------
*Note: You will need to fine-tune the model on your downstream seq2seq task to use it. See an example here.*
Flax and Tensorflow versions of the model are also available:
Limitations
-----------
Due to the nature of the web-scraped corpus on which IT5 models were trained, it is likely that their usage could reproduce and amplify pre-existing biases in the data, resulting in potentially harmful content such as racial or gender stereotypes and conspiracist views. For this reason, the study of such biases is explicitly encouraged, and model usage should ideally be restricted to research-oriented and non-user-facing endeavors.
Model curators
--------------
For problems or updates on this model, please contact gabriele.sarti996@URL.
|
[] |
[
"TAGS\n#transformers #pytorch #tf #jax #tensorboard #t5 #text2text-generation #seq2seq #lm-head #it #dataset-gsarti/clean_mc4_it #arxiv-2203.03759 #license-apache-2.0 #autotrain_compatible #text-generation-inference #region-us \n"
] |
[
94
] |
[
"passage: TAGS\n#transformers #pytorch #tf #jax #tensorboard #t5 #text2text-generation #seq2seq #lm-head #it #dataset-gsarti/clean_mc4_it #arxiv-2203.03759 #license-apache-2.0 #autotrain_compatible #text-generation-inference #region-us \n"
] |
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] |
null | null |
transformers
|
# SciBERT-NLI
This is the model [SciBERT](https://github.com/allenai/scibert) [1] fine-tuned on the [SNLI](https://nlp.stanford.edu/projects/snli/) and the [MultiNLI](https://www.nyu.edu/projects/bowman/multinli/) datasets using the [`sentence-transformers` library](https://github.com/UKPLab/sentence-transformers/) to produce universal sentence embeddings [2].
The model uses the original `scivocab` wordpiece vocabulary and was trained using the **average pooling strategy** and a **softmax loss**.
**Base model**: `allenai/scibert-scivocab-cased` from HuggingFace's `AutoModel`.
**Training time**: ~4 hours on the NVIDIA Tesla P100 GPU provided in Kaggle Notebooks.
**Parameters**:
| Parameter | Value |
|------------------|-------|
| Batch size | 64 |
| Training steps | 20000 |
| Warmup steps | 1450 |
| Lowercasing | True |
| Max. Seq. Length | 128 |
**Performances**: The performance was evaluated on the test portion of the [STS dataset](http://ixa2.si.ehu.es/stswiki/index.php/STSbenchmark) using Spearman rank correlation and compared to the performances of a general BERT base model obtained with the same procedure to verify their similarity.
| Model | Score |
|-------------------------------|-------------|
| `scibert-nli` (this) | 74.50 |
| `bert-base-nli-mean-tokens`[3]| 77.12 |
An example usage for similarity-based scientific paper retrieval is provided in the [Covid Papers Browser](https://github.com/gsarti/covid-papers-browser) repository.
**References:**
[1] I. Beltagy et al, [SciBERT: A Pretrained Language Model for Scientific Text](https://www.aclweb.org/anthology/D19-1371/)
[2] A. Conneau et al., [Supervised Learning of Universal Sentence Representations from Natural Language Inference Data](https://www.aclweb.org/anthology/D17-1070/)
[3] N. Reimers et I. Gurevych, [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://www.aclweb.org/anthology/D19-1410/)
|
{}
|
feature-extraction
|
gsarti/scibert-nli
|
[
"transformers",
"pytorch",
"jax",
"bert",
"feature-extraction",
"doi:10.57967/hf/0038",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #jax #bert #feature-extraction #doi-10.57967/hf/0038 #endpoints_compatible #region-us
|
SciBERT-NLI
===========
This is the model SciBERT [1] fine-tuned on the SNLI and the MultiNLI datasets using the 'sentence-transformers' library to produce universal sentence embeddings [2].
The model uses the original 'scivocab' wordpiece vocabulary and was trained using the average pooling strategy and a softmax loss.
Base model: 'allenai/scibert-scivocab-cased' from HuggingFace's 'AutoModel'.
Training time: ~4 hours on the NVIDIA Tesla P100 GPU provided in Kaggle Notebooks.
Parameters:
Performances: The performance was evaluated on the test portion of the STS dataset using Spearman rank correlation and compared to the performances of a general BERT base model obtained with the same procedure to verify their similarity.
An example usage for similarity-based scientific paper retrieval is provided in the Covid Papers Browser repository.
References:
[1] I. Beltagy et al, SciBERT: A Pretrained Language Model for Scientific Text
[2] A. Conneau et al., Supervised Learning of Universal Sentence Representations from Natural Language Inference Data
[3] N. Reimers et I. Gurevych, Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks
|
[] |
[
"TAGS\n#transformers #pytorch #jax #bert #feature-extraction #doi-10.57967/hf/0038 #endpoints_compatible #region-us \n"
] |
[
45
] |
[
"passage: TAGS\n#transformers #pytorch #jax #bert #feature-extraction #doi-10.57967/hf/0038 #endpoints_compatible #region-us \n"
] |
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null | null |
generic
| ERROR: type should be string, got "\nhttps://github.com/borisdayma/dalle-mini" |
{"language": ["en"], "library_name": "generic", "pipeline_tag": "text-to-image"}
|
text-to-image
|
gsurma/ai_dreamer
|
[
"generic",
"jax",
"bart",
"text-to-image",
"en",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#generic #jax #bart #text-to-image #en #region-us
|
URL
|
[] |
[
"TAGS\n#generic #jax #bart #text-to-image #en #region-us \n"
] |
[
22
] |
[
"passage: TAGS\n#generic #jax #bart #text-to-image #en #region-us \n"
] |
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] |
null | null |
transformers
|
# dummy model
This is a dummy model
|
{}
|
fill-mask
|
gulabpatel/new-dummy-model
|
[
"transformers",
"pytorch",
"camembert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #camembert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
|
# dummy model
This is a dummy model
|
[
"# dummy model\n\nThis is a dummy model"
] |
[
"TAGS\n#transformers #pytorch #camembert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"# dummy model\n\nThis is a dummy model"
] |
[
38,
10
] |
[
"passage: TAGS\n#transformers #pytorch #camembert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n# dummy model\n\nThis is a dummy model"
] |
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] |
null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4872
- Wer: 0.3417
## 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: 32
- 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: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.4857 | 4.0 | 500 | 1.4555 | 1.0040 |
| 0.5994 | 8.0 | 1000 | 0.5011 | 0.4370 |
| 0.2273 | 12.0 | 1500 | 0.4293 | 0.3903 |
| 0.1235 | 16.0 | 2000 | 0.4602 | 0.3772 |
| 0.084 | 20.0 | 2500 | 0.5055 | 0.3673 |
| 0.0615 | 24.0 | 3000 | 0.4915 | 0.3486 |
| 0.0468 | 28.0 | 3500 | 0.4872 | 0.3417 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.13.3
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-demo-colab", "results": []}]}
|
automatic-speech-recognition
|
gullenasatish/wav2vec2-base-timit-demo-colab
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
|
wav2vec2-base-timit-demo-colab
==============================
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4872
* Wer: 0.3417
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: 32
* 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: 30
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.11.3
* Pytorch 1.10.0+cu111
* Datasets 1.13.3
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 30\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.10.0+cu111\n* Datasets 1.13.3\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 30\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.10.0+cu111\n* Datasets 1.13.3\n* Tokenizers 0.10.3"
] |
[
56,
130,
4,
33
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 30\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.10.0+cu111\n* Datasets 1.13.3\n* Tokenizers 0.10.3"
] |
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null | null |
transformers
|
<!-- 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. -->
# gunghio/distilbert-base-multilingual-cased-finetuned-conll2003-ner
This model was trained from scratch on an conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0388
- Precision: 0.9360
- Recall: 0.9458
- F1: 0.9409
- Accuracy: 0.9902
## Model description
It is based on distilbert-base-multilingual-cased
## Intended uses & limitations
More information needed
## Training and evaluation data
Training dataset: [conll2003](https://huggingface.co/datasets/conll2003)
## 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1653 | 1.0 | 878 | 0.0465 | 0.9267 | 0.9300 | 0.9283 | 0.9883 |
| 0.0322 | 2.0 | 1756 | 0.0404 | 0.9360 | 0.9431 | 0.9396 | 0.9897 |
| 0.0185 | 3.0 | 2634 | 0.0388 | 0.9360 | 0.9458 | 0.9409 | 0.9902 |
### Framework versions
- Transformers 4.6.1
- Pytorch 1.8.1+cu101
- Datasets 1.6.2
- Tokenizers 0.10.2
|
{"language": ["en", "de", "nl", "es", "multilingual"], "datasets": ["conll2003"], "metrics": [{"precision": 0.936}, {"recall": 0.9458}, {"f1": 0.9409}, {"accuracy": 0.9902}], "model-index": [{"name": "gunghio/distilbert-base-multilingual-cased-finetuned-conll2003-ner", "results": [{"task": {"type": "ner", "name": "Named Entity Recognition"}, "dataset": {"name": "ConLL 2003", "type": "conll2003"}, "metrics": [{"type": "f1-score", "value": 0.9409}]}]}]}
|
token-classification
|
gunghio/distilbert-base-multilingual-cased-finetuned-conll2003-ner
|
[
"transformers",
"pytorch",
"distilbert",
"token-classification",
"en",
"de",
"nl",
"es",
"multilingual",
"dataset:conll2003",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"en",
"de",
"nl",
"es",
"multilingual"
] |
TAGS
#transformers #pytorch #distilbert #token-classification #en #de #nl #es #multilingual #dataset-conll2003 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
gunghio/distilbert-base-multilingual-cased-finetuned-conll2003-ner
==================================================================
This model was trained from scratch on an conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0388
* Precision: 0.9360
* Recall: 0.9458
* F1: 0.9409
* Accuracy: 0.9902
Model description
-----------------
It is based on distilbert-base-multilingual-cased
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
Training dataset: conll2003
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: 3
### Training results
### Framework versions
* Transformers 4.6.1
* Pytorch 1.8.1+cu101
* Datasets 1.6.2
* Tokenizers 0.10.2
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.6.1\n* Pytorch 1.8.1+cu101\n* Datasets 1.6.2\n* Tokenizers 0.10.2"
] |
[
"TAGS\n#transformers #pytorch #distilbert #token-classification #en #de #nl #es #multilingual #dataset-conll2003 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.6.1\n* Pytorch 1.8.1+cu101\n* Datasets 1.6.2\n* Tokenizers 0.10.2"
] |
[
62,
98,
4,
33
] |
[
"passage: TAGS\n#transformers #pytorch #distilbert #token-classification #en #de #nl #es #multilingual #dataset-conll2003 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3### Training results### Framework versions\n\n\n* Transformers 4.6.1\n* Pytorch 1.8.1+cu101\n* Datasets 1.6.2\n* Tokenizers 0.10.2"
] |
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] |
null | null |
transformers
|
This model is a fine-tuned version of [Helsinki-NLP/opus-tatoeba-es-zh](https://huggingface.co/Helsinki-NLP/opus-tatoeba-es-zh) on a dataset of legal domain constructed by the author himself.
# Intended uses & limitations
This model is the result of the master graduation thesis for the Tradumatics: Translation Technologies program at the Autonomous University of Barcelona.
Please refer to the GitHub repo created for this thesis for the full-text and relative open-sourced materials: https://github.com/guocheng98/MUTTT2020_TFM_ZGC
The thesis intends to explain various theories and certain algorithm details about neural machine translation, thus this fine-tuned model only serves as a hands-on practice example for that objective, without any intention of productive usage.
# Training and evaluation data
The dataset is constructed from the Chinese translation of Spanish Civil Code, Spanish Constitution, and many other laws & regulations found in the database China Law Info (北大法宝 Beida Fabao), along with their source text found on Boletín Oficial del Estado and EUR-Lex.
There are 9972 sentence pairs constructed. 1000 are used for evaluation and the rest for training.
# Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 10
- mixed_precision_training: Native AMP
- weight_decay: 0.01
- early_stopping_patience: 8
# Training results
Best validation loss achieved at step 5600.
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.9584 | 0.36 | 400 | 2.6800 |
| 2.6402 | 0.71 | 800 | 2.5017 |
| 2.5038 | 1.07 | 1200 | 2.3907 |
| 2.3279 | 1.43 | 1600 | 2.2999 |
| 2.2258 | 1.78 | 2000 | 2.2343 |
| 2.1061 | 2.14 | 2400 | 2.1961 |
| 1.9279 | 2.5 | 2800 | 2.1569 |
| 1.9059 | 2.85 | 3200 | 2.1245 |
| 1.7491 | 3.21 | 3600 | 2.1227 |
| 1.6301 | 3.57 | 4000 | 2.1169 |
| 1.6871 | 3.92 | 4400 | 2.0979 |
| 1.5203 | 4.28 | 4800 | 2.1074 |
| 1.4646 | 4.63 | 5200 | 2.1024 |
| 1.4739 | 4.99 | 5600 | 2.0905 |
| 1.338 | 5.35 | 6000 | 2.0946 |
| 1.3152 | 5.7 | 6400 | 2.0974 |
| 1.306 | 6.06 | 6800 | 2.0985 |
| 1.1991 | 6.42 | 7200 | 2.0962 |
| 1.2113 | 6.77 | 7600 | 2.1092 |
| 1.1983 | 7.13 | 8000 | 2.1060 |
| 1.1238 | 7.49 | 8400 | 2.1102 |
| 1.1417 | 7.84 | 8800 | 2.1078 |
# Framework versions
- Transformers 4.7.0
- Pytorch 1.8.1+cu101
- Datasets 1.8.0
- Tokenizers 0.10.3
|
{"language": ["es", "zh"], "license": "apache-2.0", "tags": ["translation"]}
|
translation
|
guocheng98/HelsinkiNLP-FineTuned-Legal-es-zh
|
[
"transformers",
"pytorch",
"marian",
"text2text-generation",
"translation",
"es",
"zh",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"es",
"zh"
] |
TAGS
#transformers #pytorch #marian #text2text-generation #translation #es #zh #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
This model is a fine-tuned version of Helsinki-NLP/opus-tatoeba-es-zh on a dataset of legal domain constructed by the author himself.
Intended uses & limitations
===========================
This model is the result of the master graduation thesis for the Tradumatics: Translation Technologies program at the Autonomous University of Barcelona.
Please refer to the GitHub repo created for this thesis for the full-text and relative open-sourced materials: URL
The thesis intends to explain various theories and certain algorithm details about neural machine translation, thus this fine-tuned model only serves as a hands-on practice example for that objective, without any intention of productive usage.
Training and evaluation data
============================
The dataset is constructed from the Chinese translation of Spanish Civil Code, Spanish Constitution, and many other laws & regulations found in the database China Law Info (北大法宝 Beida Fabao), along with their source text found on Boletín Oficial del Estado and EUR-Lex.
There are 9972 sentence pairs constructed. 1000 are used for evaluation and the rest for training.
Training hyperparameters
========================
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 2000
* num\_epochs: 10
* mixed\_precision\_training: Native AMP
* weight\_decay: 0.01
* early\_stopping\_patience: 8
Training results
================
Best validation loss achieved at step 5600.
Framework versions
==================
* Transformers 4.7.0
* Pytorch 1.8.1+cu101
* Datasets 1.8.0
* Tokenizers 0.10.3
|
[] |
[
"TAGS\n#transformers #pytorch #marian #text2text-generation #translation #es #zh #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
[
54
] |
[
"passage: TAGS\n#transformers #pytorch #marian #text2text-generation #translation #es #zh #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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] |
null | null | null |
# WudaoSailing
WudaoSailing is a package for pretraining chinese Language Model and finetune tasks. Now it supports GLM, Bert, T5, Cogview and Roberta models.
## Get Started
### Docker Image
We prepare two docker images based on CUDA 10.2 and CUDA 11.2. You can build images from the docker file [docs/docker/cuda102.dockerfile](docs/docker/cuda102.dcokerfile) or pull the pre-built images from Docker Hub and run with docker v19.03+
```shell
nvidia-docker run -id --hostname=V100 --network=host\
--ipc=host --shm-size=16gb --name=deepspeed-cuda \
-e NVIDIA_VISIBLE_DEVICES=0,1,2,3 \
-v /DATA/disk1/docker/containers/:/data deepspeed/cuda102:lastest
```
or replace `cuda102` with `cuda112`.
```shell
docker build -f cuda102.dockerfile -t deepspeed/cuda102 .
```
### Clone this repo
```shell
git clone https://github.com/wangguojim/WudaoSailing.git
cd WudaoSailing
pip install -r requirements.txt
```
## GLM
We show some examples based on GLM model.
### finetuene
We provide scripts for finetuning GLM on some downstream tasks.
#### SuperGLUE
- Download the [SuperGlue](https://super.gluebenchmark.com/tasks) data and check the experiment setup in
[examples/glm/scripts/ds_finetune_superglue.sh](xamples/glm/scripts/ds_finetune_superglue.sh). Note that `DATA_ROOT, CHECKPOINT_PATH, SAVE_PATH`
need to be changed to your local path. You may also change the `batch-size` and `nproc_per_node` according to your
available hardware.
- Run the following script for text similarity finetune task (use the afqmc dataset as an example)
```
cd examples/glm/
bash scripts/ds_finetune_superglue.sh\
config/model_blocklm_large_chinese.sh\
config_tasks/task_afqmc.sh
```
- Run the following script for text classification finetune task (use the thunews and thunews dataset as an example)
```
cd examples/glm/
bash scripts/ds_finetune_superglue.sh\
config/model_blocklm_large_chinese.sh\
config_tasks/task_tnews.sh
```
- Run the following script for causal inference finetune task (use the COPA dataset as an example)
```
cd examples/glm/
bash scripts/ds_finetune_superglue.sh\
config/model_blocklm_large_chinese.sh\
config_tasks/task_copa.sh
```
- To apply GLM to a new NLU dataset with cloze-filling finetuning, implement a `DataProcessor` in
[examples/glm/tasks/superglue/dataset.py](examples/glm/tasks/superglue/dataset.py) for data loading and add a `PVP` in
[examples/glm/tasks/superglue/pvp.py](examples/glm/tasks/superglue/pvp.py) for the cloze question. More details can be found
[here](examples/glm/tasks/superglue/README.md).
#### Blank Filling (Interactive)
* Change `CHECKPOINT_PATH` to your local path. Run the following script
```
bash config/generate_block.sh\
config/model_blocklm_large_chinese.sh
```
##### Example1 (Entity Prediction):
Context: 凯旋门位于意大利米兰市古城堡旁。1807年为纪念[MASK]而建，门高25米，顶上矗立两武士青铜古兵车铸像。
GLM:拿破仑军队攻克米兰城
##### Example2 (Sentence Prediction)
Context: 工业互联网（Industrial Internet）是新一代信息通信技术与工业经济深度融合的新型基础设施、应用模式和工业生态，通过对人、机、物、系统等的全面连接，构建起覆盖全产业链、全价值链的全新制造和服务体系，为工业乃至产业数字化、网络化、智能化发展提供了实现途径，是第四次工业革命的重要基石。[sMASK]它以网络为基础、平台为中枢、数据为要素、安全为保障，既是工业数字化、网络化、智能化转型的基础设施，也是互联网、大数据、人工智能与实体经济深度融合的应用模式，同时也是一种新业态、新产业，将重塑企业形态、供应链和产业链。当前，工业互联网融合应用向国民经济重点行业广泛拓展，形成平台化设计、智能化制造、网络化协同、个性化定制、服务化延伸、数字化管理六大新模式，赋能、赋智、赋值作用不断显现，有力的促进了实体经济提质、增效、降本、绿色、安全发展。
GLM: 工业互联网是制造业技术、管理、模式的重大变革,是推动互联网、大数据、人工智能和实体经济深度融合的重要载体,是建设制造强国和网络强国的重要基础。
##### Example3 (Long Text Generation)
Context: 问题:高斯所在的国家有什么汽车品牌?答案:[gMASK]
GLM:答案:[gMASK]<|startofpiece|>德国奔驰、德国大众、别克、沃尔沃、斯柯达、本田、雪铁龙.
### Ptuning
Run the following script to integrate p-tuning with GLM:
```shell
cd algutils/ptuning/
bash finetune_zy.sh
```
### Pretrain
Run the following script to pre-train the GLM-Large model
```shell
cd examples/glm/
bash scripts/ds_pretrain_nvidia.sh config/ds_block_large.sh
```
The script [examples/glm/config/ds_pretrain_nvidia.sh](examples/glm/config/ds_pretrain_nvidia.sh) launches the training program with DeepSpeed. You should change `NUM_WORKERS` and `NUM_GPUS_PER_WORKER` to the number of workers and the number of gpus per worker. Also change `HOST_FILE_PATH` to the path to an OpenMPI-style hostfile. More details about DeepSpeed launcher can be found [here](https://www.deepspeed.ai/getting-started/#resource-configuration-multi-node).
The file [examples/glm/config/ds_block_large.sh](examples/glm/config/ds_block_large.sh) defines the hyperparameters for pretraining. Most of the arguments are fairly self-explanatory. Specifically, `--train-data` can be multiple keywords defined in `NAMED_CORPORA` in [data_utils/corpora.py](data_utils/corpora.py). The hyperparameters of the optimizer are defined in the corresponding json file under `config`. The semantics of the json file can be found [here](https://www.deepspeed.ai/docs/config-json).
## Bert
We show some examples based on GLM model.
### Pretrain
Run the following script to pre-train the Bert model
```shell
cd examples/bert/
python quick_start.py
```
## CogView
### Pretrain
Run the following script to pre-train the cogview model
```shell
cd examples/cogview/
bash config/pretrain_multiple_nodes.sh
```
### inference
Run the following script to test the ability of text2image
```shell
cd examples/cogview/
bash config/text2image_cogview.sh
```
|
{}
| null |
guoqiang/WuDaoSailing
|
[
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#region-us
|
# WudaoSailing
WudaoSailing is a package for pretraining chinese Language Model and finetune tasks. Now it supports GLM, Bert, T5, Cogview and Roberta models.
## Get Started
### Docker Image
We prepare two docker images based on CUDA 10.2 and CUDA 11.2. You can build images from the docker file docs/docker/cuda102.dockerfile or pull the pre-built images from Docker Hub and run with docker v19.03+
or replace 'cuda102' with 'cuda112'.
### Clone this repo
## GLM
We show some examples based on GLM model.
### finetuene
We provide scripts for finetuning GLM on some downstream tasks.
#### SuperGLUE
- Download the SuperGlue data and check the experiment setup in
examples/glm/scripts/ds_finetune_superglue.sh. Note that 'DATA_ROOT, CHECKPOINT_PATH, SAVE_PATH'
need to be changed to your local path. You may also change the 'batch-size' and 'nproc_per_node' according to your
available hardware.
- Run the following script for text similarity finetune task (use the afqmc dataset as an example)
- Run the following script for text classification finetune task (use the thunews and thunews dataset as an example)
- Run the following script for causal inference finetune task (use the COPA dataset as an example)
- To apply GLM to a new NLU dataset with cloze-filling finetuning, implement a 'DataProcessor' in
examples/glm/tasks/superglue/URL for data loading and add a 'PVP' in
examples/glm/tasks/superglue/URL for the cloze question. More details can be found
here.
#### Blank Filling (Interactive)
* Change 'CHECKPOINT_PATH' to your local path. Run the following script
##### Example1 (Entity Prediction):
Context: 凯旋门位于意大利米兰市古城堡旁。1807年为纪念[MASK]而建，门高25米，顶上矗立两武士青铜古兵车铸像。
GLM:拿破仑军队攻克米兰城
##### Example2 (Sentence Prediction)
Context: 工业互联网（Industrial Internet）是新一代信息通信技术与工业经济深度融合的新型基础设施、应用模式和工业生态，通过对人、机、物、系统等的全面连接，构建起覆盖全产业链、全价值链的全新制造和服务体系，为工业乃至产业数字化、网络化、智能化发展提供了实现途径，是第四次工业革命的重要基石。[sMASK]它以网络为基础、平台为中枢、数据为要素、安全为保障，既是工业数字化、网络化、智能化转型的基础设施，也是互联网、大数据、人工智能与实体经济深度融合的应用模式，同时也是一种新业态、新产业，将重塑企业形态、供应链和产业链。当前，工业互联网融合应用向国民经济重点行业广泛拓展，形成平台化设计、智能化制造、网络化协同、个性化定制、服务化延伸、数字化管理六大新模式，赋能、赋智、赋值作用不断显现，有力的促进了实体经济提质、增效、降本、绿色、安全发展。
GLM: 工业互联网是制造业技术、管理、模式的重大变革,是推动互联网、大数据、人工智能和实体经济深度融合的重要载体,是建设制造强国和网络强国的重要基础。
##### Example3 (Long Text Generation)
Context: 问题:高斯所在的国家有什么汽车品牌?答案:[gMASK]
GLM:答案:[gMASK]<|startofpiece|>德国奔驰、德国大众、别克、沃尔沃、斯柯达、本田、雪铁龙.
### Ptuning
Run the following script to integrate p-tuning with GLM:
### Pretrain
Run the following script to pre-train the GLM-Large model
The script examples/glm/config/ds_pretrain_nvidia.sh launches the training program with DeepSpeed. You should change 'NUM_WORKERS' and 'NUM_GPUS_PER_WORKER' to the number of workers and the number of gpus per worker. Also change 'HOST_FILE_PATH' to the path to an OpenMPI-style hostfile. More details about DeepSpeed launcher can be found here.
The file examples/glm/config/ds_block_large.sh defines the hyperparameters for pretraining. Most of the arguments are fairly self-explanatory. Specifically, '--train-data' can be multiple keywords defined in 'NAMED_CORPORA' in data_utils/URL. The hyperparameters of the optimizer are defined in the corresponding json file under 'config'. The semantics of the json file can be found here.
## Bert
We show some examples based on GLM model.
### Pretrain
Run the following script to pre-train the Bert model
## CogView
### Pretrain
Run the following script to pre-train the cogview model
### inference
Run the following script to test the ability of text2image
|
[
"# WudaoSailing\n\nWudaoSailing is a package for pretraining chinese Language Model and finetune tasks. Now it supports GLM, Bert, T5, Cogview and Roberta models.",
"## Get Started",
"### Docker Image\nWe prepare two docker images based on CUDA 10.2 and CUDA 11.2. You can build images from the docker file docs/docker/cuda102.dockerfile or pull the pre-built images from Docker Hub and run with docker v19.03+\n \n or replace 'cuda102' with 'cuda112'.",
"### Clone this repo",
"## GLM\n\nWe show some examples based on GLM model.",
"### finetuene\nWe provide scripts for finetuning GLM on some downstream tasks.",
"#### SuperGLUE\n\n- Download the SuperGlue data and check the experiment setup in \n examples/glm/scripts/ds_finetune_superglue.sh. Note that 'DATA_ROOT, CHECKPOINT_PATH, SAVE_PATH' \n need to be changed to your local path. You may also change the 'batch-size' and 'nproc_per_node' according to your \n available hardware.\n\n- Run the following script for text similarity finetune task (use the afqmc dataset as an example)\n\n\n\n\n- Run the following script for text classification finetune task (use the thunews and thunews dataset as an example)\n\n\n\n- Run the following script for causal inference finetune task (use the COPA dataset as an example)\n\n\n \n- To apply GLM to a new NLU dataset with cloze-filling finetuning, implement a 'DataProcessor' in\n examples/glm/tasks/superglue/URL for data loading and add a 'PVP' in \n examples/glm/tasks/superglue/URL for the cloze question. More details can be found \n here.",
"#### Blank Filling (Interactive)\n* Change 'CHECKPOINT_PATH' to your local path. Run the following script",
"##### Example1 (Entity Prediction):\n\nContext: 凯旋门位于意大利米兰市古城堡旁。1807年为纪念[MASK]而建，门高25米，顶上矗立两武士青铜古兵车铸像。\n\nGLM:拿破仑军队攻克米兰城",
"##### Example2 (Sentence Prediction)\nContext: 工业互联网（Industrial Internet）是新一代信息通信技术与工业经济深度融合的新型基础设施、应用模式和工业生态，通过对人、机、物、系统等的全面连接，构建起覆盖全产业链、全价值链的全新制造和服务体系，为工业乃至产业数字化、网络化、智能化发展提供了实现途径，是第四次工业革命的重要基石。[sMASK]它以网络为基础、平台为中枢、数据为要素、安全为保障，既是工业数字化、网络化、智能化转型的基础设施，也是互联网、大数据、人工智能与实体经济深度融合的应用模式，同时也是一种新业态、新产业，将重塑企业形态、供应链和产业链。当前，工业互联网融合应用向国民经济重点行业广泛拓展，形成平台化设计、智能化制造、网络化协同、个性化定制、服务化延伸、数字化管理六大新模式，赋能、赋智、赋值作用不断显现，有力的促进了实体经济提质、增效、降本、绿色、安全发展。\n\nGLM: 工业互联网是制造业技术、管理、模式的重大变革,是推动互联网、大数据、人工智能和实体经济深度融合的重要载体,是建设制造强国和网络强国的重要基础。",
"##### Example3 (Long Text Generation)\nContext: 问题:高斯所在的国家有什么汽车品牌?答案:[gMASK]\n\nGLM:答案:[gMASK]<|startofpiece|>德国奔驰、德国大众、别克、沃尔沃、斯柯达、本田、雪铁龙.",
"### Ptuning\nRun the following script to integrate p-tuning with GLM:",
"### Pretrain\nRun the following script to pre-train the GLM-Large model\n\n\nThe script examples/glm/config/ds_pretrain_nvidia.sh launches the training program with DeepSpeed. You should change 'NUM_WORKERS' and 'NUM_GPUS_PER_WORKER' to the number of workers and the number of gpus per worker. Also change 'HOST_FILE_PATH' to the path to an OpenMPI-style hostfile. More details about DeepSpeed launcher can be found here.\n\nThe file examples/glm/config/ds_block_large.sh defines the hyperparameters for pretraining. Most of the arguments are fairly self-explanatory. Specifically, '--train-data' can be multiple keywords defined in 'NAMED_CORPORA' in data_utils/URL. The hyperparameters of the optimizer are defined in the corresponding json file under 'config'. The semantics of the json file can be found here.",
"## Bert\n\nWe show some examples based on GLM model.",
"### Pretrain\nRun the following script to pre-train the Bert model",
"## CogView",
"### Pretrain\nRun the following script to pre-train the cogview model",
"### inference\nRun the following script to test the ability of text2image"
] |
[
"TAGS\n#region-us \n",
"# WudaoSailing\n\nWudaoSailing is a package for pretraining chinese Language Model and finetune tasks. Now it supports GLM, Bert, T5, Cogview and Roberta models.",
"## Get Started",
"### Docker Image\nWe prepare two docker images based on CUDA 10.2 and CUDA 11.2. You can build images from the docker file docs/docker/cuda102.dockerfile or pull the pre-built images from Docker Hub and run with docker v19.03+\n \n or replace 'cuda102' with 'cuda112'.",
"### Clone this repo",
"## GLM\n\nWe show some examples based on GLM model.",
"### finetuene\nWe provide scripts for finetuning GLM on some downstream tasks.",
"#### SuperGLUE\n\n- Download the SuperGlue data and check the experiment setup in \n examples/glm/scripts/ds_finetune_superglue.sh. Note that 'DATA_ROOT, CHECKPOINT_PATH, SAVE_PATH' \n need to be changed to your local path. You may also change the 'batch-size' and 'nproc_per_node' according to your \n available hardware.\n\n- Run the following script for text similarity finetune task (use the afqmc dataset as an example)\n\n\n\n\n- Run the following script for text classification finetune task (use the thunews and thunews dataset as an example)\n\n\n\n- Run the following script for causal inference finetune task (use the COPA dataset as an example)\n\n\n \n- To apply GLM to a new NLU dataset with cloze-filling finetuning, implement a 'DataProcessor' in\n examples/glm/tasks/superglue/URL for data loading and add a 'PVP' in \n examples/glm/tasks/superglue/URL for the cloze question. More details can be found \n here.",
"#### Blank Filling (Interactive)\n* Change 'CHECKPOINT_PATH' to your local path. Run the following script",
"##### Example1 (Entity Prediction):\n\nContext: 凯旋门位于意大利米兰市古城堡旁。1807年为纪念[MASK]而建，门高25米，顶上矗立两武士青铜古兵车铸像。\n\nGLM:拿破仑军队攻克米兰城",
"##### Example2 (Sentence Prediction)\nContext: 工业互联网（Industrial Internet）是新一代信息通信技术与工业经济深度融合的新型基础设施、应用模式和工业生态，通过对人、机、物、系统等的全面连接，构建起覆盖全产业链、全价值链的全新制造和服务体系，为工业乃至产业数字化、网络化、智能化发展提供了实现途径，是第四次工业革命的重要基石。[sMASK]它以网络为基础、平台为中枢、数据为要素、安全为保障，既是工业数字化、网络化、智能化转型的基础设施，也是互联网、大数据、人工智能与实体经济深度融合的应用模式，同时也是一种新业态、新产业，将重塑企业形态、供应链和产业链。当前，工业互联网融合应用向国民经济重点行业广泛拓展，形成平台化设计、智能化制造、网络化协同、个性化定制、服务化延伸、数字化管理六大新模式，赋能、赋智、赋值作用不断显现，有力的促进了实体经济提质、增效、降本、绿色、安全发展。\n\nGLM: 工业互联网是制造业技术、管理、模式的重大变革,是推动互联网、大数据、人工智能和实体经济深度融合的重要载体,是建设制造强国和网络强国的重要基础。",
"##### Example3 (Long Text Generation)\nContext: 问题:高斯所在的国家有什么汽车品牌?答案:[gMASK]\n\nGLM:答案:[gMASK]<|startofpiece|>德国奔驰、德国大众、别克、沃尔沃、斯柯达、本田、雪铁龙.",
"### Ptuning\nRun the following script to integrate p-tuning with GLM:",
"### Pretrain\nRun the following script to pre-train the GLM-Large model\n\n\nThe script examples/glm/config/ds_pretrain_nvidia.sh launches the training program with DeepSpeed. You should change 'NUM_WORKERS' and 'NUM_GPUS_PER_WORKER' to the number of workers and the number of gpus per worker. Also change 'HOST_FILE_PATH' to the path to an OpenMPI-style hostfile. More details about DeepSpeed launcher can be found here.\n\nThe file examples/glm/config/ds_block_large.sh defines the hyperparameters for pretraining. Most of the arguments are fairly self-explanatory. Specifically, '--train-data' can be multiple keywords defined in 'NAMED_CORPORA' in data_utils/URL. The hyperparameters of the optimizer are defined in the corresponding json file under 'config'. The semantics of the json file can be found here.",
"## Bert\n\nWe show some examples based on GLM model.",
"### Pretrain\nRun the following script to pre-train the Bert model",
"## CogView",
"### Pretrain\nRun the following script to pre-train the cogview model",
"### inference\nRun the following script to test the ability of text2image"
] |
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] |
[
"passage: TAGS\n#region-us \n# WudaoSailing\n\nWudaoSailing is a package for pretraining chinese Language Model and finetune tasks. Now it supports GLM, Bert, T5, Cogview and Roberta models.## Get Started### Docker Image\nWe prepare two docker images based on CUDA 10.2 and CUDA 11.2. You can build images from the docker file docs/docker/cuda102.dockerfile or pull the pre-built images from Docker Hub and run with docker v19.03+\n \n or replace 'cuda102' with 'cuda112'.### Clone this repo## GLM\n\nWe show some examples based on GLM model.### finetuene\nWe provide scripts for finetuning GLM on some downstream tasks.#### SuperGLUE\n\n- Download the SuperGlue data and check the experiment setup in \n examples/glm/scripts/ds_finetune_superglue.sh. Note that 'DATA_ROOT, CHECKPOINT_PATH, SAVE_PATH' \n need to be changed to your local path. You may also change the 'batch-size' and 'nproc_per_node' according to your \n available hardware.\n\n- Run the following script for text similarity finetune task (use the afqmc dataset as an example)\n\n\n\n\n- Run the following script for text classification finetune task (use the thunews and thunews dataset as an example)\n\n\n\n- Run the following script for causal inference finetune task (use the COPA dataset as an example)\n\n\n \n- To apply GLM to a new NLU dataset with cloze-filling finetuning, implement a 'DataProcessor' in\n examples/glm/tasks/superglue/URL for data loading and add a 'PVP' in \n examples/glm/tasks/superglue/URL for the cloze question. More details can be found \n here.#### Blank Filling (Interactive)\n* Change 'CHECKPOINT_PATH' to your local path. Run the following script",
"passage: ##### Example1 (Entity Prediction):\n\nContext: 凯旋门位于意大利米兰市古城堡旁。1807年为纪念[MASK]而建，门高25米，顶上矗立两武士青铜古兵车铸像。\n\nGLM:拿破仑军队攻克米兰城##### Example2 (Sentence Prediction)\nContext: 工业互联网（Industrial Internet）是新一代信息通信技术与工业经济深度融合的新型基础设施、应用模式和工业生态，通过对人、机、物、系统等的全面连接，构建起覆盖全产业链、全价值链的全新制造和服务体系，为工业乃至产业数字化、网络化、智能化发展提供了实现途径，是第四次工业革命的重要基石。[sMASK]它以网络为基础、平台为中枢、数据为要素、安全为保障，既是工业数字化、网络化、智能化转型的基础设施，也是互联网、大数据、人工智能与实体经济深度融合的应用模式，同时也是一种新业态、新产业，将重塑企业形态、供应链和产业链。当前，工业互联网融合应用向国民经济重点行业广泛拓展，形成平台化设计、智能化制造、网络化协同、个性化定制、服务化延伸、数字化管理六大新模式，赋能、赋智、赋值作用不断显现，有力的促进了实体经济提质、增效、降本、绿色、安全发展。\n\nGLM: 工业互联网是制造业技术、管理、模式的重大变革,是推动互联网、大数据、人工智能和实体经济深度融合的重要载体,是建设制造强国和网络强国的重要基础。##### Example3 (Long Text Generation)\nContext: 问题:高斯所在的国家有什么汽车品牌?答案:[gMASK]\n\nGLM:答案:[gMASK]<|startofpiece|>德国奔驰、德国大众、别克、沃尔沃、斯柯达、本田、雪铁龙.### Ptuning\nRun the following script to integrate p-tuning with GLM:"
] |
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null | null | null |
# WudaoSailing
WudaoSailing is a package for pretraining chinese Language Model and finetune tasks. Now it supports GLM, Bert, T5, Cogview and Roberta models.
## Get Started
### Docker Image
We prepare two docker images based on CUDA 10.2 and CUDA 11.2. You can build images from the docker file [docs/docker/cuda102.dockerfile](docs/docker/cuda102.dcokerfile) or pull the pre-built images from Docker Hub and run with docker v19.03+
```shell
nvidia-docker run -id --hostname=V100 --network=host\
--ipc=host --shm-size=16gb --name=deepspeed-cuda \
-e NVIDIA_VISIBLE_DEVICES=0,1,2,3 \
-v /DATA/disk1/docker/containers/:/data deepspeed/cuda102:lastest
```
or replace `cuda102` with `cuda112`.
```shell
docker build -f cuda102.dockerfile -t deepspeed/cuda102 .
```
### Clone this repo
```shell
git clone https://github.com/wangguojim/WudaoSailing.git
cd WudaoSailing
pip install -r requirements.txt
```
## GLM
We show some examples based on GLM model.
### finetuene
We provide scripts for finetuning GLM on some downstream tasks.
#### SuperGLUE
- Download the [SuperGlue](https://super.gluebenchmark.com/tasks) data and check the experiment setup in
[examples/glm/scripts/ds_finetune_superglue.sh](xamples/glm/scripts/ds_finetune_superglue.sh). Note that `DATA_ROOT, CHECKPOINT_PATH, SAVE_PATH`
need to be changed to your local path. You may also change the `batch-size` and `nproc_per_node` according to your
available hardware.
- Run the following script for text similarity finetune task (use the afqmc dataset as an example)
```
cd examples/glm/
bash scripts/ds_finetune_superglue.sh\
config/model_blocklm_large_chinese.sh\
config_tasks/task_afqmc.sh
```
- Run the following script for text classification finetune task (use the thunews and thunews dataset as an example)
```
cd examples/glm/
bash scripts/ds_finetune_superglue.sh\
config/model_blocklm_large_chinese.sh\
config_tasks/task_tnews.sh
```
- Run the following script for causal inference finetune task (use the COPA dataset as an example)
```
cd examples/glm/
bash scripts/ds_finetune_superglue.sh\
config/model_blocklm_large_chinese.sh\
config_tasks/task_copa.sh
```
- To apply GLM to a new NLU dataset with cloze-filling finetuning, implement a `DataProcessor` in
[examples/glm/tasks/superglue/dataset.py](examples/glm/tasks/superglue/dataset.py) for data loading and add a `PVP` in
[examples/glm/tasks/superglue/pvp.py](examples/glm/tasks/superglue/pvp.py) for the cloze question. More details can be found
[here](examples/glm/tasks/superglue/README.md).
#### Blank Filling (Interactive)
* Change `CHECKPOINT_PATH` to your local path. Run the following script
```
bash config/generate_block.sh\
config/model_blocklm_large_chinese.sh
```
##### Example1 (Entity Prediction):
Context: 凯旋门位于意大利米兰市古城堡旁。1807年为纪念[MASK]而建，门高25米，顶上矗立两武士青铜古兵车铸像。
GLM:拿破仑军队攻克米兰城
##### Example2 (Sentence Prediction)
Context: 工业互联网（Industrial Internet）是新一代信息通信技术与工业经济深度融合的新型基础设施、应用模式和工业生态，通过对人、机、物、系统等的全面连接，构建起覆盖全产业链、全价值链的全新制造和服务体系，为工业乃至产业数字化、网络化、智能化发展提供了实现途径，是第四次工业革命的重要基石。[sMASK]它以网络为基础、平台为中枢、数据为要素、安全为保障，既是工业数字化、网络化、智能化转型的基础设施，也是互联网、大数据、人工智能与实体经济深度融合的应用模式，同时也是一种新业态、新产业，将重塑企业形态、供应链和产业链。当前，工业互联网融合应用向国民经济重点行业广泛拓展，形成平台化设计、智能化制造、网络化协同、个性化定制、服务化延伸、数字化管理六大新模式，赋能、赋智、赋值作用不断显现，有力的促进了实体经济提质、增效、降本、绿色、安全发展。
GLM: 工业互联网是制造业技术、管理、模式的重大变革,是推动互联网、大数据、人工智能和实体经济深度融合的重要载体,是建设制造强国和网络强国的重要基础。
##### Example3 (Long Text Generation)
Context: 问题:高斯所在的国家有什么汽车品牌?答案:[gMASK]
GLM:答案:[gMASK]<|startofpiece|>德国奔驰、德国大众、别克、沃尔沃、斯柯达、本田、雪铁龙.
### Ptuning
Run the following script to integrate p-tuning with GLM:
```shell
cd algutils/ptuning/
bash finetune_zy.sh
```
### Pretrain
Run the following script to pre-train the GLM-Large model
```shell
cd examples/glm/
bash scripts/ds_pretrain_nvidia.sh config/ds_block_large.sh
```
The script [examples/glm/config/ds_pretrain_nvidia.sh](examples/glm/config/ds_pretrain_nvidia.sh) launches the training program with DeepSpeed. You should change `NUM_WORKERS` and `NUM_GPUS_PER_WORKER` to the number of workers and the number of gpus per worker. Also change `HOST_FILE_PATH` to the path to an OpenMPI-style hostfile. More details about DeepSpeed launcher can be found [here](https://www.deepspeed.ai/getting-started/#resource-configuration-multi-node).
The file [examples/glm/config/ds_block_large.sh](examples/glm/config/ds_block_large.sh) defines the hyperparameters for pretraining. Most of the arguments are fairly self-explanatory. Specifically, `--train-data` can be multiple keywords defined in `NAMED_CORPORA` in [data_utils/corpora.py](data_utils/corpora.py). The hyperparameters of the optimizer are defined in the corresponding json file under `config`. The semantics of the json file can be found [here](https://www.deepspeed.ai/docs/config-json).
## Bert
We show some examples based on GLM model.
### Pretrain
Run the following script to pre-train the Bert model
```shell
cd examples/bert/
python quick_start.py
```
## CogView
### Pretrain
Run the following script to pre-train the cogview model
```shell
cd examples/cogview/
bash config/pretrain_multiple_nodes.sh
```
### inference
Run the following script to test the ability of text2image
```shell
cd examples/cogview/
bash config/text2image_cogview.sh
```
|
{}
| null |
guoqiang/glm
|
[
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#region-us
|
# WudaoSailing
WudaoSailing is a package for pretraining chinese Language Model and finetune tasks. Now it supports GLM, Bert, T5, Cogview and Roberta models.
## Get Started
### Docker Image
We prepare two docker images based on CUDA 10.2 and CUDA 11.2. You can build images from the docker file docs/docker/cuda102.dockerfile or pull the pre-built images from Docker Hub and run with docker v19.03+
or replace 'cuda102' with 'cuda112'.
### Clone this repo
## GLM
We show some examples based on GLM model.
### finetuene
We provide scripts for finetuning GLM on some downstream tasks.
#### SuperGLUE
- Download the SuperGlue data and check the experiment setup in
examples/glm/scripts/ds_finetune_superglue.sh. Note that 'DATA_ROOT, CHECKPOINT_PATH, SAVE_PATH'
need to be changed to your local path. You may also change the 'batch-size' and 'nproc_per_node' according to your
available hardware.
- Run the following script for text similarity finetune task (use the afqmc dataset as an example)
- Run the following script for text classification finetune task (use the thunews and thunews dataset as an example)
- Run the following script for causal inference finetune task (use the COPA dataset as an example)
- To apply GLM to a new NLU dataset with cloze-filling finetuning, implement a 'DataProcessor' in
examples/glm/tasks/superglue/URL for data loading and add a 'PVP' in
examples/glm/tasks/superglue/URL for the cloze question. More details can be found
here.
#### Blank Filling (Interactive)
* Change 'CHECKPOINT_PATH' to your local path. Run the following script
##### Example1 (Entity Prediction):
Context: 凯旋门位于意大利米兰市古城堡旁。1807年为纪念[MASK]而建，门高25米，顶上矗立两武士青铜古兵车铸像。
GLM:拿破仑军队攻克米兰城
##### Example2 (Sentence Prediction)
Context: 工业互联网（Industrial Internet）是新一代信息通信技术与工业经济深度融合的新型基础设施、应用模式和工业生态，通过对人、机、物、系统等的全面连接，构建起覆盖全产业链、全价值链的全新制造和服务体系，为工业乃至产业数字化、网络化、智能化发展提供了实现途径，是第四次工业革命的重要基石。[sMASK]它以网络为基础、平台为中枢、数据为要素、安全为保障，既是工业数字化、网络化、智能化转型的基础设施，也是互联网、大数据、人工智能与实体经济深度融合的应用模式，同时也是一种新业态、新产业，将重塑企业形态、供应链和产业链。当前，工业互联网融合应用向国民经济重点行业广泛拓展，形成平台化设计、智能化制造、网络化协同、个性化定制、服务化延伸、数字化管理六大新模式，赋能、赋智、赋值作用不断显现，有力的促进了实体经济提质、增效、降本、绿色、安全发展。
GLM: 工业互联网是制造业技术、管理、模式的重大变革,是推动互联网、大数据、人工智能和实体经济深度融合的重要载体,是建设制造强国和网络强国的重要基础。
##### Example3 (Long Text Generation)
Context: 问题:高斯所在的国家有什么汽车品牌?答案:[gMASK]
GLM:答案:[gMASK]<|startofpiece|>德国奔驰、德国大众、别克、沃尔沃、斯柯达、本田、雪铁龙.
### Ptuning
Run the following script to integrate p-tuning with GLM:
### Pretrain
Run the following script to pre-train the GLM-Large model
The script examples/glm/config/ds_pretrain_nvidia.sh launches the training program with DeepSpeed. You should change 'NUM_WORKERS' and 'NUM_GPUS_PER_WORKER' to the number of workers and the number of gpus per worker. Also change 'HOST_FILE_PATH' to the path to an OpenMPI-style hostfile. More details about DeepSpeed launcher can be found here.
The file examples/glm/config/ds_block_large.sh defines the hyperparameters for pretraining. Most of the arguments are fairly self-explanatory. Specifically, '--train-data' can be multiple keywords defined in 'NAMED_CORPORA' in data_utils/URL. The hyperparameters of the optimizer are defined in the corresponding json file under 'config'. The semantics of the json file can be found here.
## Bert
We show some examples based on GLM model.
### Pretrain
Run the following script to pre-train the Bert model
## CogView
### Pretrain
Run the following script to pre-train the cogview model
### inference
Run the following script to test the ability of text2image
|
[
"# WudaoSailing\n\nWudaoSailing is a package for pretraining chinese Language Model and finetune tasks. Now it supports GLM, Bert, T5, Cogview and Roberta models.",
"## Get Started",
"### Docker Image\nWe prepare two docker images based on CUDA 10.2 and CUDA 11.2. You can build images from the docker file docs/docker/cuda102.dockerfile or pull the pre-built images from Docker Hub and run with docker v19.03+\n \n or replace 'cuda102' with 'cuda112'.",
"### Clone this repo",
"## GLM\n\nWe show some examples based on GLM model.",
"### finetuene\nWe provide scripts for finetuning GLM on some downstream tasks.",
"#### SuperGLUE\n\n- Download the SuperGlue data and check the experiment setup in \n examples/glm/scripts/ds_finetune_superglue.sh. Note that 'DATA_ROOT, CHECKPOINT_PATH, SAVE_PATH' \n need to be changed to your local path. You may also change the 'batch-size' and 'nproc_per_node' according to your \n available hardware.\n\n- Run the following script for text similarity finetune task (use the afqmc dataset as an example)\n\n\n\n\n- Run the following script for text classification finetune task (use the thunews and thunews dataset as an example)\n\n\n\n- Run the following script for causal inference finetune task (use the COPA dataset as an example)\n\n\n \n- To apply GLM to a new NLU dataset with cloze-filling finetuning, implement a 'DataProcessor' in\n examples/glm/tasks/superglue/URL for data loading and add a 'PVP' in \n examples/glm/tasks/superglue/URL for the cloze question. More details can be found \n here.",
"#### Blank Filling (Interactive)\n* Change 'CHECKPOINT_PATH' to your local path. Run the following script",
"##### Example1 (Entity Prediction):\n\nContext: 凯旋门位于意大利米兰市古城堡旁。1807年为纪念[MASK]而建，门高25米，顶上矗立两武士青铜古兵车铸像。\n\nGLM:拿破仑军队攻克米兰城",
"##### Example2 (Sentence Prediction)\nContext: 工业互联网（Industrial Internet）是新一代信息通信技术与工业经济深度融合的新型基础设施、应用模式和工业生态，通过对人、机、物、系统等的全面连接，构建起覆盖全产业链、全价值链的全新制造和服务体系，为工业乃至产业数字化、网络化、智能化发展提供了实现途径，是第四次工业革命的重要基石。[sMASK]它以网络为基础、平台为中枢、数据为要素、安全为保障，既是工业数字化、网络化、智能化转型的基础设施，也是互联网、大数据、人工智能与实体经济深度融合的应用模式，同时也是一种新业态、新产业，将重塑企业形态、供应链和产业链。当前，工业互联网融合应用向国民经济重点行业广泛拓展，形成平台化设计、智能化制造、网络化协同、个性化定制、服务化延伸、数字化管理六大新模式，赋能、赋智、赋值作用不断显现，有力的促进了实体经济提质、增效、降本、绿色、安全发展。\n\nGLM: 工业互联网是制造业技术、管理、模式的重大变革,是推动互联网、大数据、人工智能和实体经济深度融合的重要载体,是建设制造强国和网络强国的重要基础。",
"##### Example3 (Long Text Generation)\nContext: 问题:高斯所在的国家有什么汽车品牌?答案:[gMASK]\n\nGLM:答案:[gMASK]<|startofpiece|>德国奔驰、德国大众、别克、沃尔沃、斯柯达、本田、雪铁龙.",
"### Ptuning\nRun the following script to integrate p-tuning with GLM:",
"### Pretrain\nRun the following script to pre-train the GLM-Large model\n\n\nThe script examples/glm/config/ds_pretrain_nvidia.sh launches the training program with DeepSpeed. You should change 'NUM_WORKERS' and 'NUM_GPUS_PER_WORKER' to the number of workers and the number of gpus per worker. Also change 'HOST_FILE_PATH' to the path to an OpenMPI-style hostfile. More details about DeepSpeed launcher can be found here.\n\nThe file examples/glm/config/ds_block_large.sh defines the hyperparameters for pretraining. Most of the arguments are fairly self-explanatory. Specifically, '--train-data' can be multiple keywords defined in 'NAMED_CORPORA' in data_utils/URL. The hyperparameters of the optimizer are defined in the corresponding json file under 'config'. The semantics of the json file can be found here.",
"## Bert\n\nWe show some examples based on GLM model.",
"### Pretrain\nRun the following script to pre-train the Bert model",
"## CogView",
"### Pretrain\nRun the following script to pre-train the cogview model",
"### inference\nRun the following script to test the ability of text2image"
] |
[
"TAGS\n#region-us \n",
"# WudaoSailing\n\nWudaoSailing is a package for pretraining chinese Language Model and finetune tasks. Now it supports GLM, Bert, T5, Cogview and Roberta models.",
"## Get Started",
"### Docker Image\nWe prepare two docker images based on CUDA 10.2 and CUDA 11.2. You can build images from the docker file docs/docker/cuda102.dockerfile or pull the pre-built images from Docker Hub and run with docker v19.03+\n \n or replace 'cuda102' with 'cuda112'.",
"### Clone this repo",
"## GLM\n\nWe show some examples based on GLM model.",
"### finetuene\nWe provide scripts for finetuning GLM on some downstream tasks.",
"#### SuperGLUE\n\n- Download the SuperGlue data and check the experiment setup in \n examples/glm/scripts/ds_finetune_superglue.sh. Note that 'DATA_ROOT, CHECKPOINT_PATH, SAVE_PATH' \n need to be changed to your local path. You may also change the 'batch-size' and 'nproc_per_node' according to your \n available hardware.\n\n- Run the following script for text similarity finetune task (use the afqmc dataset as an example)\n\n\n\n\n- Run the following script for text classification finetune task (use the thunews and thunews dataset as an example)\n\n\n\n- Run the following script for causal inference finetune task (use the COPA dataset as an example)\n\n\n \n- To apply GLM to a new NLU dataset with cloze-filling finetuning, implement a 'DataProcessor' in\n examples/glm/tasks/superglue/URL for data loading and add a 'PVP' in \n examples/glm/tasks/superglue/URL for the cloze question. More details can be found \n here.",
"#### Blank Filling (Interactive)\n* Change 'CHECKPOINT_PATH' to your local path. Run the following script",
"##### Example1 (Entity Prediction):\n\nContext: 凯旋门位于意大利米兰市古城堡旁。1807年为纪念[MASK]而建，门高25米，顶上矗立两武士青铜古兵车铸像。\n\nGLM:拿破仑军队攻克米兰城",
"##### Example2 (Sentence Prediction)\nContext: 工业互联网（Industrial Internet）是新一代信息通信技术与工业经济深度融合的新型基础设施、应用模式和工业生态，通过对人、机、物、系统等的全面连接，构建起覆盖全产业链、全价值链的全新制造和服务体系，为工业乃至产业数字化、网络化、智能化发展提供了实现途径，是第四次工业革命的重要基石。[sMASK]它以网络为基础、平台为中枢、数据为要素、安全为保障，既是工业数字化、网络化、智能化转型的基础设施，也是互联网、大数据、人工智能与实体经济深度融合的应用模式，同时也是一种新业态、新产业，将重塑企业形态、供应链和产业链。当前，工业互联网融合应用向国民经济重点行业广泛拓展，形成平台化设计、智能化制造、网络化协同、个性化定制、服务化延伸、数字化管理六大新模式，赋能、赋智、赋值作用不断显现，有力的促进了实体经济提质、增效、降本、绿色、安全发展。\n\nGLM: 工业互联网是制造业技术、管理、模式的重大变革,是推动互联网、大数据、人工智能和实体经济深度融合的重要载体,是建设制造强国和网络强国的重要基础。",
"##### Example3 (Long Text Generation)\nContext: 问题:高斯所在的国家有什么汽车品牌?答案:[gMASK]\n\nGLM:答案:[gMASK]<|startofpiece|>德国奔驰、德国大众、别克、沃尔沃、斯柯达、本田、雪铁龙.",
"### Ptuning\nRun the following script to integrate p-tuning with GLM:",
"### Pretrain\nRun the following script to pre-train the GLM-Large model\n\n\nThe script examples/glm/config/ds_pretrain_nvidia.sh launches the training program with DeepSpeed. You should change 'NUM_WORKERS' and 'NUM_GPUS_PER_WORKER' to the number of workers and the number of gpus per worker. Also change 'HOST_FILE_PATH' to the path to an OpenMPI-style hostfile. More details about DeepSpeed launcher can be found here.\n\nThe file examples/glm/config/ds_block_large.sh defines the hyperparameters for pretraining. Most of the arguments are fairly self-explanatory. Specifically, '--train-data' can be multiple keywords defined in 'NAMED_CORPORA' in data_utils/URL. The hyperparameters of the optimizer are defined in the corresponding json file under 'config'. The semantics of the json file can be found here.",
"## Bert\n\nWe show some examples based on GLM model.",
"### Pretrain\nRun the following script to pre-train the Bert model",
"## CogView",
"### Pretrain\nRun the following script to pre-train the cogview model",
"### inference\nRun the following script to test the ability of text2image"
] |
[
6,
47,
4,
80,
6,
14,
22,
257,
29,
71,
285,
72,
20,
237,
13,
16,
4,
18,
16
] |
[
"passage: TAGS\n#region-us \n# WudaoSailing\n\nWudaoSailing is a package for pretraining chinese Language Model and finetune tasks. Now it supports GLM, Bert, T5, Cogview and Roberta models.## Get Started### Docker Image\nWe prepare two docker images based on CUDA 10.2 and CUDA 11.2. You can build images from the docker file docs/docker/cuda102.dockerfile or pull the pre-built images from Docker Hub and run with docker v19.03+\n \n or replace 'cuda102' with 'cuda112'.### Clone this repo## GLM\n\nWe show some examples based on GLM model.### finetuene\nWe provide scripts for finetuning GLM on some downstream tasks.#### SuperGLUE\n\n- Download the SuperGlue data and check the experiment setup in \n examples/glm/scripts/ds_finetune_superglue.sh. Note that 'DATA_ROOT, CHECKPOINT_PATH, SAVE_PATH' \n need to be changed to your local path. You may also change the 'batch-size' and 'nproc_per_node' according to your \n available hardware.\n\n- Run the following script for text similarity finetune task (use the afqmc dataset as an example)\n\n\n\n\n- Run the following script for text classification finetune task (use the thunews and thunews dataset as an example)\n\n\n\n- Run the following script for causal inference finetune task (use the COPA dataset as an example)\n\n\n \n- To apply GLM to a new NLU dataset with cloze-filling finetuning, implement a 'DataProcessor' in\n examples/glm/tasks/superglue/URL for data loading and add a 'PVP' in \n examples/glm/tasks/superglue/URL for the cloze question. More details can be found \n here.#### Blank Filling (Interactive)\n* Change 'CHECKPOINT_PATH' to your local path. Run the following script",
"passage: ##### Example1 (Entity Prediction):\n\nContext: 凯旋门位于意大利米兰市古城堡旁。1807年为纪念[MASK]而建，门高25米，顶上矗立两武士青铜古兵车铸像。\n\nGLM:拿破仑军队攻克米兰城##### Example2 (Sentence Prediction)\nContext: 工业互联网（Industrial Internet）是新一代信息通信技术与工业经济深度融合的新型基础设施、应用模式和工业生态，通过对人、机、物、系统等的全面连接，构建起覆盖全产业链、全价值链的全新制造和服务体系，为工业乃至产业数字化、网络化、智能化发展提供了实现途径，是第四次工业革命的重要基石。[sMASK]它以网络为基础、平台为中枢、数据为要素、安全为保障，既是工业数字化、网络化、智能化转型的基础设施，也是互联网、大数据、人工智能与实体经济深度融合的应用模式，同时也是一种新业态、新产业，将重塑企业形态、供应链和产业链。当前，工业互联网融合应用向国民经济重点行业广泛拓展，形成平台化设计、智能化制造、网络化协同、个性化定制、服务化延伸、数字化管理六大新模式，赋能、赋智、赋值作用不断显现，有力的促进了实体经济提质、增效、降本、绿色、安全发展。\n\nGLM: 工业互联网是制造业技术、管理、模式的重大变革,是推动互联网、大数据、人工智能和实体经济深度融合的重要载体,是建设制造强国和网络强国的重要基础。##### Example3 (Long Text Generation)\nContext: 问题:高斯所在的国家有什么汽车品牌?答案:[gMASK]\n\nGLM:答案:[gMASK]<|startofpiece|>德国奔驰、德国大众、别克、沃尔沃、斯柯达、本田、雪铁龙.### Ptuning\nRun the following script to integrate p-tuning with GLM:"
] |
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] |
null | null |
transformers
|
# Turkish News Text Classification
Turkish text classification model obtained by fine-tuning the Turkish bert model (dbmdz/bert-base-turkish-cased)
# Dataset
Dataset consists of 11 classes were obtained from https://www.trthaber.com/. The model was created using the most distinctive 6 classes.
Dataset can be accessed at https://github.com/gurkan08/datasets/tree/master/trt_11_category.
label_dict = {
'LABEL_0': 'ekonomi',
'LABEL_1': 'spor',
'LABEL_2': 'saglik',
'LABEL_3': 'kultur_sanat',
'LABEL_4': 'bilim_teknoloji',
'LABEL_5': 'egitim'
}
70% of the data were used for training and 30% for testing.
train f1-weighted score = %97
test f1-weighted score = %94
# Usage
from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("gurkan08/bert-turkish-text-classification")
model = AutoModelForSequenceClassification.from_pretrained("gurkan08/bert-turkish-text-classification")
nlp = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)
text = ["Süper Lig'in 6. haftasında Sivasspor ile Çaykur Rizespor karşı karşıya geldi...",
"Son 24 saatte 69 kişi Kovid-19 nedeniyle yaşamını yitirdi, 1573 kişi iyileşti"]
out = nlp(text)
label_dict = {
'LABEL_0': 'ekonomi',
'LABEL_1': 'spor',
'LABEL_2': 'saglik',
'LABEL_3': 'kultur_sanat',
'LABEL_4': 'bilim_teknoloji',
'LABEL_5': 'egitim'
}
results = []
for result in out:
result['label'] = label_dict[result['label']]
results.append(result)
print(results)
# > [{'label': 'spor', 'score': 0.9992026090621948}, {'label': 'saglik', 'score': 0.9972177147865295}]
|
{"language": "tr"}
|
text-classification
|
gurkan08/bert-turkish-text-classification
|
[
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"tr",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"tr"
] |
TAGS
#transformers #pytorch #jax #bert #text-classification #tr #autotrain_compatible #endpoints_compatible #region-us
|
# Turkish News Text Classification
Turkish text classification model obtained by fine-tuning the Turkish bert model (dbmdz/bert-base-turkish-cased)
# Dataset
Dataset consists of 11 classes were obtained from URL The model was created using the most distinctive 6 classes.
Dataset can be accessed at URL
label_dict = {
'LABEL_0': 'ekonomi',
'LABEL_1': 'spor',
'LABEL_2': 'saglik',
'LABEL_3': 'kultur_sanat',
'LABEL_4': 'bilim_teknoloji',
'LABEL_5': 'egitim'
}
70% of the data were used for training and 30% for testing.
train f1-weighted score = %97
test f1-weighted score = %94
# Usage
from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("gurkan08/bert-turkish-text-classification")
model = AutoModelForSequenceClassification.from_pretrained("gurkan08/bert-turkish-text-classification")
nlp = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)
text = ["Süper Lig'in 6. haftasında Sivasspor ile Çaykur Rizespor karşı karşıya geldi...",
"Son 24 saatte 69 kişi Kovid-19 nedeniyle yaşamını yitirdi, 1573 kişi iyileşti"]
out = nlp(text)
label_dict = {
'LABEL_0': 'ekonomi',
'LABEL_1': 'spor',
'LABEL_2': 'saglik',
'LABEL_3': 'kultur_sanat',
'LABEL_4': 'bilim_teknoloji',
'LABEL_5': 'egitim'
}
results = []
for result in out:
result['label'] = label_dict[result['label']]
URL(result)
print(results)
# > [{'label': 'spor', 'score': 0.9992026090621948}, {'label': 'saglik', 'score': 0.9972177147865295}]
|
[
"# Turkish News Text Classification\n\n Turkish text classification model obtained by fine-tuning the Turkish bert model (dbmdz/bert-base-turkish-cased)",
"# Dataset\n\nDataset consists of 11 classes were obtained from URL The model was created using the most distinctive 6 classes.\n\nDataset can be accessed at URL\n\n label_dict = {\n 'LABEL_0': 'ekonomi',\n 'LABEL_1': 'spor',\n 'LABEL_2': 'saglik',\n 'LABEL_3': 'kultur_sanat',\n 'LABEL_4': 'bilim_teknoloji',\n 'LABEL_5': 'egitim'\n }\n\n70% of the data were used for training and 30% for testing.\n\ntrain f1-weighted score = %97\n\ntest f1-weighted score = %94",
"# Usage\n\n from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification\n\n tokenizer = AutoTokenizer.from_pretrained(\"gurkan08/bert-turkish-text-classification\")\n model = AutoModelForSequenceClassification.from_pretrained(\"gurkan08/bert-turkish-text-classification\")\n\n nlp = pipeline(\"sentiment-analysis\", model=model, tokenizer=tokenizer)\n\n text = [\"Süper Lig'in 6. haftasında Sivasspor ile Çaykur Rizespor karşı karşıya geldi...\",\n \"Son 24 saatte 69 kişi Kovid-19 nedeniyle yaşamını yitirdi, 1573 kişi iyileşti\"]\n\n out = nlp(text)\n \n label_dict = {\n 'LABEL_0': 'ekonomi',\n 'LABEL_1': 'spor',\n 'LABEL_2': 'saglik',\n 'LABEL_3': 'kultur_sanat',\n 'LABEL_4': 'bilim_teknoloji',\n 'LABEL_5': 'egitim'\n }\n\n results = []\n for result in out:\n result['label'] = label_dict[result['label']]\n URL(result)\n print(results)\n\n # > [{'label': 'spor', 'score': 0.9992026090621948}, {'label': 'saglik', 'score': 0.9972177147865295}]"
] |
[
"TAGS\n#transformers #pytorch #jax #bert #text-classification #tr #autotrain_compatible #endpoints_compatible #region-us \n",
"# Turkish News Text Classification\n\n Turkish text classification model obtained by fine-tuning the Turkish bert model (dbmdz/bert-base-turkish-cased)",
"# Dataset\n\nDataset consists of 11 classes were obtained from URL The model was created using the most distinctive 6 classes.\n\nDataset can be accessed at URL\n\n label_dict = {\n 'LABEL_0': 'ekonomi',\n 'LABEL_1': 'spor',\n 'LABEL_2': 'saglik',\n 'LABEL_3': 'kultur_sanat',\n 'LABEL_4': 'bilim_teknoloji',\n 'LABEL_5': 'egitim'\n }\n\n70% of the data were used for training and 30% for testing.\n\ntrain f1-weighted score = %97\n\ntest f1-weighted score = %94",
"# Usage\n\n from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification\n\n tokenizer = AutoTokenizer.from_pretrained(\"gurkan08/bert-turkish-text-classification\")\n model = AutoModelForSequenceClassification.from_pretrained(\"gurkan08/bert-turkish-text-classification\")\n\n nlp = pipeline(\"sentiment-analysis\", model=model, tokenizer=tokenizer)\n\n text = [\"Süper Lig'in 6. haftasında Sivasspor ile Çaykur Rizespor karşı karşıya geldi...\",\n \"Son 24 saatte 69 kişi Kovid-19 nedeniyle yaşamını yitirdi, 1573 kişi iyileşti\"]\n\n out = nlp(text)\n \n label_dict = {\n 'LABEL_0': 'ekonomi',\n 'LABEL_1': 'spor',\n 'LABEL_2': 'saglik',\n 'LABEL_3': 'kultur_sanat',\n 'LABEL_4': 'bilim_teknoloji',\n 'LABEL_5': 'egitim'\n }\n\n results = []\n for result in out:\n result['label'] = label_dict[result['label']]\n URL(result)\n print(results)\n\n # > [{'label': 'spor', 'score': 0.9992026090621948}, {'label': 'saglik', 'score': 0.9972177147865295}]"
] |
[
41,
41,
146,
337
] |
[
"passage: TAGS\n#transformers #pytorch #jax #bert #text-classification #tr #autotrain_compatible #endpoints_compatible #region-us \n# Turkish News Text Classification\n\n Turkish text classification model obtained by fine-tuning the Turkish bert model (dbmdz/bert-base-turkish-cased)# Dataset\n\nDataset consists of 11 classes were obtained from URL The model was created using the most distinctive 6 classes.\n\nDataset can be accessed at URL\n\n label_dict = {\n 'LABEL_0': 'ekonomi',\n 'LABEL_1': 'spor',\n 'LABEL_2': 'saglik',\n 'LABEL_3': 'kultur_sanat',\n 'LABEL_4': 'bilim_teknoloji',\n 'LABEL_5': 'egitim'\n }\n\n70% of the data were used for training and 30% for testing.\n\ntrain f1-weighted score = %97\n\ntest f1-weighted score = %94"
] |
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null | null |
transformers
|
# Rick bot
|
{"tags": ["conversational"]}
|
text-generation
|
gusintheshell/DialoGPT-small-rickbot
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Rick bot
|
[
"# Rick bot"
] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Rick bot"
] |
[
51,
3
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Rick bot"
] |
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] |
null | null |
transformers
|
### Quantized BigScience's T0 3B with 8-bit weights
This is a version of [BigScience's T0](https://huggingface.co/bigscience/T0_3B) with 3 billion parameters that is modified so you can generate **and fine-tune the model in colab or equivalent desktop gpu (e.g. single 1080Ti)**. Inspired by [GPT-J 8bit](https://huggingface.co/hivemind/gpt-j-6B-8bit).
Here's how to run it: [](https://colab.research.google.com/drive/1lMja-CPc0vm5_-gXNXAWU-9c0nom7vZ9)
This model can be easily loaded using the `T5ForConditionalGeneration` functionality:
```python
from transformers import T5ForConditionalGeneration
model = T5ForConditionalGeneration.from_pretrained("gustavecortal/T0_3B-8bit")
```
Before loading, you have to Monkey-Patch T5:
```python
class T5ForConditionalGeneration(transformers.models.t5.modeling_t5.T5ForConditionalGeneration):
def __init__(self, config):
super().__init__(config)
convert_to_int8(self)
transformers.models.t5.modeling_t5.T5ForConditionalGeneration = T5ForConditionalGeneration
```
## Model Description
T0* shows zero-shot task generalization on English natural language prompts, outperforming GPT-3 on many tasks, while being 16x smaller. It is a series of encoder-decoder models trained on a large set of different tasks specified in natural language prompts. We convert numerous English supervised datasets into prompts, each with multiple templates using varying formulations. These prompted datasets allow for benchmarking the ability of a model to perform completely unseen tasks specified in natural language. To obtain T0*, we fine-tune a pretrained language model on this multitask mixture covering many different NLP tasks.
## Links
* [BigScience](https://bigscience.huggingface.co/)
* [Hivemind](https://training-transformers-together.github.io/)
* [Gustave Cortal](https://twitter.com/gustavecortal)
```bibtex
@misc{sanh2021multitask,
title={Multitask Prompted Training Enables Zero-Shot Task Generalization},
author={Victor Sanh and Albert Webson and Colin Raffel and Stephen H. Bach and Lintang Sutawika and Zaid Alyafeai and Antoine Chaffin and Arnaud Stiegler and Teven Le Scao and Arun Raja and Manan Dey and M Saiful Bari and Canwen Xu and Urmish Thakker and Shanya Sharma Sharma and Eliza Szczechla and Taewoon Kim and Gunjan Chhablani and Nihal Nayak and Debajyoti Datta and Jonathan Chang and Mike Tian-Jian Jiang and Han Wang and Matteo Manica and Sheng Shen and Zheng Xin Yong and Harshit Pandey and Rachel Bawden and Thomas Wang and Trishala Neeraj and Jos Rozen and Abheesht Sharma and Andrea Santilli and Thibault Fevry and Jason Alan Fries and Ryan Teehan and Stella Biderman and Leo Gao and Tali Bers and Thomas Wolf and Alexander M. Rush},
year={2021},
eprint={2110.08207},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
|
{"language": "fr", "license": "mit", "tags": ["en"], "datasets": ["bigscience/P3"]}
|
text2text-generation
|
gustavecortal/T0_3B-8bit
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"en",
"fr",
"dataset:bigscience/P3",
"arxiv:2110.08207",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2110.08207"
] |
[
"fr"
] |
TAGS
#transformers #pytorch #t5 #text2text-generation #en #fr #dataset-bigscience/P3 #arxiv-2110.08207 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
### Quantized BigScience's T0 3B with 8-bit weights
This is a version of BigScience's T0 with 3 billion parameters that is modified so you can generate and fine-tune the model in colab or equivalent desktop gpu (e.g. single 1080Ti). Inspired by GPT-J 8bit.
Here's how to run it: ![colab](URL
This model can be easily loaded using the 'T5ForConditionalGeneration' functionality:
Before loading, you have to Monkey-Patch T5:
## Model Description
T0* shows zero-shot task generalization on English natural language prompts, outperforming GPT-3 on many tasks, while being 16x smaller. It is a series of encoder-decoder models trained on a large set of different tasks specified in natural language prompts. We convert numerous English supervised datasets into prompts, each with multiple templates using varying formulations. These prompted datasets allow for benchmarking the ability of a model to perform completely unseen tasks specified in natural language. To obtain T0*, we fine-tune a pretrained language model on this multitask mixture covering many different NLP tasks.
## Links
* BigScience
* Hivemind
* Gustave Cortal
|
[
"### Quantized BigScience's T0 3B with 8-bit weights\n\n\nThis is a version of BigScience's T0 with 3 billion parameters that is modified so you can generate and fine-tune the model in colab or equivalent desktop gpu (e.g. single 1080Ti). Inspired by GPT-J 8bit. \n\nHere's how to run it: ![colab](URL\n\nThis model can be easily loaded using the 'T5ForConditionalGeneration' functionality:\n\n\nBefore loading, you have to Monkey-Patch T5:",
"## Model Description\n\nT0* shows zero-shot task generalization on English natural language prompts, outperforming GPT-3 on many tasks, while being 16x smaller. It is a series of encoder-decoder models trained on a large set of different tasks specified in natural language prompts. We convert numerous English supervised datasets into prompts, each with multiple templates using varying formulations. These prompted datasets allow for benchmarking the ability of a model to perform completely unseen tasks specified in natural language. To obtain T0*, we fine-tune a pretrained language model on this multitask mixture covering many different NLP tasks.",
"## Links\n\n* BigScience\n* Hivemind\n* Gustave Cortal"
] |
[
"TAGS\n#transformers #pytorch #t5 #text2text-generation #en #fr #dataset-bigscience/P3 #arxiv-2110.08207 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Quantized BigScience's T0 3B with 8-bit weights\n\n\nThis is a version of BigScience's T0 with 3 billion parameters that is modified so you can generate and fine-tune the model in colab or equivalent desktop gpu (e.g. single 1080Ti). Inspired by GPT-J 8bit. \n\nHere's how to run it: ![colab](URL\n\nThis model can be easily loaded using the 'T5ForConditionalGeneration' functionality:\n\n\nBefore loading, you have to Monkey-Patch T5:",
"## Model Description\n\nT0* shows zero-shot task generalization on English natural language prompts, outperforming GPT-3 on many tasks, while being 16x smaller. It is a series of encoder-decoder models trained on a large set of different tasks specified in natural language prompts. We convert numerous English supervised datasets into prompts, each with multiple templates using varying formulations. These prompted datasets allow for benchmarking the ability of a model to perform completely unseen tasks specified in natural language. To obtain T0*, we fine-tune a pretrained language model on this multitask mixture covering many different NLP tasks.",
"## Links\n\n* BigScience\n* Hivemind\n* Gustave Cortal"
] |
[
76,
125,
153,
14
] |
[
"passage: TAGS\n#transformers #pytorch #t5 #text2text-generation #en #fr #dataset-bigscience/P3 #arxiv-2110.08207 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Quantized BigScience's T0 3B with 8-bit weights\n\n\nThis is a version of BigScience's T0 with 3 billion parameters that is modified so you can generate and fine-tune the model in colab or equivalent desktop gpu (e.g. single 1080Ti). Inspired by GPT-J 8bit. \n\nHere's how to run it: ![colab](URL\n\nThis model can be easily loaded using the 'T5ForConditionalGeneration' functionality:\n\n\nBefore loading, you have to Monkey-Patch T5:## Model Description\n\nT0* shows zero-shot task generalization on English natural language prompts, outperforming GPT-3 on many tasks, while being 16x smaller. It is a series of encoder-decoder models trained on a large set of different tasks specified in natural language prompts. We convert numerous English supervised datasets into prompts, each with multiple templates using varying formulations. These prompted datasets allow for benchmarking the ability of a model to perform completely unseen tasks specified in natural language. To obtain T0*, we fine-tune a pretrained language model on this multitask mixture covering many different NLP tasks.## Links\n\n* BigScience\n* Hivemind\n* Gustave Cortal"
] |
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] |
null | null |
transformers
|
### Quantized Cedille/fr-boris with 8-bit weights
This is a version of Cedille's GPT-J (fr-boris) with 6 billion parameters that is modified so you can generate **and fine-tune the model in colab or equivalent desktop gpu (e.g. single 1080Ti)**. Inspired by [GPT-J 8bit](https://huggingface.co/hivemind/gpt-j-6B-8bit).
Here's how to run it: [](https://colab.research.google.com/drive/1lMja-CPc0vm5_-gXNXAWU-9c0nom7vZ9)
This model can be easily loaded using the `GPTJForCausalLM` functionality:
```python
from transformers import GPTJForCausalLM
model = GPTJForCausalLM.from_pretrained("gustavecortal/fr-boris-8bit")
```
## fr-boris
Boris is a 6B parameter autoregressive language model based on the GPT-J architecture and trained using the [mesh-transformer-jax](https://github.com/kingoflolz/mesh-transformer-jax) codebase.
Boris was trained on around 78B tokens of French text from the [C4](https://huggingface.co/datasets/c4) dataset.
## Links
* [Cedille](https://en.cedille.ai/)
* [Hivemind](https://training-transformers-together.github.io/)
* [Gustave Cortal](https://twitter.com/gustavecortal)
|
{"language": "fr", "license": "mit", "tags": ["causal-lm", "fr"], "datasets": ["c4", "The Pile"]}
|
text-generation
|
gustavecortal/fr-boris-8bit
|
[
"transformers",
"pytorch",
"gptj",
"text-generation",
"causal-lm",
"fr",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"fr"
] |
TAGS
#transformers #pytorch #gptj #text-generation #causal-lm #fr #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
|
### Quantized Cedille/fr-boris with 8-bit weights
This is a version of Cedille's GPT-J (fr-boris) with 6 billion parameters that is modified so you can generate and fine-tune the model in colab or equivalent desktop gpu (e.g. single 1080Ti). Inspired by GPT-J 8bit.
Here's how to run it: ![colab](URL
This model can be easily loaded using the 'GPTJForCausalLM' functionality:
## fr-boris
Boris is a 6B parameter autoregressive language model based on the GPT-J architecture and trained using the mesh-transformer-jax codebase.
Boris was trained on around 78B tokens of French text from the C4 dataset.
## Links
* Cedille
* Hivemind
* Gustave Cortal
|
[
"### Quantized Cedille/fr-boris with 8-bit weights\n\n\nThis is a version of Cedille's GPT-J (fr-boris) with 6 billion parameters that is modified so you can generate and fine-tune the model in colab or equivalent desktop gpu (e.g. single 1080Ti). Inspired by GPT-J 8bit. \n\nHere's how to run it: ![colab](URL\n\nThis model can be easily loaded using the 'GPTJForCausalLM' functionality:",
"## fr-boris\n\nBoris is a 6B parameter autoregressive language model based on the GPT-J architecture and trained using the mesh-transformer-jax codebase.\n\nBoris was trained on around 78B tokens of French text from the C4 dataset.",
"## Links\n\n* Cedille\n* Hivemind\n* Gustave Cortal"
] |
[
"TAGS\n#transformers #pytorch #gptj #text-generation #causal-lm #fr #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Quantized Cedille/fr-boris with 8-bit weights\n\n\nThis is a version of Cedille's GPT-J (fr-boris) with 6 billion parameters that is modified so you can generate and fine-tune the model in colab or equivalent desktop gpu (e.g. single 1080Ti). Inspired by GPT-J 8bit. \n\nHere's how to run it: ![colab](URL\n\nThis model can be easily loaded using the 'GPTJForCausalLM' functionality:",
"## fr-boris\n\nBoris is a 6B parameter autoregressive language model based on the GPT-J architecture and trained using the mesh-transformer-jax codebase.\n\nBoris was trained on around 78B tokens of French text from the C4 dataset.",
"## Links\n\n* Cedille\n* Hivemind\n* Gustave Cortal"
] |
[
55,
119,
61,
15
] |
[
"passage: TAGS\n#transformers #pytorch #gptj #text-generation #causal-lm #fr #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n### Quantized Cedille/fr-boris with 8-bit weights\n\n\nThis is a version of Cedille's GPT-J (fr-boris) with 6 billion parameters that is modified so you can generate and fine-tune the model in colab or equivalent desktop gpu (e.g. single 1080Ti). Inspired by GPT-J 8bit. \n\nHere's how to run it: ![colab](URL\n\nThis model can be easily loaded using the 'GPTJForCausalLM' functionality:## fr-boris\n\nBoris is a 6B parameter autoregressive language model based on the GPT-J architecture and trained using the mesh-transformer-jax codebase.\n\nBoris was trained on around 78B tokens of French text from the C4 dataset.## Links\n\n* Cedille\n* Hivemind\n* Gustave Cortal"
] |
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] |
null | null |
transformers
|
### Quantized EleutherAI/gpt-neo-2.7B with 8-bit weights
This is a version of [EleutherAI's GPT-Neo](https://huggingface.co/EleutherAI/gpt-neo-2.7B) with 2.7 billion parameters that is modified so you can generate **and fine-tune the model in colab or equivalent desktop gpu (e.g. single 1080Ti)**. Inspired by [GPT-J 8bit](https://huggingface.co/hivemind/gpt-j-6B-8bit).
Here's how to run it: [](https://colab.research.google.com/drive/1lMja-CPc0vm5_-gXNXAWU-9c0nom7vZ9)
## Model Description
GPT-Neo 2.7B is a transformer model designed using EleutherAI's replication of the GPT-3 architecture. GPT-Neo refers to the class of models, while 2.7B represents the number of parameters of this particular pre-trained model.
## Links
* [EleutherAI](https://www.eleuther.ai)
* [Hivemind](https://training-transformers-together.github.io/)
* [Gustave Cortal](https://twitter.com/gustavecortal)
|
{"language": "en", "license": "mit", "tags": ["causal-lm"], "datasets": ["The_Pile"]}
|
text-generation
|
gustavecortal/gpt-neo-2.7B-8bit
|
[
"transformers",
"pytorch",
"gpt_neo",
"text-generation",
"causal-lm",
"en",
"dataset:The_Pile",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #gpt_neo #text-generation #causal-lm #en #dataset-The_Pile #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
### Quantized EleutherAI/gpt-neo-2.7B with 8-bit weights
This is a version of EleutherAI's GPT-Neo with 2.7 billion parameters that is modified so you can generate and fine-tune the model in colab or equivalent desktop gpu (e.g. single 1080Ti). Inspired by GPT-J 8bit.
Here's how to run it: ![colab](URL
## Model Description
GPT-Neo 2.7B is a transformer model designed using EleutherAI's replication of the GPT-3 architecture. GPT-Neo refers to the class of models, while 2.7B represents the number of parameters of this particular pre-trained model.
## Links
* EleutherAI
* Hivemind
* Gustave Cortal
|
[
"### Quantized EleutherAI/gpt-neo-2.7B with 8-bit weights\n\n\nThis is a version of EleutherAI's GPT-Neo with 2.7 billion parameters that is modified so you can generate and fine-tune the model in colab or equivalent desktop gpu (e.g. single 1080Ti). Inspired by GPT-J 8bit. \n\nHere's how to run it: ![colab](URL",
"## Model Description\n\nGPT-Neo 2.7B is a transformer model designed using EleutherAI's replication of the GPT-3 architecture. GPT-Neo refers to the class of models, while 2.7B represents the number of parameters of this particular pre-trained model.",
"## Links\n\n* EleutherAI\n* Hivemind\n* Gustave Cortal"
] |
[
"TAGS\n#transformers #pytorch #gpt_neo #text-generation #causal-lm #en #dataset-The_Pile #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Quantized EleutherAI/gpt-neo-2.7B with 8-bit weights\n\n\nThis is a version of EleutherAI's GPT-Neo with 2.7 billion parameters that is modified so you can generate and fine-tune the model in colab or equivalent desktop gpu (e.g. single 1080Ti). Inspired by GPT-J 8bit. \n\nHere's how to run it: ![colab](URL",
"## Model Description\n\nGPT-Neo 2.7B is a transformer model designed using EleutherAI's replication of the GPT-3 architecture. GPT-Neo refers to the class of models, while 2.7B represents the number of parameters of this particular pre-trained model.",
"## Links\n\n* EleutherAI\n* Hivemind\n* Gustave Cortal"
] |
[
60,
99,
67,
16
] |
[
"passage: TAGS\n#transformers #pytorch #gpt_neo #text-generation #causal-lm #en #dataset-The_Pile #license-mit #autotrain_compatible #endpoints_compatible #region-us \n### Quantized EleutherAI/gpt-neo-2.7B with 8-bit weights\n\n\nThis is a version of EleutherAI's GPT-Neo with 2.7 billion parameters that is modified so you can generate and fine-tune the model in colab or equivalent desktop gpu (e.g. single 1080Ti). Inspired by GPT-J 8bit. \n\nHere's how to run it: ![colab](URL## Model Description\n\nGPT-Neo 2.7B is a transformer model designed using EleutherAI's replication of the GPT-3 architecture. GPT-Neo refers to the class of models, while 2.7B represents the number of parameters of this particular pre-trained model.## Links\n\n* EleutherAI\n* Hivemind\n* Gustave Cortal"
] |
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] |
null | null |
transformers
|
# Wav2Vec2-Large-XLSR-53-ml
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on ml (Malayalam) using the [Indic TTS Malayalam Speech Corpus (via Kaggle)](https://www.kaggle.com/kavyamanohar/indic-tts-malayalam-speech-corpus), [Openslr Malayalam Speech Corpus](http://openslr.org/63/), [SMC Malayalam Speech Corpus](https://blog.smc.org.in/malayalam-speech-corpus/) and [IIIT-H Indic Speech Databases](http://speech.iiit.ac.in/index.php/research-svl/69.html). The notebooks used to train model are available [here](https://github.com/gauthamsuresh09/wav2vec2-large-xlsr-53-malayalam/). When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
```python
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
test_dataset = <load-test-split-of-combined-dataset> # Details on loading this dataset in the evaluation section
processor = Wav2Vec2Processor.from_pretrained("gvs/wav2vec2-large-xlsr-malayalam")
model = Wav2Vec2ForCTC.from_pretrained("gvs/wav2vec2-large-xlsr-malayalam")
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the audio files as arrays
def speech_file_to_array_fn(batch):
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
predicted_ids = torch.argmax(logits, dim=-1)
print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset["sentence"])
```
## Evaluation
The model can be evaluated as follows on the test data of combined custom dataset. For more details on dataset preparation, check the notebooks mentioned at the end of this file.
```python
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re
from datasets import load_dataset, load_metric
from pathlib import Path
# The custom dataset needs to be created using notebook mentioned at the end of this file
data_dir = Path('<path-to-custom-dataset>')
dataset_folders = {
'iiit': 'iiit_mal_abi',
'openslr': 'openslr',
'indic-tts': 'indic-tts-ml',
'msc-reviewed': 'msc-reviewed-speech-v1.0+20200825',
}
# Set directories for datasets
openslr_male_dir = data_dir / dataset_folders['openslr'] / 'male'
openslr_female_dir = data_dir / dataset_folders['openslr'] / 'female'
iiit_dir = data_dir / dataset_folders['iiit']
indic_tts_male_dir = data_dir / dataset_folders['indic-tts'] / 'male'
indic_tts_female_dir = data_dir / dataset_folders['indic-tts'] / 'female'
msc_reviewed_dir = data_dir / dataset_folders['msc-reviewed']
# Load the datasets
openslr_male = load_dataset("json", data_files=[f"{str(openslr_male_dir.absolute())}/sample_{i}.json" for i in range(2023)], split="train")
openslr_female = load_dataset("json", data_files=[f"{str(openslr_female_dir.absolute())}/sample_{i}.json" for i in range(2103)], split="train")
iiit = load_dataset("json", data_files=[f"{str(iiit_dir.absolute())}/sample_{i}.json" for i in range(1000)], split="train")
indic_tts_male = load_dataset("json", data_files=[f"{str(indic_tts_male_dir.absolute())}/sample_{i}.json" for i in range(5649)], split="train")
indic_tts_female = load_dataset("json", data_files=[f"{str(indic_tts_female_dir.absolute())}/sample_{i}.json" for i in range(2950)], split="train")
msc_reviewed = load_dataset("json", data_files=[f"{str(msc_reviewed_dir.absolute())}/sample_{i}.json" for i in range(1541)], split="train")
# Create test split as 20%, set random seed as well.
test_size = 0.2
random_seed=1
openslr_male_splits = openslr_male.train_test_split(test_size=test_size, seed=random_seed)
openslr_female_splits = openslr_female.train_test_split(test_size=test_size, seed=random_seed)
iiit_splits = iiit.train_test_split(test_size=test_size, seed=random_seed)
indic_tts_male_splits = indic_tts_male.train_test_split(test_size=test_size, seed=random_seed)
indic_tts_female_splits = indic_tts_female.train_test_split(test_size=test_size, seed=random_seed)
msc_reviewed_splits = msc_reviewed.train_test_split(test_size=test_size, seed=random_seed)
# Get combined test dataset
split_list = [openslr_male_splits, openslr_female_splits, indic_tts_male_splits, indic_tts_female_splits, msc_reviewed_splits, iiit_splits]
test_dataset = datasets.concatenate_datasets([split['test'] for split in split_list)
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("gvs/wav2vec2-large-xlsr-malayalam")
model = Wav2Vec2ForCTC.from_pretrained("gvs/wav2vec2-large-xlsr-malayalam")
model.to("cuda")
resamplers = {
48000: torchaudio.transforms.Resample(48_000, 16_000),
}
chars_to_ignore_regex = '[\\\\,\\\\?\\\\.\\\\!\\\\-\\\\;\\\\:\\\\"\\\\“\\\\%\\\\‘\\\\”\\\\�Utrnle\\\\_]'
unicode_ignore_regex = r'[\\\\u200e]'
# Preprocessing the datasets.
# We need to read the audio files as arrays
def speech_file_to_array_fn(batch):
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"])
batch["sentence"] = re.sub(unicode_ignore_regex, '', batch["sentence"])
speech_array, sampling_rate = torchaudio.load(batch["path"])
# Resample if its not in 16kHz
if sampling_rate != 16000:
batch["speech"] = resamplers[sampling_rate](speech_array).squeeze().numpy()
else:
batch["speech"] = speech_array.squeeze().numpy()
# If more than one dimension is present, pick first one
if batch["speech"].ndim > 1:
batch["speech"] = batch["speech"][0]
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
# Preprocessing the datasets.
# We need to read the audio files as arrays
def evaluate(batch):
inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
pred_ids = torch.argmax(logits, dim=-1)
batch["pred_strings"] = processor.batch_decode(pred_ids)
return batch
result = test_dataset.map(evaluate, batched=True, batch_size=8)
print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
```
**Test Result (WER)**: 28.43 %
## Training
A combined dataset was created using [Indic TTS Malayalam Speech Corpus (via Kaggle)](https://www.kaggle.com/kavyamanohar/indic-tts-malayalam-speech-corpus), [Openslr Malayalam Speech Corpus](http://openslr.org/63/), [SMC Malayalam Speech Corpus](https://blog.smc.org.in/malayalam-speech-corpus/) and [IIIT-H Indic Speech Databases](http://speech.iiit.ac.in/index.php/research-svl/69.html). The datasets were downloaded and was converted to HF Dataset format using [this notebook](https://github.com/gauthamsuresh09/wav2vec2-large-xlsr-53-malayalam/blob/main/make_hf_dataset.ipynb)
The notebook used for training and evaluation can be found [here](https://github.com/gauthamsuresh09/wav2vec2-large-xlsr-53-malayalam/blob/main/fine-tune-xlsr-wav2vec2-on-malayalam-asr-with-transformers_v2.ipynb)
|
{"language": "ml", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["Indic TTS Malayalam Speech Corpus", "Openslr Malayalam Speech Corpus", "SMC Malayalam Speech Corpus", "IIIT-H Indic Speech Databases"], "metrics": ["wer"], "model-index": [{"name": "Malayalam XLSR Wav2Vec2 Large 53", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Test split of combined dataset using all datasets mentioned above", "type": "custom", "args": "ml"}, "metrics": [{"type": "wer", "value": 28.43, "name": "Test WER"}]}]}]}
|
automatic-speech-recognition
|
gvs/wav2vec2-large-xlsr-malayalam
|
[
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"ml",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"ml"
] |
TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #ml #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us
|
# Wav2Vec2-Large-XLSR-53-ml
Fine-tuned facebook/wav2vec2-large-xlsr-53 on ml (Malayalam) using the Indic TTS Malayalam Speech Corpus (via Kaggle), Openslr Malayalam Speech Corpus, SMC Malayalam Speech Corpus and IIIT-H Indic Speech Databases. The notebooks used to train model are available here. When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
## Evaluation
The model can be evaluated as follows on the test data of combined custom dataset. For more details on dataset preparation, check the notebooks mentioned at the end of this file.
Test Result (WER): 28.43 %
## Training
A combined dataset was created using Indic TTS Malayalam Speech Corpus (via Kaggle), Openslr Malayalam Speech Corpus, SMC Malayalam Speech Corpus and IIIT-H Indic Speech Databases. The datasets were downloaded and was converted to HF Dataset format using this notebook
The notebook used for training and evaluation can be found here
|
[
"# Wav2Vec2-Large-XLSR-53-ml\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on ml (Malayalam) using the Indic TTS Malayalam Speech Corpus (via Kaggle), Openslr Malayalam Speech Corpus, SMC Malayalam Speech Corpus and IIIT-H Indic Speech Databases. The notebooks used to train model are available here. When using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model can be evaluated as follows on the test data of combined custom dataset. For more details on dataset preparation, check the notebooks mentioned at the end of this file.\n\n\n\n\nTest Result (WER): 28.43 %",
"## Training\n\nA combined dataset was created using Indic TTS Malayalam Speech Corpus (via Kaggle), Openslr Malayalam Speech Corpus, SMC Malayalam Speech Corpus and IIIT-H Indic Speech Databases. The datasets were downloaded and was converted to HF Dataset format using this notebook\n\nThe notebook used for training and evaluation can be found here"
] |
[
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #ml #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us \n",
"# Wav2Vec2-Large-XLSR-53-ml\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on ml (Malayalam) using the Indic TTS Malayalam Speech Corpus (via Kaggle), Openslr Malayalam Speech Corpus, SMC Malayalam Speech Corpus and IIIT-H Indic Speech Databases. The notebooks used to train model are available here. When using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model can be evaluated as follows on the test data of combined custom dataset. For more details on dataset preparation, check the notebooks mentioned at the end of this file.\n\n\n\n\nTest Result (WER): 28.43 %",
"## Training\n\nA combined dataset was created using Indic TTS Malayalam Speech Corpus (via Kaggle), Openslr Malayalam Speech Corpus, SMC Malayalam Speech Corpus and IIIT-H Indic Speech Databases. The datasets were downloaded and was converted to HF Dataset format using this notebook\n\nThe notebook used for training and evaluation can be found here"
] |
[
75,
109,
20,
51,
76
] |
[
"passage: TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #ml #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us \n# Wav2Vec2-Large-XLSR-53-ml\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on ml (Malayalam) using the Indic TTS Malayalam Speech Corpus (via Kaggle), Openslr Malayalam Speech Corpus, SMC Malayalam Speech Corpus and IIIT-H Indic Speech Databases. The notebooks used to train model are available here. When using this model, make sure that your speech input is sampled at 16kHz.## Usage\n\nThe model can be used directly (without a language model) as follows:## Evaluation\n\nThe model can be evaluated as follows on the test data of combined custom dataset. For more details on dataset preparation, check the notebooks mentioned at the end of this file.\n\n\n\n\nTest Result (WER): 28.43 %## Training\n\nA combined dataset was created using Indic TTS Malayalam Speech Corpus (via Kaggle), Openslr Malayalam Speech Corpus, SMC Malayalam Speech Corpus and IIIT-H Indic Speech Databases. The datasets were downloaded and was converted to HF Dataset format using this notebook\n\nThe notebook used for training and evaluation can be found here"
] |
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] |
null | null |
transformers
|
"5050_base_test"
|
{}
| null |
gwkim22/5050_b_disc
|
[
"transformers",
"pytorch",
"electra",
"pretraining",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #electra #pretraining #endpoints_compatible #region-us
|
"5050_base_test"
|
[] |
[
"TAGS\n#transformers #pytorch #electra #pretraining #endpoints_compatible #region-us \n"
] |
[
27
] |
[
"passage: TAGS\n#transformers #pytorch #electra #pretraining #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
"test_5050"
|
{}
| null |
gwkim22/5050_s_disc
|
[
"transformers",
"pytorch",
"electra",
"pretraining",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #electra #pretraining #endpoints_compatible #region-us
|
"test_5050"
|
[] |
[
"TAGS\n#transformers #pytorch #electra #pretraining #endpoints_compatible #region-us \n"
] |
[
27
] |
[
"passage: TAGS\n#transformers #pytorch #electra #pretraining #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
"domain_base_test"
|
{}
| null |
gwkim22/domain_b_disc
|
[
"transformers",
"pytorch",
"electra",
"pretraining",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #electra #pretraining #endpoints_compatible #region-us
|
"domain_base_test"
|
[] |
[
"TAGS\n#transformers #pytorch #electra #pretraining #endpoints_compatible #region-us \n"
] |
[
27
] |
[
"passage: TAGS\n#transformers #pytorch #electra #pretraining #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
"domain_base2_disc_0719"
|
{}
| null |
gwkim22/domain_base2_disc
|
[
"transformers",
"pytorch",
"electra",
"pretraining",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #electra #pretraining #endpoints_compatible #region-us
|
"domain_base2_disc_0719"
|
[] |
[
"TAGS\n#transformers #pytorch #electra #pretraining #endpoints_compatible #region-us \n"
] |
[
27
] |
[
"passage: TAGS\n#transformers #pytorch #electra #pretraining #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
"test_domain_only"
|
{}
| null |
gwkim22/domain_s_disc
|
[
"transformers",
"pytorch",
"electra",
"pretraining",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #electra #pretraining #endpoints_compatible #region-us
|
"test_domain_only"
|
[] |
[
"TAGS\n#transformers #pytorch #electra #pretraining #endpoints_compatible #region-us \n"
] |
[
27
] |
[
"passage: TAGS\n#transformers #pytorch #electra #pretraining #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
"general_base_test"
|
{}
| null |
gwkim22/general_b_disc
|
[
"transformers",
"pytorch",
"electra",
"pretraining",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #electra #pretraining #endpoints_compatible #region-us
|
"general_base_test"
|
[] |
[
"TAGS\n#transformers #pytorch #electra #pretraining #endpoints_compatible #region-us \n"
] |
[
27
] |
[
"passage: TAGS\n#transformers #pytorch #electra #pretraining #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
"general_test"
|
{}
| null |
gwkim22/general_s_disc
|
[
"transformers",
"pytorch",
"electra",
"pretraining",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #electra #pretraining #endpoints_compatible #region-us
|
"general_test"
|
[] |
[
"TAGS\n#transformers #pytorch #electra #pretraining #endpoints_compatible #region-us \n"
] |
[
27
] |
[
"passage: TAGS\n#transformers #pytorch #electra #pretraining #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
<!-- 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-xsum
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 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: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| No log | 1.0 | 13 | 3.6429 | 15.3135 | 1.0725 | 12.0447 | 12.445 | 18.97 |
### Framework versions
- Transformers 4.9.0
- Pytorch 1.9.0+cu102
- Datasets 1.10.2
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": [], "model_index": [{"name": "t5-small-finetuned-xsum", "results": [{"task": {"name": "Sequence-to-sequence Language Modeling", "type": "text2text-generation"}}]}]}
|
text2text-generation
|
gwynethfae/t5-small-finetuned-xsum
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
t5-small-finetuned-xsum
=======================
This model is a fine-tuned version of t5-small on the None dataset.
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 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: 1
### Training results
### Framework versions
* Transformers 4.9.0
* Pytorch 1.9.0+cu102
* Datasets 1.10.2
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.9.0\n* Pytorch 1.9.0+cu102\n* Datasets 1.10.2\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.9.0\n* Pytorch 1.9.0+cu102\n* Datasets 1.10.2\n* Tokenizers 0.10.3"
] |
[
67,
98,
4,
35
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1### Training results### Framework versions\n\n\n* Transformers 4.9.0\n* Pytorch 1.9.0+cu102\n* Datasets 1.10.2\n* Tokenizers 0.10.3"
] |
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null | null | null |
# MultiLingual CLIP
Multilingual CLIP is a pre-trained model which can be used for multilingual semantic search and zero-shot image classification in 100 languages.
# Model Architecture
Multilingual CLIP was built using [OpenAI CLIP](https://github.com/openai/CLIP) model. I have used the same Vision encoder (ResNet 50x4), but instead I replaced their text encoder (Transformer) with a Mulilingual Text Encoder ([XLM-Roberta](https://huggingface.co/xlm-roberta-large)) and a configurable number of projection heads, as seen below:
The model was trained in a distributed fashion on 16 Habana Gaudi Accelerators and with mixed Precision in two phases (using COCO Dataset for phase 1 and Google Conceptual Captions for phase 2). The training pipeline was built using PyTorch, PyTorch Lightning, and Distributed Data Parallel.
# Datasets
Three datasets have been used for building the model. COCO captions was used for training phase 1 and Google Conceptual Captions was used for training phase 2. Unsplash dataset was used for testing and inference.
## COCO Captions
COCO (Common Objects in Context) is a large-scale object detection, segmentation, and captioning dataset. The COCO captions dataset has around ~85000 images and captions pairs.
Run the following to download the dataset:
```bash
./download_coco.sh
```
This dataset was used for the first pre-training phase.
## Google Conceptual Captions
Conceptual Captions is a dataset consisting of ~3.3 million images annotated with captions. In contrast with the curated style of other image caption annotations, Conceptual Caption images and their raw descriptions are harvested from the web, and therefore represent a wider variety of styles.
Download the datasets urls/captions from [here](https://storage.cloud.google.com/gcc-data/Train/GCC-training.tsv?_ga=2.191230122.-1896153081.1529438250) as save it to `datasets/googlecc/googlecc.tsv`. The full dataset has over 3 million images, but you can select a subset by loading the `googlecc.tsv` file and saving only the number of rows you want (I have used 1 million images for training).
Then run the following commands to download each image on the `googlecc.tsv` file:
```bash
npm install
node download_build_googlecc.js
```
This dataset was used for the second pre-training phase.
## Unplash
This dataset was used as the test set during inference.
Run `python3.8 download_unsplash.py` to download the dataset.
# Training
## Setup
Create two Habana instances ([AWS EC2 DL1](https://aws.amazon.com/ec2/instance-types/dl1/)) using [Habana® Deep Learning Base AMI (Ubuntu 20.04)](https://aws.amazon.com/marketplace/pp/prodview-fw46rwuxrtfse)
Create the PyTorch docker container running:
```bash
docker run --name pytorch -td --runtime=habana -e HABANA_VISIBLE_DEVICES=all -e OMPI_MCA_btl_vader_single_copy_mechanism=none --cap-add=sys_nice --net=host --ipc=host vault.habana.ai/gaudi-docker/1.2.0/ubuntu20.04/habanalabs/pytorch-installer-1.10.0:1.2.0-585
```
Enter the docker image by running:
```
docker exec -it pytorch /bin/bash
```
#### Setup password-less ssh between all connected servers
1. Configure password-less ssh between all nodes:
Do the following in all the nodes' docker sessions:
```bash
mkdir ~/.ssh
cd ~/.ssh
ssh-keygen -t rsa -b 4096
```
Copy id_rsa.pub contents from every node's docker to every other node's docker's ~/.ssh/authorized_keys (all public keys need to be in all hosts' authorized_keys):
```bash
cat id_rsa.pub > authorized_keys
vi authorized_keys
```
Copy the contents from inside to other systems.
Paste all hosts' public keys in all hosts' “authorized_keys” file.
2. On each system:
Add all hosts (including itself) to known_hosts. The IP addresses used below are just for illustration:
```bash
ssh-keyscan -p 3022 -H $IP1 >> ~/.ssh/known_hosts
ssh-keyscan -p 3022 -H $IP2 >> ~/.ssh/known_hosts
```
3. Change Docker SSH port to 3022
```bash
sed -i 's/#Port 22/Port 3022/g' /etc/ssh/sshd_config
sed -i 's/#PermitRootLogin prohibit-password/PermitRootLogin yes/' /etc/ssh/sshd_config
service ssh restart
```
[Allow all TCP](https://docs.aws.amazon.com/vpc/latest/userguide/VPC_SecurityGroups.html) traffic between the nodes on AWS
Clone the git repo:
```bash
git clone https://github.com/gzomer/clip-multilingual
```
Create environment:
```bash
python3.8 -m venv .env
```
Install requirements:
```bash
python3.8 -r requirements.txt
```
Activate environment
```bash
source .env/bin/activate
```
## Training params
Learning rate: 1e-3
Batch size: 64
Phase 1 - Epochs: 100
Phase 2 - Epochs: 15
## Train script arguments
```
--dataset-num-workers Number of workers (default: 8)
--dataset-type Dataset type (coco or googlecc) (default: coco)
--dataset-dir Dataset dir (default: ./datasets/coco/)
--dataset-subset-size Load only a subset of the dataset (useful for debugging)
--dataset-train-split Dataset train split (default: 0.8)
--train-device Type of device to use (default: hpu)
--distributed-num-nodes Number of nodes (machines) (default: 2)
--distributed-parallel-devices Number of parallel devices per node (default: 8)
--distributed-master-address Master node IP address
--distributed-master-port Master node port (default: 12345)
--distributed-bucket-cap-mb DDP bucket cap MB (default: 200)
--checkpoint-dir Model checkpoint dir (default: ./models)
--checkpoint-save-every-n Save every n epochs (default: 1)
--checkpoint-load-vision-path Load vision encoder checkpoint
--checkpoint-load-text-path Load text encoder checkpoint
--model-visual-name Which visual model to use (default: RN50x4)
--model-textual-name Which textual model to use (default: xlm-roberta-base)
--hyperparam-num-layers Number of layers (default: 3)
--hyperparam-lr Model learning rate (default: 0.001)
--hyperparam-epochs Max epochs (default: 100)
--hyperparam-precision Precision (default: 16)
--hyperparam-batch-size Batch size (default: 64)
--wandb-project W&B project name (default: clip)
--wandb-enabled W&B is enabled? (default: True)
```
## Habana Gaudi - 8 accelerators
### Phase 1 training
```bash
python3.8 train.py --train-device hpu --distributed-parallel-devices 8 --distributed-num-nodes 1
```
### Phase 2 training
```bash
python3.8 train.py --train-device hpu --distributed-parallel-devices 8 --distributed-num-nodes 1 --hyperparam-epochs 15 --checkpoint-load-text-path /home/models/text-last.ckpt --checkpoint-load-vision-path /home/models/vision-last.ckpt --checkpoint-dir ./models_phase2
```
## Habana Gaudi - 16 accelerators (multi-server training)
Change the master IP address based on your instances (use local IP, not public IP).
### Phase 1 training
```bash
NODE_RANK=0 python3.8 train.py --distributed-master-address 172.31.86.231 --train-device hpu --distributed-parallel-devices 8 --distributed-num-nodes 2
```
```bash
NODE_RANK=1 python3.8 train.py --distributed-master-address 172.31.86.231 --train-device hpu --distributed-parallel-devices 8 --distributed-num-nodes 2
```
### Phase 2 training
```bash
NODE_RANK=0 python3.8 train.py --distributed-master-address 172.31.86.231 --train-device hpu --distributed-parallel-devices 8 --distributed-num-nodes 2 --hyperparam-epochs 10 --checkpoint-load-text-path /home/models/text-last.ckpt --checkpoint-load-vision-path /home/models/vision-last.ckpt --checkpoint-dir ./models_phase2
```
```bash
NODE_RANK=1 python3.8 train.py --distributed-master-address 172.31.86.231 --train-device hpu --distributed-parallel-devices 8 --distributed-num-nodes 2 --hyperparam-epochs 15 --checkpoint-load-text-path /home/models/text-last.ckpt --checkpoint-load-vision-path /home/models/vision-last.ckpt --checkpoint-dir ./models_phase2
```
## Other devices
If you don't have access to a Habana Gaudi accelerator yet, you can also train on CPU/GPU, although it will be way slower.
To train on CPU, just pass `--train-device=cpu` and on GPU `--train-device=cuda` to the `train.py` script.
# Inference
## Loading pre-trained model from Hugging Face HUB
```python
from models import create_and_load_from_hub
model = create_and_load_from_hub()
```
## Loading model from local checkpoint
```python
from models import MultiLingualCLIP, load_model
text_checkpoint_path = '/path/to/text model checkpoint'
vision_checkpoint_path = '/path/to/vision model checkpoint'
model = MultiLingualCLIP(num_layers=3)
load_model(model, vision_checkpoint_path, text_checkpoint_path)
```
## Generate embeddings
Run the following (after downloading Unplash dataset):
`python3.8 ./generate_embeddings.py`
## Searching images
```python
import numpy as np
from search import MultiLingualSearch
images_embeddings = np.load('/path/to/images_embeddings')
images_data = [...] # List of image info for each row of the embeddings. For instance, it could be a list of urls, filepaths, ids. They will be returned when calling the search function
semantic_search = MultiLingualSearch(model, images_embeddings, images_data)
results = semantic_search.search('विद्यालय में') # Means at school
print(results)
```
```json
[{"image": "https://images.unsplash.com/photo-1557804506-669a67965ba0?crop=entropy&cs=tinysrgb&fit=max&fm=jpg&ixid=MnwyNDg3OTV8MHwxfHNlYXJjaHwxM3x8bWVldGluZ3N8ZW58MHx8fHwxNjQ1NjA2MjQz&ixlib=rb-1.2.1&q=80&w=400",
"prob": 0.2461608648300171},
{"image": "https://images.unsplash.com/photo-1558403194-611308249627?crop=entropy&cs=tinysrgb&fit=max&fm=jpg&ixid=MnwyNDg3OTV8MHwxfHNlYXJjaHwyMXx8cGVvcGxlJTIwd29ya2luZ3xlbnwwfHx8fDE2NDU2MDMyMjE&ixlib=rb-1.2.1&q=80&w=400",
"prob": 0.16881239414215088},
{"image": "https://images.unsplash.com/photo-1531497865144-0464ef8fb9a9?crop=entropy&cs=tinysrgb&fit=max&fm=jpg&ixid=MnwyNDg3OTV8MHwxfHNlYXJjaHw4Nnx8cGVvcGxlJTIwd29ya2luZ3xlbnwwfHx8fDE2NDU2MDY5ODc&ixlib=rb-1.2.1&q=80&w=400",
"prob": 0.14744874835014343},
{"image": "https://images.unsplash.com/photo-1561089489-f13d5e730d72?crop=entropy&cs=tinysrgb&fit=max&fm=jpg&ixid=MnwyNDg3OTV8MHwxfHNlYXJjaHw5MHx8ZWR1Y2F0aW9ufGVufDB8fHx8MTY0NTYwNjk1Nw&ixlib=rb-1.2.1&q=80&w=400",
"prob": 0.095176100730896},
{"image": "https://images.unsplash.com/photo-1580582932707-520aed937b7b?crop=entropy&cs=tinysrgb&fit=max&fm=jpg&ixid=MnwyNDg3OTV8MHwxfHNlYXJjaHwxMnx8ZWR1Y2F0aW9ufGVufDB8fHx8MTY0NTYwMzIwMA&ixlib=rb-1.2.1&q=80&w=400",
"prob": 0.05218643322587013}]
```
|
{"language": "multilingual", "license": "mit", "tags": ["clip", "vision", "text"]}
| null |
gzomer/clip-multilingual
|
[
"clip",
"vision",
"text",
"multilingual",
"license:mit",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"multilingual"
] |
TAGS
#clip #vision #text #multilingual #license-mit #has_space #region-us
|
# MultiLingual CLIP
Multilingual CLIP is a pre-trained model which can be used for multilingual semantic search and zero-shot image classification in 100 languages.
# Model Architecture
Multilingual CLIP was built using OpenAI CLIP model. I have used the same Vision encoder (ResNet 50x4), but instead I replaced their text encoder (Transformer) with a Mulilingual Text Encoder (XLM-Roberta) and a configurable number of projection heads, as seen below:
!Model Architecture
The model was trained in a distributed fashion on 16 Habana Gaudi Accelerators and with mixed Precision in two phases (using COCO Dataset for phase 1 and Google Conceptual Captions for phase 2). The training pipeline was built using PyTorch, PyTorch Lightning, and Distributed Data Parallel.
# Datasets
Three datasets have been used for building the model. COCO captions was used for training phase 1 and Google Conceptual Captions was used for training phase 2. Unsplash dataset was used for testing and inference.
## COCO Captions
COCO (Common Objects in Context) is a large-scale object detection, segmentation, and captioning dataset. The COCO captions dataset has around ~85000 images and captions pairs.
Run the following to download the dataset:
This dataset was used for the first pre-training phase.
## Google Conceptual Captions
Conceptual Captions is a dataset consisting of ~3.3 million images annotated with captions. In contrast with the curated style of other image caption annotations, Conceptual Caption images and their raw descriptions are harvested from the web, and therefore represent a wider variety of styles.
Download the datasets urls/captions from here as save it to 'datasets/googlecc/URL'. The full dataset has over 3 million images, but you can select a subset by loading the 'URL' file and saving only the number of rows you want (I have used 1 million images for training).
Then run the following commands to download each image on the 'URL' file:
This dataset was used for the second pre-training phase.
## Unplash
This dataset was used as the test set during inference.
Run 'python3.8 download_unsplash.py' to download the dataset.
# Training
!Training phase 1
!Training phase 2
## Setup
Create two Habana instances (AWS EC2 DL1) using Habana® Deep Learning Base AMI (Ubuntu 20.04)
Create the PyTorch docker container running:
Enter the docker image by running:
#### Setup password-less ssh between all connected servers
1. Configure password-less ssh between all nodes:
Do the following in all the nodes' docker sessions:
Copy id_rsa.pub contents from every node's docker to every other node's docker's ~/.ssh/authorized_keys (all public keys need to be in all hosts' authorized_keys):
Copy the contents from inside to other systems.
Paste all hosts' public keys in all hosts' “authorized_keys” file.
2. On each system:
Add all hosts (including itself) to known_hosts. The IP addresses used below are just for illustration:
3. Change Docker SSH port to 3022
Allow all TCP traffic between the nodes on AWS
Clone the git repo:
Create environment:
Install requirements:
Activate environment
## Training params
Learning rate: 1e-3
Batch size: 64
Phase 1 - Epochs: 100
Phase 2 - Epochs: 15
## Train script arguments
## Habana Gaudi - 8 accelerators
### Phase 1 training
### Phase 2 training
## Habana Gaudi - 16 accelerators (multi-server training)
Change the master IP address based on your instances (use local IP, not public IP).
### Phase 1 training
### Phase 2 training
## Other devices
If you don't have access to a Habana Gaudi accelerator yet, you can also train on CPU/GPU, although it will be way slower.
To train on CPU, just pass '--train-device=cpu' and on GPU '--train-device=cuda' to the 'URL' script.
# Inference
## Loading pre-trained model from Hugging Face HUB
## Loading model from local checkpoint
## Generate embeddings
Run the following (after downloading Unplash dataset):
'python3.8 ./generate_embeddings.py'
## Searching images
|
[
"# MultiLingual CLIP\n\nMultilingual CLIP is a pre-trained model which can be used for multilingual semantic search and zero-shot image classification in 100 languages.",
"# Model Architecture\nMultilingual CLIP was built using OpenAI CLIP model. I have used the same Vision encoder (ResNet 50x4), but instead I replaced their text encoder (Transformer) with a Mulilingual Text Encoder (XLM-Roberta) and a configurable number of projection heads, as seen below:\n\n!Model Architecture\n\nThe model was trained in a distributed fashion on 16 Habana Gaudi Accelerators and with mixed Precision in two phases (using COCO Dataset for phase 1 and Google Conceptual Captions for phase 2). The training pipeline was built using PyTorch, PyTorch Lightning, and Distributed Data Parallel.",
"# Datasets\n\nThree datasets have been used for building the model. COCO captions was used for training phase 1 and Google Conceptual Captions was used for training phase 2. Unsplash dataset was used for testing and inference.",
"## COCO Captions\n\nCOCO (Common Objects in Context) is a large-scale object detection, segmentation, and captioning dataset. The COCO captions dataset has around ~85000 images and captions pairs.\n\nRun the following to download the dataset:\n\n\n\nThis dataset was used for the first pre-training phase.",
"## Google Conceptual Captions\n\nConceptual Captions is a dataset consisting of ~3.3 million images annotated with captions. In contrast with the curated style of other image caption annotations, Conceptual Caption images and their raw descriptions are harvested from the web, and therefore represent a wider variety of styles.\n\nDownload the datasets urls/captions from here as save it to 'datasets/googlecc/URL'. The full dataset has over 3 million images, but you can select a subset by loading the 'URL' file and saving only the number of rows you want (I have used 1 million images for training).\n\nThen run the following commands to download each image on the 'URL' file:\n\n\n\nThis dataset was used for the second pre-training phase.",
"## Unplash\n\nThis dataset was used as the test set during inference.\n\nRun 'python3.8 download_unsplash.py' to download the dataset.",
"# Training\n\n!Training phase 1\n\n!Training phase 2",
"## Setup\n\nCreate two Habana instances (AWS EC2 DL1) using Habana® Deep Learning Base AMI (Ubuntu 20.04)\n\n\nCreate the PyTorch docker container running:\n\n\n\nEnter the docker image by running:",
"#### Setup password-less ssh between all connected servers\n\n1. Configure password-less ssh between all nodes:\n\n Do the following in all the nodes' docker sessions:\n \n Copy id_rsa.pub contents from every node's docker to every other node's docker's ~/.ssh/authorized_keys (all public keys need to be in all hosts' authorized_keys):\n \n Copy the contents from inside to other systems.\n Paste all hosts' public keys in all hosts' “authorized_keys” file.\n\n2. On each system:\n Add all hosts (including itself) to known_hosts. The IP addresses used below are just for illustration:\n \n\n3. Change Docker SSH port to 3022\n \n\nAllow all TCP traffic between the nodes on AWS\n\nClone the git repo:\n\n\n\nCreate environment:\n\n\n\nInstall requirements:\n\n\n\nActivate environment",
"## Training params\n\nLearning rate: 1e-3\n\nBatch size: 64\n\nPhase 1 - Epochs: 100\n\nPhase 2 - Epochs: 15",
"## Train script arguments",
"## Habana Gaudi - 8 accelerators",
"### Phase 1 training",
"### Phase 2 training",
"## Habana Gaudi - 16 accelerators (multi-server training)\n\nChange the master IP address based on your instances (use local IP, not public IP).",
"### Phase 1 training",
"### Phase 2 training",
"## Other devices\nIf you don't have access to a Habana Gaudi accelerator yet, you can also train on CPU/GPU, although it will be way slower.\n\nTo train on CPU, just pass '--train-device=cpu' and on GPU '--train-device=cuda' to the 'URL' script.",
"# Inference",
"## Loading pre-trained model from Hugging Face HUB",
"## Loading model from local checkpoint",
"## Generate embeddings\n\nRun the following (after downloading Unplash dataset):\n\n'python3.8 ./generate_embeddings.py'",
"## Searching images"
] |
[
"TAGS\n#clip #vision #text #multilingual #license-mit #has_space #region-us \n",
"# MultiLingual CLIP\n\nMultilingual CLIP is a pre-trained model which can be used for multilingual semantic search and zero-shot image classification in 100 languages.",
"# Model Architecture\nMultilingual CLIP was built using OpenAI CLIP model. I have used the same Vision encoder (ResNet 50x4), but instead I replaced their text encoder (Transformer) with a Mulilingual Text Encoder (XLM-Roberta) and a configurable number of projection heads, as seen below:\n\n!Model Architecture\n\nThe model was trained in a distributed fashion on 16 Habana Gaudi Accelerators and with mixed Precision in two phases (using COCO Dataset for phase 1 and Google Conceptual Captions for phase 2). The training pipeline was built using PyTorch, PyTorch Lightning, and Distributed Data Parallel.",
"# Datasets\n\nThree datasets have been used for building the model. COCO captions was used for training phase 1 and Google Conceptual Captions was used for training phase 2. Unsplash dataset was used for testing and inference.",
"## COCO Captions\n\nCOCO (Common Objects in Context) is a large-scale object detection, segmentation, and captioning dataset. The COCO captions dataset has around ~85000 images and captions pairs.\n\nRun the following to download the dataset:\n\n\n\nThis dataset was used for the first pre-training phase.",
"## Google Conceptual Captions\n\nConceptual Captions is a dataset consisting of ~3.3 million images annotated with captions. In contrast with the curated style of other image caption annotations, Conceptual Caption images and their raw descriptions are harvested from the web, and therefore represent a wider variety of styles.\n\nDownload the datasets urls/captions from here as save it to 'datasets/googlecc/URL'. The full dataset has over 3 million images, but you can select a subset by loading the 'URL' file and saving only the number of rows you want (I have used 1 million images for training).\n\nThen run the following commands to download each image on the 'URL' file:\n\n\n\nThis dataset was used for the second pre-training phase.",
"## Unplash\n\nThis dataset was used as the test set during inference.\n\nRun 'python3.8 download_unsplash.py' to download the dataset.",
"# Training\n\n!Training phase 1\n\n!Training phase 2",
"## Setup\n\nCreate two Habana instances (AWS EC2 DL1) using Habana® Deep Learning Base AMI (Ubuntu 20.04)\n\n\nCreate the PyTorch docker container running:\n\n\n\nEnter the docker image by running:",
"#### Setup password-less ssh between all connected servers\n\n1. Configure password-less ssh between all nodes:\n\n Do the following in all the nodes' docker sessions:\n \n Copy id_rsa.pub contents from every node's docker to every other node's docker's ~/.ssh/authorized_keys (all public keys need to be in all hosts' authorized_keys):\n \n Copy the contents from inside to other systems.\n Paste all hosts' public keys in all hosts' “authorized_keys” file.\n\n2. On each system:\n Add all hosts (including itself) to known_hosts. The IP addresses used below are just for illustration:\n \n\n3. Change Docker SSH port to 3022\n \n\nAllow all TCP traffic between the nodes on AWS\n\nClone the git repo:\n\n\n\nCreate environment:\n\n\n\nInstall requirements:\n\n\n\nActivate environment",
"## Training params\n\nLearning rate: 1e-3\n\nBatch size: 64\n\nPhase 1 - Epochs: 100\n\nPhase 2 - Epochs: 15",
"## Train script arguments",
"## Habana Gaudi - 8 accelerators",
"### Phase 1 training",
"### Phase 2 training",
"## Habana Gaudi - 16 accelerators (multi-server training)\n\nChange the master IP address based on your instances (use local IP, not public IP).",
"### Phase 1 training",
"### Phase 2 training",
"## Other devices\nIf you don't have access to a Habana Gaudi accelerator yet, you can also train on CPU/GPU, although it will be way slower.\n\nTo train on CPU, just pass '--train-device=cpu' and on GPU '--train-device=cuda' to the 'URL' script.",
"# Inference",
"## Loading pre-trained model from Hugging Face HUB",
"## Loading model from local checkpoint",
"## Generate embeddings\n\nRun the following (after downloading Unplash dataset):\n\n'python3.8 ./generate_embeddings.py'",
"## Searching images"
] |
[
25,
42,
157,
51,
76,
176,
35,
12,
50,
203,
31,
5,
9,
5,
5,
34,
5,
5,
79,
4,
13,
7,
36,
4
] |
[
"passage: TAGS\n#clip #vision #text #multilingual #license-mit #has_space #region-us \n# MultiLingual CLIP\n\nMultilingual CLIP is a pre-trained model which can be used for multilingual semantic search and zero-shot image classification in 100 languages.# Model Architecture\nMultilingual CLIP was built using OpenAI CLIP model. I have used the same Vision encoder (ResNet 50x4), but instead I replaced their text encoder (Transformer) with a Mulilingual Text Encoder (XLM-Roberta) and a configurable number of projection heads, as seen below:\n\n!Model Architecture\n\nThe model was trained in a distributed fashion on 16 Habana Gaudi Accelerators and with mixed Precision in two phases (using COCO Dataset for phase 1 and Google Conceptual Captions for phase 2). The training pipeline was built using PyTorch, PyTorch Lightning, and Distributed Data Parallel.# Datasets\n\nThree datasets have been used for building the model. COCO captions was used for training phase 1 and Google Conceptual Captions was used for training phase 2. Unsplash dataset was used for testing and inference.## COCO Captions\n\nCOCO (Common Objects in Context) is a large-scale object detection, segmentation, and captioning dataset. The COCO captions dataset has around ~85000 images and captions pairs.\n\nRun the following to download the dataset:\n\n\n\nThis dataset was used for the first pre-training phase.",
"passage: ## Google Conceptual Captions\n\nConceptual Captions is a dataset consisting of ~3.3 million images annotated with captions. In contrast with the curated style of other image caption annotations, Conceptual Caption images and their raw descriptions are harvested from the web, and therefore represent a wider variety of styles.\n\nDownload the datasets urls/captions from here as save it to 'datasets/googlecc/URL'. The full dataset has over 3 million images, but you can select a subset by loading the 'URL' file and saving only the number of rows you want (I have used 1 million images for training).\n\nThen run the following commands to download each image on the 'URL' file:\n\n\n\nThis dataset was used for the second pre-training phase.## Unplash\n\nThis dataset was used as the test set during inference.\n\nRun 'python3.8 download_unsplash.py' to download the dataset.# Training\n\n!Training phase 1\n\n!Training phase 2## Setup\n\nCreate two Habana instances (AWS EC2 DL1) using Habana® Deep Learning Base AMI (Ubuntu 20.04)\n\n\nCreate the PyTorch docker container running:\n\n\n\nEnter the docker image by running:#### Setup password-less ssh between all connected servers\n\n1. Configure password-less ssh between all nodes:\n\n Do the following in all the nodes' docker sessions:\n \n Copy id_rsa.pub contents from every node's docker to every other node's docker's ~/.ssh/authorized_keys (all public keys need to be in all hosts' authorized_keys):\n \n Copy the contents from inside to other systems.\n Paste all hosts' public keys in all hosts' “authorized_keys” file.\n\n2. On each system:\n Add all hosts (including itself) to known_hosts. The IP addresses used below are just for illustration:\n \n\n3. Change Docker SSH port to 3022\n \n\nAllow all TCP traffic between the nodes on AWS\n\nClone the git repo:\n\n\n\nCreate environment:\n\n\n\nInstall requirements:\n\n\n\nActivate environment## Training params\n\nLearning rate: 1e-3\n\nBatch size: 64\n\nPhase 1 - Epochs: 100\n\nPhase 2 - Epochs: 15## Train script arguments## Habana Gaudi - 8 accelerators### Phase 1 training### Phase 2 training## Habana Gaudi - 16 accelerators (multi-server training)\n\nChange the master IP address based on your instances (use local IP, not public IP).### Phase 1 training### Phase 2 training## Other devices\nIf you don't have access to a Habana Gaudi accelerator yet, you can also train on CPU/GPU, although it will be way slower.\n\nTo train on CPU, just pass '--train-device=cpu' and on GPU '--train-device=cuda' to the 'URL' script.# Inference## Loading pre-trained model from Hugging Face HUB## Loading model from local checkpoint"
] |
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] |
null | null |
transformers
|
hello
|
{}
|
text-generation
|
ha-mulan/moby-dick
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
hello
|
[] |
[
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
[
50
] |
[
"passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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null | null |
transformers
|
<!-- 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. -->
# egy-slang-model
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.9273
- Wer: 1.0000
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.64 | 200 | 2.9735 | 1.0 |
| 3.8098 | 3.28 | 400 | 2.9765 | 1.0 |
| 3.8098 | 4.91 | 600 | 2.9662 | 1.0 |
| 2.9531 | 6.56 | 800 | 2.9708 | 1.0 |
| 2.9531 | 8.2 | 1000 | 2.9673 | 1.0 |
| 2.9259 | 9.83 | 1200 | 2.9989 | 1.0 |
| 2.9259 | 11.47 | 1400 | 2.9889 | 1.0 |
| 2.9023 | 13.11 | 1600 | 2.9739 | 1.0 |
| 2.9023 | 14.75 | 1800 | 3.0040 | 1.0000 |
| 2.8832 | 16.39 | 2000 | 3.0170 | 1.0 |
| 2.8832 | 18.03 | 2200 | 2.9963 | 0.9999 |
| 2.8691 | 19.67 | 2400 | 2.9273 | 1.0000 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.1
- Datasets 1.13.3
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "egy-slang-model", "results": []}]}
|
automatic-speech-recognition
|
habiba/egy-slang-model
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
egy-slang-model
===============
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 2.9273
* Wer: 1.0000
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.001
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 20
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.11.3
* Pytorch 1.10.1
* Datasets 1.13.3
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 20\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.10.1\n* Datasets 1.13.3\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 20\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.10.1\n* Datasets 1.13.3\n* Tokenizers 0.10.3"
] |
[
60,
158,
4,
30
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #has_space #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 20\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.10.1\n* Datasets 1.13.3\n* Tokenizers 0.10.3"
] |
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] |
null | null |
transformers
|
This is a test!
|
{}
|
fill-mask
|
hackertec/dummy2
|
[
"transformers",
"pytorch",
"camembert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #camembert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
|
This is a test!
|
[] |
[
"TAGS\n#transformers #pytorch #camembert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] |
[
38
] |
[
"passage: TAGS\n#transformers #pytorch #camembert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-bne-finetuned-amazon_reviews_multi-taller
This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/roberta-base-bne) on the amazon_reviews_multi dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2463
- Accuracy: 0.9113
## 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
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2474 | 1.0 | 125 | 0.2463 | 0.9113 |
### Framework versions
- Transformers 4.9.2
- Pytorch 1.9.0+cu102
- Datasets 1.11.0
- Tokenizers 0.10.3
|
{"license": "cc-by-4.0", "tags": ["generated_from_trainer"], "datasets": ["amazon_reviews_multi"], "metrics": ["accuracy"], "model_index": [{"name": "roberta-base-bne-finetuned-amazon_reviews_multi-taller", "results": [{"task": {"name": "Text Classification", "type": "text-classification"}, "dataset": {"name": "amazon_reviews_multi", "type": "amazon_reviews_multi", "args": "es"}, "metric": {"name": "Accuracy", "type": "accuracy", "value": 0.91125}}]}]}
|
text-classification
|
hackertec/roberta-base-bne-finetuned-amazon_reviews_multi-taller
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:amazon_reviews_multi",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #dataset-amazon_reviews_multi #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
roberta-base-bne-finetuned-amazon\_reviews\_multi-taller
========================================================
This model is a fine-tuned version of BSC-TeMU/roberta-base-bne on the amazon\_reviews\_multi dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2463
* Accuracy: 0.9113
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
### Training results
### Framework versions
* Transformers 4.9.2
* Pytorch 1.9.0+cu102
* Datasets 1.11.0
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.9.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.11.0\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #dataset-amazon_reviews_multi #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.9.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.11.0\n* Tokenizers 0.10.3"
] |
[
68,
98,
4,
34
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #dataset-amazon_reviews_multi #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1### Training results### Framework versions\n\n\n* Transformers 4.9.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.11.0\n* Tokenizers 0.10.3"
] |
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null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-bne-finetuned-amazon_reviews_multi
This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/roberta-base-bne) on the amazon_reviews_multi dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2557
- Accuracy: 0.9085
## 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
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2296 | 1.0 | 125 | 0.2557 | 0.9085 |
### Framework versions
- Transformers 4.9.2
- Pytorch 1.9.0+cu102
- Datasets 1.11.0
- Tokenizers 0.10.3
|
{"license": "cc-by-4.0", "tags": ["generated_from_trainer"], "datasets": ["amazon_reviews_multi"], "metrics": ["accuracy"], "model_index": [{"name": "roberta-base-bne-finetuned-amazon_reviews_multi", "results": [{"task": {"name": "Text Classification", "type": "text-classification"}, "dataset": {"name": "amazon_reviews_multi", "type": "amazon_reviews_multi", "args": "es"}, "metric": {"name": "Accuracy", "type": "accuracy", "value": 0.9085}}]}]}
|
text-classification
|
hackertec/roberta-base-bne-finetuned-amazon_reviews_multi
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:amazon_reviews_multi",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #dataset-amazon_reviews_multi #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
roberta-base-bne-finetuned-amazon\_reviews\_multi
=================================================
This model is a fine-tuned version of BSC-TeMU/roberta-base-bne on the amazon\_reviews\_multi dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2557
* Accuracy: 0.9085
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
### Training results
### Framework versions
* Transformers 4.9.2
* Pytorch 1.9.0+cu102
* Datasets 1.11.0
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.9.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.11.0\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #dataset-amazon_reviews_multi #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.9.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.11.0\n* Tokenizers 0.10.3"
] |
[
68,
98,
4,
34
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #dataset-amazon_reviews_multi #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1### Training results### Framework versions\n\n\n* Transformers 4.9.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.11.0\n* Tokenizers 0.10.3"
] |
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] |
null | null | null |
# Test
|
{"license": "afl-3.0", "tags": ["es", "bert"], "pipeline_tag": "text-classification", "widget": [{"text": "Mi nombre es Omar", "exdample_title": "Example 1"}, {"text": "Otra prueba", "example_title": "Test"}]}
|
text-classification
|
hackertec9/test
|
[
"es",
"bert",
"text-classification",
"license:afl-3.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#es #bert #text-classification #license-afl-3.0 #region-us
|
# Test
|
[
"# Test"
] |
[
"TAGS\n#es #bert #text-classification #license-afl-3.0 #region-us \n",
"# Test"
] |
[
23,
2
] |
[
"passage: TAGS\n#es #bert #text-classification #license-afl-3.0 #region-us \n# Test"
] |
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null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) 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: 32
- 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: 1
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.13.3
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-demo-colab", "results": []}]}
|
automatic-speech-recognition
|
hady/wav2vec2-base-timit-demo-colab
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-base-timit-demo-colab
This model is a fine-tuned version of facebook/wav2vec2-base 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: 32
- 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: 1
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.13.3
- Tokenizers 0.10.3
|
[
"# wav2vec2-base-timit-demo-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0001\n- train_batch_size: 32\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 1000\n- num_epochs: 1\n- mixed_precision_training: Native AMP",
"### Training results",
"### Framework versions\n\n- Transformers 4.11.3\n- Pytorch 1.10.0+cu111\n- Datasets 1.13.3\n- Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2-base-timit-demo-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0001\n- train_batch_size: 32\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 1000\n- num_epochs: 1\n- mixed_precision_training: Native AMP",
"### Training results",
"### Framework versions\n\n- Transformers 4.11.3\n- Pytorch 1.10.0+cu111\n- Datasets 1.13.3\n- Tokenizers 0.10.3"
] |
[
56,
39,
6,
12,
8,
3,
117,
4,
33
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n# wav2vec2-base-timit-demo-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0001\n- train_batch_size: 32\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 1000\n- num_epochs: 1\n- mixed_precision_training: Native AMP### Training results### Framework versions\n\n- Transformers 4.11.3\n- Pytorch 1.10.0+cu111\n- Datasets 1.13.3\n- Tokenizers 0.10.3"
] |
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] |
null | null |
transformers
|
Github: https://github.com/haisongzhang/roberta-tiny-cased
|
{}
|
feature-extraction
|
haisongzhang/roberta-tiny-cased
|
[
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"feature-extraction",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tf #jax #bert #feature-extraction #endpoints_compatible #has_space #region-us
|
Github: URL
|
[] |
[
"TAGS\n#transformers #pytorch #tf #jax #bert #feature-extraction #endpoints_compatible #has_space #region-us \n"
] |
[
39
] |
[
"passage: TAGS\n#transformers #pytorch #tf #jax #bert #feature-extraction #endpoints_compatible #has_space #region-us \n"
] |
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] |
null | null |
transformers
|
<!-- 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. -->
# bertweet-base-SNS_BRANDS_100k
This model is a fine-tuned version of [vinai/bertweet-base](https://huggingface.co/vinai/bertweet-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0483
## 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: 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 0.0735 | 1.0 | 2928 | 0.0670 |
| 0.0574 | 2.0 | 5856 | 0.0529 |
| 0.0497 | 3.0 | 8784 | 0.0483 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
{"tags": ["generated_from_trainer"], "model-index": [{"name": "bertweet-base-SNS_BRANDS_100k", "results": []}]}
|
fill-mask
|
haji2438/bertweet-base-SNS_BRANDS_100k
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
bertweet-base-SNS\_BRANDS\_100k
===============================
This model is a fine-tuned version of vinai/bertweet-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0483
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: 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
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.0+cu111
* Datasets 1.17.0
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3"
] |
[
48,
98,
4,
33
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0### Training results### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3"
] |
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] |
null | null |
transformers
|
<!-- 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. -->
# bertweet-base-SNS_BRANDS_200k
This model is a fine-tuned version of [vinai/bertweet-base](https://huggingface.co/vinai/bertweet-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0243
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.0428 | 1.0 | 5882 | 0.0336 |
| 0.0276 | 2.0 | 11764 | 0.0241 |
| 0.0251 | 3.0 | 17646 | 0.0243 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.0
- Tokenizers 0.10.3
|
{"tags": ["generated_from_trainer"], "model-index": [{"name": "bertweet-base-SNS_BRANDS_200k", "results": []}]}
|
fill-mask
|
haji2438/bertweet-base-SNS_BRANDS_200k
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
bertweet-base-SNS\_BRANDS\_200k
===============================
This model is a fine-tuned version of vinai/bertweet-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0243
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 3
### Training results
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.0+cu111
* Datasets 1.18.0
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.18.0\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.18.0\n* Tokenizers 0.10.3"
] |
[
48,
116,
4,
35
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 3### Training results### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.18.0\n* Tokenizers 0.10.3"
] |
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null | null |
transformers
|
<!-- 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. -->
# bertweet-base-SNS_BRANDS_50k
This model is a fine-tuned version of [vinai/bertweet-base](https://huggingface.co/vinai/bertweet-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0490
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.0787 | 1.0 | 1465 | 0.0751 |
| 0.0662 | 2.0 | 2930 | 0.0628 |
| 0.053 | 3.0 | 4395 | 0.0531 |
| 0.0452 | 4.0 | 5860 | 0.0490 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.0
- Tokenizers 0.10.3
|
{"tags": ["generated_from_trainer"], "model-index": [{"name": "bertweet-base-SNS_BRANDS_50k", "results": []}]}
|
fill-mask
|
haji2438/bertweet-base-SNS_BRANDS_50k
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
bertweet-base-SNS\_BRANDS\_50k
==============================
This model is a fine-tuned version of vinai/bertweet-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0490
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 4
### Training results
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.0+cu111
* Datasets 1.18.0
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 4",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.18.0\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 4",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.18.0\n* Tokenizers 0.10.3"
] |
[
48,
116,
4,
35
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 4### Training results### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.18.0\n* Tokenizers 0.10.3"
] |
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null | null |
transformers
|
<!-- 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. -->
# bertweet-base-finetuned-IGtext
This model is a fine-tuned version of [vinai/bertweet-base](https://huggingface.co/vinai/bertweet-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0334
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 2.6741 | 1.0 | 505 | 2.2096 |
| 2.3183 | 2.0 | 1010 | 2.0934 |
| 2.2089 | 3.0 | 1515 | 2.0595 |
| 2.1473 | 4.0 | 2020 | 2.0246 |
### Framework versions
- Transformers 4.12.3
- Pytorch 1.10.0+cu111
- Datasets 1.15.1
- Tokenizers 0.10.3
|
{"tags": ["generated_from_trainer"], "model-index": [{"name": "bertweet-base-finetuned-IGtext", "results": []}]}
|
fill-mask
|
haji2438/bertweet-base-finetuned-IGtext
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
bertweet-base-finetuned-IGtext
==============================
This model is a fine-tuned version of vinai/bertweet-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 2.0334
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 32
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 4
### Training results
### Framework versions
* Transformers 4.12.3
* Pytorch 1.10.0+cu111
* Datasets 1.15.1
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.12.3\n* Pytorch 1.10.0+cu111\n* Datasets 1.15.1\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.12.3\n* Pytorch 1.10.0+cu111\n* Datasets 1.15.1\n* Tokenizers 0.10.3"
] |
[
48,
98,
4,
33
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4### Training results### Framework versions\n\n\n* Transformers 4.12.3\n* Pytorch 1.10.0+cu111\n* Datasets 1.15.1\n* Tokenizers 0.10.3"
] |
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] |
null | null |
transformers
|
<!-- 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. -->
# bertweet-base-finetuned-SNS-brand-personality
This model is a fine-tuned version of [vinai/bertweet-base](https://huggingface.co/vinai/bertweet-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0498
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.0757 | 1.0 | 1549 | 0.0723 |
| 0.0605 | 2.0 | 3098 | 0.0573 |
| 0.0498 | 3.0 | 4647 | 0.0498 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
{"tags": ["generated_from_trainer"], "model-index": [{"name": "bertweet-base-finetuned-SNS-brand-personality", "results": []}]}
|
fill-mask
|
haji2438/bertweet-base-finetuned-SNS-brand-personality
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
bertweet-base-finetuned-SNS-brand-personality
=============================================
This model is a fine-tuned version of vinai/bertweet-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0498
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3.0
### Training results
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.0+cu111
* Datasets 1.17.0
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3"
] |
[
48,
98,
4,
33
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0### Training results### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3"
] |
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] |
null | null |
transformers
|
# XLNet-japanese
## Model description
This model require Mecab and senetencepiece with XLNetTokenizer.
See details https://qiita.com/mkt3/items/4d0ae36f3f212aee8002
This model uses NFKD as the normalization method for character encoding.
Japanese muddle marks and semi-muddle marks will be lost.
*日本語の濁点・半濁点がないモデルです*
#### How to use
```python
from fugashi import Tagger
from transformers import (
pipeline,
XLNetLMHeadModel,
XLNetTokenizer
)
class XLNet():
def __init__(self):
self.m = Tagger('-Owakati')
self.gen_model = XLNetLMHeadModel.from_pretrained("hajime9652/xlnet-japanese")
self.gen_tokenizer = XLNetTokenizer.from_pretrained("hajime9652/xlnet-japanese")
def generate(self, prompt="福岡のご飯は美味しい。コンパクトで暮らしやすい街。"):
prompt = self.m.parse(prompt)
inputs = self.gen_tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt")
prompt_length = len(self.gen_tokenizer.decode(inputs[0], skip_special_tokens=True, clean_up_tokenization_spaces=True))
outputs = self.gen_model.generate(inputs, max_length=200, do_sample=True, top_p=0.95, top_k=60)
generated = prompt + self.gen_tokenizer.decode(outputs[0])[prompt_length:]
return generated
```
#### Limitations and bias
This model's training use the Japanese Business News.
# Important matter
The company that created and published this model is called Stockmark.
This repository is for use by HuggingFace and not for infringement.
See this documents https://qiita.com/mkt3/items/4d0ae36f3f212aee8002
published by https://github.com/mkt3
|
{"language": ["ja"], "license": ["apache-2.0"], "tags": ["xlnet", "lm-head", "causal-lm"], "datasets": ["Japanese_Business_News"]}
|
text-generation
|
hajime9652/xlnet-japanese
|
[
"transformers",
"pytorch",
"xlnet",
"text-generation",
"lm-head",
"causal-lm",
"ja",
"dataset:Japanese_Business_News",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"ja"
] |
TAGS
#transformers #pytorch #xlnet #text-generation #lm-head #causal-lm #ja #dataset-Japanese_Business_News #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# XLNet-japanese
## Model description
This model require Mecab and senetencepiece with XLNetTokenizer.
See details URL
This model uses NFKD as the normalization method for character encoding.
Japanese muddle marks and semi-muddle marks will be lost.
*日本語の濁点・半濁点がないモデルです*
#### How to use
#### Limitations and bias
This model's training use the Japanese Business News.
# Important matter
The company that created and published this model is called Stockmark.
This repository is for use by HuggingFace and not for infringement.
See this documents URL
published by URL
|
[
"# XLNet-japanese",
"## Model description\nThis model require Mecab and senetencepiece with XLNetTokenizer.\nSee details URL\n\nThis model uses NFKD as the normalization method for character encoding.\nJapanese muddle marks and semi-muddle marks will be lost.\n\n*日本語の濁点・半濁点がないモデルです*",
"#### How to use",
"#### Limitations and bias\nThis model's training use the Japanese Business News.",
"# Important matter\nThe company that created and published this model is called Stockmark.\nThis repository is for use by HuggingFace and not for infringement.\nSee this documents URL\npublished by URL"
] |
[
"TAGS\n#transformers #pytorch #xlnet #text-generation #lm-head #causal-lm #ja #dataset-Japanese_Business_News #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# XLNet-japanese",
"## Model description\nThis model require Mecab and senetencepiece with XLNetTokenizer.\nSee details URL\n\nThis model uses NFKD as the normalization method for character encoding.\nJapanese muddle marks and semi-muddle marks will be lost.\n\n*日本語の濁点・半濁点がないモデルです*",
"#### How to use",
"#### Limitations and bias\nThis model's training use the Japanese Business News.",
"# Important matter\nThe company that created and published this model is called Stockmark.\nThis repository is for use by HuggingFace and not for infringement.\nSee this documents URL\npublished by URL"
] |
[
68,
7,
71,
5,
18,
42
] |
[
"passage: TAGS\n#transformers #pytorch #xlnet #text-generation #lm-head #causal-lm #ja #dataset-Japanese_Business_News #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# XLNet-japanese## Model description\nThis model require Mecab and senetencepiece with XLNetTokenizer.\nSee details URL\n\nThis model uses NFKD as the normalization method for character encoding.\nJapanese muddle marks and semi-muddle marks will be lost.\n\n*日本語の濁点・半濁点がないモデルです*#### How to use#### Limitations and bias\nThis model's training use the Japanese Business News.# Important matter\nThe company that created and published this model is called Stockmark.\nThis repository is for use by HuggingFace and not for infringement.\nSee this documents URL\npublished by URL"
] |
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] |
null | null |
transformers
|
This model has been initialized with random values. It is supposed to be used for the purpose of debugging.
|
{}
|
text-generation
|
hakurei/gpt-j-random-tinier
|
[
"transformers",
"pytorch",
"gptj",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #gptj #text-generation #autotrain_compatible #endpoints_compatible #region-us
|
This model has been initialized with random values. It is supposed to be used for the purpose of debugging.
|
[] |
[
"TAGS\n#transformers #pytorch #gptj #text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
[
38
] |
[
"passage: TAGS\n#transformers #pytorch #gptj #text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
# Lit-125M - A Small Fine-tuned Model For Fictional Storytelling
Lit-125M is a GPT-Neo 125M model fine-tuned on 2GB of a diverse range of light novels, erotica, and annotated literature for the purpose of generating novel-like fictional text.
## Model Description
The model used for fine-tuning is [GPT-Neo 125M](https://huggingface.co/EleutherAI/gpt-neo-125M), which is a 125 million parameter auto-regressive language model trained on [The Pile](https://pile.eleuther.ai/)..
## Training Data & Annotative Prompting
The data used in fine-tuning has been gathered from various sources such as the [Gutenberg Project](https://www.gutenberg.org/). The annotated fiction dataset has prepended tags to assist in generating towards a particular style. Here is an example prompt that shows how to use the annotations.
```
[ Title: The Dunwich Horror; Author: H. P. Lovecraft; Genre: Horror; Tags: 3rdperson, scary; Style: Dark ]
***
When a traveler in north central Massachusetts takes the wrong fork...
```
The annotations can be mixed and matched to help generate towards a specific style.
## Downstream Uses
This model can be used for entertainment purposes and as a creative writing assistant for fiction writers. The small size of the model can also help for easy debugging or further development of other models with a similar purpose.
## Example Code
```
from transformers import AutoTokenizer, AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained('hakurei/lit-125M')
tokenizer = AutoTokenizer.from_pretrained('hakurei/lit-125M')
prompt = '''[ Title: The Dunwich Horror; Author: H. P. Lovecraft; Genre: Horror ]
***
When a traveler'''
input_ids = tokenizer.encode(prompt, return_tensors='pt')
output = model.generate(input_ids, do_sample=True, temperature=1.0, top_p=0.9, repetition_penalty=1.2, max_length=len(input_ids[0])+100, pad_token_id=tokenizer.eos_token_id)
generated_text = tokenizer.decode(output[0])
print(generated_text)
```
An example output from this code produces a result that will look similar to:
```
[ Title: The Dunwich Horror; Author: H. P. Lovecraft; Genre: Horror ]
***
When a traveler takes a trip through the streets of the world, the traveler feels like a youkai with a whole world inside her mind. It can be very scary for a youkai. When someone goes in the opposite direction and knocks on your door, it is actually the first time you have ever come to investigate something like that.
That's right: everyone has heard stories about youkai, right? If you have heard them, you know what I'm talking about.
It's hard not to say you
```
## Team members and Acknowledgements
- [Anthony Mercurio](https://github.com/harubaru)
- Imperishable_NEET
|
{"language": ["en"], "license": "mit", "tags": ["pytorch", "causal-lm"]}
|
text-generation
|
hakurei/lit-125M
|
[
"transformers",
"pytorch",
"gpt_neo",
"text-generation",
"causal-lm",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #gpt_neo #text-generation #causal-lm #en #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Lit-125M - A Small Fine-tuned Model For Fictional Storytelling
Lit-125M is a GPT-Neo 125M model fine-tuned on 2GB of a diverse range of light novels, erotica, and annotated literature for the purpose of generating novel-like fictional text.
## Model Description
The model used for fine-tuning is GPT-Neo 125M, which is a 125 million parameter auto-regressive language model trained on The Pile..
## Training Data & Annotative Prompting
The data used in fine-tuning has been gathered from various sources such as the Gutenberg Project. The annotated fiction dataset has prepended tags to assist in generating towards a particular style. Here is an example prompt that shows how to use the annotations.
The annotations can be mixed and matched to help generate towards a specific style.
## Downstream Uses
This model can be used for entertainment purposes and as a creative writing assistant for fiction writers. The small size of the model can also help for easy debugging or further development of other models with a similar purpose.
## Example Code
An example output from this code produces a result that will look similar to:
## Team members and Acknowledgements
- Anthony Mercurio
- Imperishable_NEET
|
[
"# Lit-125M - A Small Fine-tuned Model For Fictional Storytelling\n\nLit-125M is a GPT-Neo 125M model fine-tuned on 2GB of a diverse range of light novels, erotica, and annotated literature for the purpose of generating novel-like fictional text.",
"## Model Description\n\nThe model used for fine-tuning is GPT-Neo 125M, which is a 125 million parameter auto-regressive language model trained on The Pile..",
"## Training Data & Annotative Prompting\n\nThe data used in fine-tuning has been gathered from various sources such as the Gutenberg Project. The annotated fiction dataset has prepended tags to assist in generating towards a particular style. Here is an example prompt that shows how to use the annotations.\n\n\n\nThe annotations can be mixed and matched to help generate towards a specific style.",
"## Downstream Uses\n\nThis model can be used for entertainment purposes and as a creative writing assistant for fiction writers. The small size of the model can also help for easy debugging or further development of other models with a similar purpose.",
"## Example Code\n\n\n\nAn example output from this code produces a result that will look similar to:",
"## Team members and Acknowledgements\n\n- Anthony Mercurio\n- Imperishable_NEET"
] |
[
"TAGS\n#transformers #pytorch #gpt_neo #text-generation #causal-lm #en #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Lit-125M - A Small Fine-tuned Model For Fictional Storytelling\n\nLit-125M is a GPT-Neo 125M model fine-tuned on 2GB of a diverse range of light novels, erotica, and annotated literature for the purpose of generating novel-like fictional text.",
"## Model Description\n\nThe model used for fine-tuning is GPT-Neo 125M, which is a 125 million parameter auto-regressive language model trained on The Pile..",
"## Training Data & Annotative Prompting\n\nThe data used in fine-tuning has been gathered from various sources such as the Gutenberg Project. The annotated fiction dataset has prepended tags to assist in generating towards a particular style. Here is an example prompt that shows how to use the annotations.\n\n\n\nThe annotations can be mixed and matched to help generate towards a specific style.",
"## Downstream Uses\n\nThis model can be used for entertainment purposes and as a creative writing assistant for fiction writers. The small size of the model can also help for easy debugging or further development of other models with a similar purpose.",
"## Example Code\n\n\n\nAn example output from this code produces a result that will look similar to:",
"## Team members and Acknowledgements\n\n- Anthony Mercurio\n- Imperishable_NEET"
] |
[
56,
70,
41,
89,
51,
20,
19
] |
[
"passage: TAGS\n#transformers #pytorch #gpt_neo #text-generation #causal-lm #en #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n# Lit-125M - A Small Fine-tuned Model For Fictional Storytelling\n\nLit-125M is a GPT-Neo 125M model fine-tuned on 2GB of a diverse range of light novels, erotica, and annotated literature for the purpose of generating novel-like fictional text.## Model Description\n\nThe model used for fine-tuning is GPT-Neo 125M, which is a 125 million parameter auto-regressive language model trained on The Pile..## Training Data & Annotative Prompting\n\nThe data used in fine-tuning has been gathered from various sources such as the Gutenberg Project. The annotated fiction dataset has prepended tags to assist in generating towards a particular style. Here is an example prompt that shows how to use the annotations.\n\n\n\nThe annotations can be mixed and matched to help generate towards a specific style.## Downstream Uses\n\nThis model can be used for entertainment purposes and as a creative writing assistant for fiction writers. The small size of the model can also help for easy debugging or further development of other models with a similar purpose.## Example Code\n\n\n\nAn example output from this code produces a result that will look similar to:## Team members and Acknowledgements\n\n- Anthony Mercurio\n- Imperishable_NEET"
] |
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] |
null | null |
transformers
|
# Lit-6B - A Large Fine-tuned Model For Fictional Storytelling
Lit-6B is a GPT-J 6B model fine-tuned on 2GB of a diverse range of light novels, erotica, and annotated literature for the purpose of generating novel-like fictional text.
## Model Description
The model used for fine-tuning is [GPT-J](https://github.com/kingoflolz/mesh-transformer-jax), which is a 6 billion parameter auto-regressive language model trained on [The Pile](https://pile.eleuther.ai/).
## Training Data & Annotative Prompting
The data used in fine-tuning has been gathered from various sources such as the [Gutenberg Project](https://www.gutenberg.org/). The annotated fiction dataset has prepended tags to assist in generating towards a particular style. Here is an example prompt that shows how to use the annotations.
```
[ Title: The Dunwich Horror; Author: H. P. Lovecraft; Genre: Horror; Tags: 3rdperson, scary; Style: Dark ]
***
When a traveler in north central Massachusetts takes the wrong fork...
```
The annotations can be mixed and matched to help generate towards a specific style.
## Downstream Uses
This model can be used for entertainment purposes and as a creative writing assistant for fiction writers.
## Example Code
```
from transformers import AutoTokenizer, AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained('hakurei/lit-6B')
tokenizer = AutoTokenizer.from_pretrained('hakurei/lit-6B')
prompt = '''[ Title: The Dunwich Horror; Author: H. P. Lovecraft; Genre: Horror ]
***
When a traveler'''
input_ids = tokenizer.encode(prompt, return_tensors='pt')
output = model.generate(input_ids, do_sample=True, temperature=1.0, top_p=0.9, repetition_penalty=1.2, max_length=len(input_ids[0])+100, pad_token_id=tokenizer.eos_token_id)
generated_text = tokenizer.decode(output[0])
print(generated_text)
```
An example output from this code produces a result that will look similar to:
```
[ Title: The Dunwich Horror; Author: H. P. Lovecraft; Genre: Horror ]
***
When a traveler comes to an unknown region, his thoughts turn inevitably towards the old gods and legends which cluster around its appearance. It is not that he believes in them or suspects their reality—but merely because they are present somewhere else in creation just as truly as himself, and so belong of necessity in any landscape whose features cannot be altogether strange to him. Moreover, man has been prone from ancient times to brood over those things most connected with the places where he dwells. Thus the Olympian deities who ruled Hyper
```
## Team members and Acknowledgements
This project would not have been possible without the computational resources graciously provided by the [TPU Research Cloud](https://sites.research.google/trc/)
- [Anthony Mercurio](https://github.com/harubaru)
- Imperishable_NEET
|
{"language": ["en"], "license": "mit", "tags": ["pytorch", "causal-lm"]}
| null |
hakurei/lit-6B-8bit
|
[
"transformers",
"pytorch",
"causal-lm",
"en",
"license:mit",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #causal-lm #en #license-mit #endpoints_compatible #region-us
|
# Lit-6B - A Large Fine-tuned Model For Fictional Storytelling
Lit-6B is a GPT-J 6B model fine-tuned on 2GB of a diverse range of light novels, erotica, and annotated literature for the purpose of generating novel-like fictional text.
## Model Description
The model used for fine-tuning is GPT-J, which is a 6 billion parameter auto-regressive language model trained on The Pile.
## Training Data & Annotative Prompting
The data used in fine-tuning has been gathered from various sources such as the Gutenberg Project. The annotated fiction dataset has prepended tags to assist in generating towards a particular style. Here is an example prompt that shows how to use the annotations.
The annotations can be mixed and matched to help generate towards a specific style.
## Downstream Uses
This model can be used for entertainment purposes and as a creative writing assistant for fiction writers.
## Example Code
An example output from this code produces a result that will look similar to:
## Team members and Acknowledgements
This project would not have been possible without the computational resources graciously provided by the TPU Research Cloud
- Anthony Mercurio
- Imperishable_NEET
|
[
"# Lit-6B - A Large Fine-tuned Model For Fictional Storytelling\n\nLit-6B is a GPT-J 6B model fine-tuned on 2GB of a diverse range of light novels, erotica, and annotated literature for the purpose of generating novel-like fictional text.",
"## Model Description\n\nThe model used for fine-tuning is GPT-J, which is a 6 billion parameter auto-regressive language model trained on The Pile.",
"## Training Data & Annotative Prompting\n\nThe data used in fine-tuning has been gathered from various sources such as the Gutenberg Project. The annotated fiction dataset has prepended tags to assist in generating towards a particular style. Here is an example prompt that shows how to use the annotations.\n\n\n\nThe annotations can be mixed and matched to help generate towards a specific style.",
"## Downstream Uses\n\nThis model can be used for entertainment purposes and as a creative writing assistant for fiction writers.",
"## Example Code\n\n\n\nAn example output from this code produces a result that will look similar to:",
"## Team members and Acknowledgements\n\nThis project would not have been possible without the computational resources graciously provided by the TPU Research Cloud\n\n- Anthony Mercurio\n- Imperishable_NEET"
] |
[
"TAGS\n#transformers #pytorch #causal-lm #en #license-mit #endpoints_compatible #region-us \n",
"# Lit-6B - A Large Fine-tuned Model For Fictional Storytelling\n\nLit-6B is a GPT-J 6B model fine-tuned on 2GB of a diverse range of light novels, erotica, and annotated literature for the purpose of generating novel-like fictional text.",
"## Model Description\n\nThe model used for fine-tuning is GPT-J, which is a 6 billion parameter auto-regressive language model trained on The Pile.",
"## Training Data & Annotative Prompting\n\nThe data used in fine-tuning has been gathered from various sources such as the Gutenberg Project. The annotated fiction dataset has prepended tags to assist in generating towards a particular style. Here is an example prompt that shows how to use the annotations.\n\n\n\nThe annotations can be mixed and matched to help generate towards a specific style.",
"## Downstream Uses\n\nThis model can be used for entertainment purposes and as a creative writing assistant for fiction writers.",
"## Example Code\n\n\n\nAn example output from this code produces a result that will look similar to:",
"## Team members and Acknowledgements\n\nThis project would not have been possible without the computational resources graciously provided by the TPU Research Cloud\n\n- Anthony Mercurio\n- Imperishable_NEET"
] |
[
34,
67,
37,
89,
26,
20,
41
] |
[
"passage: TAGS\n#transformers #pytorch #causal-lm #en #license-mit #endpoints_compatible #region-us \n# Lit-6B - A Large Fine-tuned Model For Fictional Storytelling\n\nLit-6B is a GPT-J 6B model fine-tuned on 2GB of a diverse range of light novels, erotica, and annotated literature for the purpose of generating novel-like fictional text.## Model Description\n\nThe model used for fine-tuning is GPT-J, which is a 6 billion parameter auto-regressive language model trained on The Pile.## Training Data & Annotative Prompting\n\nThe data used in fine-tuning has been gathered from various sources such as the Gutenberg Project. The annotated fiction dataset has prepended tags to assist in generating towards a particular style. Here is an example prompt that shows how to use the annotations.\n\n\n\nThe annotations can be mixed and matched to help generate towards a specific style.## Downstream Uses\n\nThis model can be used for entertainment purposes and as a creative writing assistant for fiction writers.## Example Code\n\n\n\nAn example output from this code produces a result that will look similar to:## Team members and Acknowledgements\n\nThis project would not have been possible without the computational resources graciously provided by the TPU Research Cloud\n\n- Anthony Mercurio\n- Imperishable_NEET"
] |
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null | null |
transformers
|
# Lit-6B - A Large Fine-tuned Model For Fictional Storytelling
Lit-6B is a GPT-J 6B model fine-tuned on 2GB of a diverse range of light novels, erotica, and annotated literature for the purpose of generating novel-like fictional text.
## Model Description
The model used for fine-tuning is [GPT-J](https://github.com/kingoflolz/mesh-transformer-jax), which is a 6 billion parameter auto-regressive language model trained on [The Pile](https://pile.eleuther.ai/).
## Training Data & Annotative Prompting
The data used in fine-tuning has been gathered from various sources such as the [Gutenberg Project](https://www.gutenberg.org/). The annotated fiction dataset has prepended tags to assist in generating towards a particular style. Here is an example prompt that shows how to use the annotations.
```
[ Title: The Dunwich Horror; Author: H. P. Lovecraft; Genre: Horror; Tags: 3rdperson, scary; Style: Dark ]
***
When a traveler in north central Massachusetts takes the wrong fork...
```
The annotations can be mixed and matched to help generate towards a specific style.
## Downstream Uses
This model can be used for entertainment purposes and as a creative writing assistant for fiction writers.
## Example Code
```
from transformers import AutoTokenizer, AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained('hakurei/lit-6B')
tokenizer = AutoTokenizer.from_pretrained('hakurei/lit-6B')
prompt = '''[ Title: The Dunwich Horror; Author: H. P. Lovecraft; Genre: Horror ]
***
When a traveler'''
input_ids = tokenizer.encode(prompt, return_tensors='pt')
output = model.generate(input_ids, do_sample=True, temperature=1.0, top_p=0.9, repetition_penalty=1.2, max_length=len(input_ids[0])+100, pad_token_id=tokenizer.eos_token_id)
generated_text = tokenizer.decode(output[0])
print(generated_text)
```
An example output from this code produces a result that will look similar to:
```
[ Title: The Dunwich Horror; Author: H. P. Lovecraft; Genre: Horror ]
***
When a traveler comes to an unknown region, his thoughts turn inevitably towards the old gods and legends which cluster around its appearance. It is not that he believes in them or suspects their reality—but merely because they are present somewhere else in creation just as truly as himself, and so belong of necessity in any landscape whose features cannot be altogether strange to him. Moreover, man has been prone from ancient times to brood over those things most connected with the places where he dwells. Thus the Olympian deities who ruled Hyper
```
## Team members and Acknowledgements
This project would not have been possible without the computational resources graciously provided by the [TPU Research Cloud](https://sites.research.google/trc/)
- [Anthony Mercurio](https://github.com/harubaru)
- Imperishable_NEET
|
{"language": ["en"], "license": "mit", "tags": ["pytorch", "causal-lm"]}
|
text-generation
|
hakurei/lit-6B
|
[
"transformers",
"pytorch",
"gptj",
"text-generation",
"causal-lm",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #gptj #text-generation #causal-lm #en #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Lit-6B - A Large Fine-tuned Model For Fictional Storytelling
Lit-6B is a GPT-J 6B model fine-tuned on 2GB of a diverse range of light novels, erotica, and annotated literature for the purpose of generating novel-like fictional text.
## Model Description
The model used for fine-tuning is GPT-J, which is a 6 billion parameter auto-regressive language model trained on The Pile.
## Training Data & Annotative Prompting
The data used in fine-tuning has been gathered from various sources such as the Gutenberg Project. The annotated fiction dataset has prepended tags to assist in generating towards a particular style. Here is an example prompt that shows how to use the annotations.
The annotations can be mixed and matched to help generate towards a specific style.
## Downstream Uses
This model can be used for entertainment purposes and as a creative writing assistant for fiction writers.
## Example Code
An example output from this code produces a result that will look similar to:
## Team members and Acknowledgements
This project would not have been possible without the computational resources graciously provided by the TPU Research Cloud
- Anthony Mercurio
- Imperishable_NEET
|
[
"# Lit-6B - A Large Fine-tuned Model For Fictional Storytelling\n\nLit-6B is a GPT-J 6B model fine-tuned on 2GB of a diverse range of light novels, erotica, and annotated literature for the purpose of generating novel-like fictional text.",
"## Model Description\n\nThe model used for fine-tuning is GPT-J, which is a 6 billion parameter auto-regressive language model trained on The Pile.",
"## Training Data & Annotative Prompting\n\nThe data used in fine-tuning has been gathered from various sources such as the Gutenberg Project. The annotated fiction dataset has prepended tags to assist in generating towards a particular style. Here is an example prompt that shows how to use the annotations.\n\n\n\nThe annotations can be mixed and matched to help generate towards a specific style.",
"## Downstream Uses\n\nThis model can be used for entertainment purposes and as a creative writing assistant for fiction writers.",
"## Example Code\n\n\n\nAn example output from this code produces a result that will look similar to:",
"## Team members and Acknowledgements\n\nThis project would not have been possible without the computational resources graciously provided by the TPU Research Cloud\n\n- Anthony Mercurio\n- Imperishable_NEET"
] |
[
"TAGS\n#transformers #pytorch #gptj #text-generation #causal-lm #en #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Lit-6B - A Large Fine-tuned Model For Fictional Storytelling\n\nLit-6B is a GPT-J 6B model fine-tuned on 2GB of a diverse range of light novels, erotica, and annotated literature for the purpose of generating novel-like fictional text.",
"## Model Description\n\nThe model used for fine-tuning is GPT-J, which is a 6 billion parameter auto-regressive language model trained on The Pile.",
"## Training Data & Annotative Prompting\n\nThe data used in fine-tuning has been gathered from various sources such as the Gutenberg Project. The annotated fiction dataset has prepended tags to assist in generating towards a particular style. Here is an example prompt that shows how to use the annotations.\n\n\n\nThe annotations can be mixed and matched to help generate towards a specific style.",
"## Downstream Uses\n\nThis model can be used for entertainment purposes and as a creative writing assistant for fiction writers.",
"## Example Code\n\n\n\nAn example output from this code produces a result that will look similar to:",
"## Team members and Acknowledgements\n\nThis project would not have been possible without the computational resources graciously provided by the TPU Research Cloud\n\n- Anthony Mercurio\n- Imperishable_NEET"
] |
[
55,
67,
37,
89,
26,
20,
41
] |
[
"passage: TAGS\n#transformers #pytorch #gptj #text-generation #causal-lm #en #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n# Lit-6B - A Large Fine-tuned Model For Fictional Storytelling\n\nLit-6B is a GPT-J 6B model fine-tuned on 2GB of a diverse range of light novels, erotica, and annotated literature for the purpose of generating novel-like fictional text.## Model Description\n\nThe model used for fine-tuning is GPT-J, which is a 6 billion parameter auto-regressive language model trained on The Pile.## Training Data & Annotative Prompting\n\nThe data used in fine-tuning has been gathered from various sources such as the Gutenberg Project. The annotated fiction dataset has prepended tags to assist in generating towards a particular style. Here is an example prompt that shows how to use the annotations.\n\n\n\nThe annotations can be mixed and matched to help generate towards a specific style.## Downstream Uses\n\nThis model can be used for entertainment purposes and as a creative writing assistant for fiction writers.## Example Code\n\n\n\nAn example output from this code produces a result that will look similar to:## Team members and Acknowledgements\n\nThis project would not have been possible without the computational resources graciously provided by the TPU Research Cloud\n\n- Anthony Mercurio\n- Imperishable_NEET"
] |
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] |
null | null |
transformers
|
# DOC DialoGPT Model
|
{"tags": ["conversational"]}
|
text-generation
|
hama/Doctor_Bot
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# DOC DialoGPT Model
|
[
"# DOC DialoGPT Model"
] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# DOC DialoGPT Model"
] |
[
51,
7
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# DOC DialoGPT Model"
] |
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] |
null | null |
transformers
|
# Harry Potter DialoGPT Model
|
{"tags": ["conversational"]}
|
text-generation
|
hama/Harry_Bot
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Harry Potter DialoGPT Model
|
[
"# Harry Potter DialoGPT Model"
] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Harry Potter DialoGPT Model"
] |
[
51,
8
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Harry Potter DialoGPT Model"
] |
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] |
null | null |
transformers
|
# BArney DialoGPT Model
|
{"tags": ["conversational"]}
|
text-generation
|
hama/barney_bot
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# BArney DialoGPT Model
|
[
"# BArney DialoGPT Model"
] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# BArney DialoGPT Model"
] |
[
51,
9
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# BArney DialoGPT Model"
] |
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null | null |
transformers
|
# me 101
|
{"tags": ["conversational"]}
|
text-generation
|
hama/me0.01
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# me 101
|
[
"# me 101"
] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# me 101"
] |
[
51,
3
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# me 101"
] |
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] |
null | null |
transformers
|
# Rick and Morty DialoGPT Model
|
{"tags": ["conversational"]}
|
text-generation
|
hama/rick_bot
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Rick and Morty DialoGPT Model
|
[
"# Rick and Morty DialoGPT Model"
] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Rick and Morty DialoGPT Model"
] |
[
51,
10
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Rick and Morty DialoGPT Model"
] |
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] |
null | null |
transformers
|
# mBart50 for Zeroshot Azerbaijani-Turkish Translation
The mBart50 model is finetuned on English-Azerbaijani-Turkish translation leaving Az<->Tr as zeroshot directions. The method of tied representations is used to enforce alignment between semantically equivalent sentences leading to superior zeroshot performance.
|
{}
|
text2text-generation
|
hamishs/mBART50-en-az-tr1
|
[
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #mbart #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
|
# mBart50 for Zeroshot Azerbaijani-Turkish Translation
The mBart50 model is finetuned on English-Azerbaijani-Turkish translation leaving Az<->Tr as zeroshot directions. The method of tied representations is used to enforce alignment between semantically equivalent sentences leading to superior zeroshot performance.
|
[
"# mBart50 for Zeroshot Azerbaijani-Turkish Translation\nThe mBart50 model is finetuned on English-Azerbaijani-Turkish translation leaving Az<->Tr as zeroshot directions. The method of tied representations is used to enforce alignment between semantically equivalent sentences leading to superior zeroshot performance."
] |
[
"TAGS\n#transformers #pytorch #mbart #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n",
"# mBart50 for Zeroshot Azerbaijani-Turkish Translation\nThe mBart50 model is finetuned on English-Azerbaijani-Turkish translation leaving Az<->Tr as zeroshot directions. The method of tied representations is used to enforce alignment between semantically equivalent sentences leading to superior zeroshot performance."
] |
[
39,
74
] |
[
"passage: TAGS\n#transformers #pytorch #mbart #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n# mBart50 for Zeroshot Azerbaijani-Turkish Translation\nThe mBart50 model is finetuned on English-Azerbaijani-Turkish translation leaving Az<->Tr as zeroshot directions. The method of tied representations is used to enforce alignment between semantically equivalent sentences leading to superior zeroshot performance."
] |
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] |
null | null | null |
hello
|
{}
| null |
hamxxxa/SBert
|
[
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#region-us
|
hello
|
[] |
[
"TAGS\n#region-us \n"
] |
[
6
] |
[
"passage: TAGS\n#region-us \n"
] |
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] |
null | null |
transformers
|
<!-- 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. -->
# electra-small-discriminator-finetuned-squad
This model is a fine-tuned version of [google/electra-small-discriminator](https://huggingface.co/google/electra-small-discriminator) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2174
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.5751 | 1.0 | 2767 | 1.3952 |
| 1.2939 | 2.0 | 5534 | 1.2458 |
| 1.1866 | 3.0 | 8301 | 1.2174 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "electra-small-discriminator-finetuned-squad", "results": []}]}
|
question-answering
|
hankzhong/electra-small-discriminator-finetuned-squad
|
[
"transformers",
"pytorch",
"tensorboard",
"electra",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #electra #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
electra-small-discriminator-finetuned-squad
===========================================
This model is a fine-tuned version of google/electra-small-discriminator on the squad dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2174
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3
### Training results
### Framework versions
* Transformers 4.12.5
* Pytorch 1.10.0+cu111
* Datasets 1.16.1
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.12.5\n* Pytorch 1.10.0+cu111\n* Datasets 1.16.1\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #electra #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.12.5\n* Pytorch 1.10.0+cu111\n* Datasets 1.16.1\n* Tokenizers 0.10.3"
] |
[
55,
98,
4,
33
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #electra #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3### Training results### Framework versions\n\n\n* Transformers 4.12.5\n* Pytorch 1.10.0+cu111\n* Datasets 1.16.1\n* Tokenizers 0.10.3"
] |
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] |
null | null |
transformers
|
## Not yet
|
{}
|
fill-mask
|
hansgun/model_test
|
[
"transformers",
"tf",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #tf #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
|
## Not yet
|
[
"## Not yet"
] |
[
"TAGS\n#transformers #tf #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"## Not yet"
] |
[
35,
3
] |
[
"passage: TAGS\n#transformers #tf #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n## Not yet"
] |
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] |
null | null |
transformers
|
# Helsinki-NLP/opus-mt-en-vi
- This model is a fine-tune checkpoint of [Helsinki-NLP/opus-mt-en-vi](https://huggingface.co/Helsinki-NLP/opus-mt-en-vi).
- This model reaches BLEU score = 33.086 on the test set of IWSLT'15 English-Vietnamese data.
# Fine-tuning hyper-parameters
- learning_rate = 1e-4
- batch_size = 4
- num_train_epochs = 3.0
|
{}
|
text2text-generation
|
haotieu/en-vi-mt-model
|
[
"transformers",
"pytorch",
"marian",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #marian #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Helsinki-NLP/opus-mt-en-vi
- This model is a fine-tune checkpoint of Helsinki-NLP/opus-mt-en-vi.
- This model reaches BLEU score = 33.086 on the test set of IWSLT'15 English-Vietnamese data.
# Fine-tuning hyper-parameters
- learning_rate = 1e-4
- batch_size = 4
- num_train_epochs = 3.0
|
[
"# Helsinki-NLP/opus-mt-en-vi\n- This model is a fine-tune checkpoint of Helsinki-NLP/opus-mt-en-vi.\n- This model reaches BLEU score = 33.086 on the test set of IWSLT'15 English-Vietnamese data.",
"# Fine-tuning hyper-parameters\n- learning_rate = 1e-4\n- batch_size = 4\n- num_train_epochs = 3.0"
] |
[
"TAGS\n#transformers #pytorch #marian #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Helsinki-NLP/opus-mt-en-vi\n- This model is a fine-tune checkpoint of Helsinki-NLP/opus-mt-en-vi.\n- This model reaches BLEU score = 33.086 on the test set of IWSLT'15 English-Vietnamese data.",
"# Fine-tuning hyper-parameters\n- learning_rate = 1e-4\n- batch_size = 4\n- num_train_epochs = 3.0"
] |
[
43,
71,
36
] |
[
"passage: TAGS\n#transformers #pytorch #marian #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us \n# Helsinki-NLP/opus-mt-en-vi\n- This model is a fine-tune checkpoint of Helsinki-NLP/opus-mt-en-vi.\n- This model reaches BLEU score = 33.086 on the test set of IWSLT'15 English-Vietnamese data.# Fine-tuning hyper-parameters\n- learning_rate = 1e-4\n- batch_size = 4\n- num_train_epochs = 3.0"
] |
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null | null |
sentence-transformers
|
# multi-qa-MiniLM-L6-cos-v1
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and was designed for **semantic search**. It has been trained on 215M (question, answer) pairs from diverse sources. For an introduction to semantic search, have a look at: [SBERT.net - Semantic Search](https://www.sbert.net/examples/applications/semantic-search/README.html)
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer, util
query = "How many people live in London?"
docs = ["Around 9 Million people live in London", "London is known for its financial district"]
#Load the model
model = SentenceTransformer('sentence-transformers/multi-qa-MiniLM-L6-cos-v1')
#Encode query and documents
query_emb = model.encode(query)
doc_emb = model.encode(docs)
#Compute dot score between query and all document embeddings
scores = util.dot_score(query_emb, doc_emb)[0].cpu().tolist()
#Combine docs & scores
doc_score_pairs = list(zip(docs, scores))
#Sort by decreasing score
doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True)
#Output passages & scores
for doc, score in doc_score_pairs:
print(score, doc)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the correct pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
import torch.nn.functional as F
#Mean Pooling - Take average of all tokens
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output.last_hidden_state #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
#Encode text
def encode(texts):
# Tokenize sentences
encoded_input = tokenizer(texts, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input, return_dict=True)
# Perform pooling
embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
# Normalize embeddings
embeddings = F.normalize(embeddings, p=2, dim=1)
return embeddings
# Sentences we want sentence embeddings for
query = "How many people live in London?"
docs = ["Around 9 Million people live in London", "London is known for its financial district"]
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/multi-qa-MiniLM-L6-cos-v1")
model = AutoModel.from_pretrained("sentence-transformers/multi-qa-MiniLM-L6-cos-v1")
#Encode query and docs
query_emb = encode(query)
doc_emb = encode(docs)
#Compute dot score between query and all document embeddings
scores = torch.mm(query_emb, doc_emb.transpose(0, 1))[0].cpu().tolist()
#Combine docs & scores
doc_score_pairs = list(zip(docs, scores))
#Sort by decreasing score
doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True)
#Output passages & scores
for doc, score in doc_score_pairs:
print(score, doc)
```
## Technical Details
In the following some technical details how this model must be used:
| Setting | Value |
| --- | :---: |
| Dimensions | 384 |
| Produces normalized embeddings | Yes |
| Pooling-Method | Mean pooling |
| Suitable score functions | dot-product (`util.dot_score`), cosine-similarity (`util.cos_sim`), or euclidean distance |
Note: When loaded with `sentence-transformers`, this model produces normalized embeddings with length 1. In that case, dot-product and cosine-similarity are equivalent. dot-product is preferred as it is faster. Euclidean distance is proportional to dot-product and can also be used.
----
## Background
The project aims to train sentence embedding models on very large sentence level datasets using a self-supervised
contrastive learning objective. We use a contrastive learning objective: given a sentence from the pair, the model should predict which out of a set of randomly sampled other sentences, was actually paired with it in our dataset.
We developped this model during the
[Community week using JAX/Flax for NLP & CV](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104),
organized by Hugging Face. We developped this model as part of the project:
[Train the Best Sentence Embedding Model Ever with 1B Training Pairs](https://discuss.huggingface.co/t/train-the-best-sentence-embedding-model-ever-with-1b-training-pairs/7354). We benefited from efficient hardware infrastructure to run the project: 7 TPUs v3-8, as well as intervention from Googles Flax, JAX, and Cloud team member about efficient deep learning frameworks.
## Intended uses
Our model is intented to be used for semantic search: It encodes queries / questions and text paragraphs in a dense vector space. It finds relevant documents for the given passages.
Note that there is a limit of 512 word pieces: Text longer than that will be truncated. Further note that the model was just trained on input text up to 250 word pieces. It might not work well for longer text.
## Training procedure
The full training script is accessible in this current repository: `train_script.py`.
### Pre-training
We use the pretrained [`nreimers/MiniLM-L6-H384-uncased`](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased) model. Please refer to the model card for more detailed information about the pre-training procedure.
#### Training
We use the concatenation from multiple datasets to fine-tune our model. In total we have about 215M (question, answer) pairs.
We sampled each dataset given a weighted probability which configuration is detailed in the `data_config.json` file.
The model was trained with [MultipleNegativesRankingLoss](https://www.sbert.net/docs/package_reference/losses.html#multiplenegativesrankingloss) using Mean-pooling, cosine-similarity as similarity function, and a scale of 20.
| Dataset | Number of training tuples |
|--------------------------------------------------------|:--------------------------:|
| [WikiAnswers](https://github.com/afader/oqa#wikianswers-corpus) Duplicate question pairs from WikiAnswers | 77,427,422 |
| [PAQ](https://github.com/facebookresearch/PAQ) Automatically generated (Question, Paragraph) pairs for each paragraph in Wikipedia | 64,371,441 |
| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title, Body) pairs from all StackExchanges | 25,316,456 |
| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title, Answer) pairs from all StackExchanges | 21,396,559 |
| [MS MARCO](https://microsoft.github.io/msmarco/) Triplets (query, answer, hard_negative) for 500k queries from Bing search engine | 17,579,773 |
| [GOOAQ: Open Question Answering with Diverse Answer Types](https://github.com/allenai/gooaq) (query, answer) pairs for 3M Google queries and Google featured snippet | 3,012,496 |
| [Amazon-QA](http://jmcauley.ucsd.edu/data/amazon/qa/) (Question, Answer) pairs from Amazon product pages | 2,448,839
| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Answer) pairs from Yahoo Answers | 1,198,260 |
| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Question, Answer) pairs from Yahoo Answers | 681,164 |
| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Question) pairs from Yahoo Answers | 659,896 |
| [SearchQA](https://huggingface.co/datasets/search_qa) (Question, Answer) pairs for 140k questions, each with Top5 Google snippets on that question | 582,261 |
| [ELI5](https://huggingface.co/datasets/eli5) (Question, Answer) pairs from Reddit ELI5 (explainlikeimfive) | 325,475 |
| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions pairs (titles) | 304,525 |
| [Quora Question Triplets](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) (Question, Duplicate_Question, Hard_Negative) triplets for Quora Questions Pairs dataset | 103,663 |
| [Natural Questions (NQ)](https://ai.google.com/research/NaturalQuestions) (Question, Paragraph) pairs for 100k real Google queries with relevant Wikipedia paragraph | 100,231 |
| [SQuAD2.0](https://rajpurkar.github.io/SQuAD-explorer/) (Question, Paragraph) pairs from SQuAD2.0 dataset | 87,599 |
| [TriviaQA](https://huggingface.co/datasets/trivia_qa) (Question, Evidence) pairs | 73,346 |
| **Total** | **214,988,242** |
|
{"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "pipeline_tag": "feature-extraction"}
|
feature-extraction
|
haqishen/test-mode-fe
|
[
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #endpoints_compatible #region-us
|
multi-qa-MiniLM-L6-cos-v1
=========================
This is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and was designed for semantic search. It has been trained on 215M (question, answer) pairs from diverse sources. For an introduction to semantic search, have a look at: URL - Semantic Search
Usage (Sentence-Transformers)
-----------------------------
Using this model becomes easy when you have sentence-transformers installed:
Then you can use the model like this:
Usage (HuggingFace Transformers)
--------------------------------
Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the correct pooling-operation on-top of the contextualized word embeddings.
Technical Details
-----------------
In the following some technical details how this model must be used:
Note: When loaded with 'sentence-transformers', this model produces normalized embeddings with length 1. In that case, dot-product and cosine-similarity are equivalent. dot-product is preferred as it is faster. Euclidean distance is proportional to dot-product and can also be used.
---
Background
----------
The project aims to train sentence embedding models on very large sentence level datasets using a self-supervised
contrastive learning objective. We use a contrastive learning objective: given a sentence from the pair, the model should predict which out of a set of randomly sampled other sentences, was actually paired with it in our dataset.
We developped this model during the
Community week using JAX/Flax for NLP & CV,
organized by Hugging Face. We developped this model as part of the project:
Train the Best Sentence Embedding Model Ever with 1B Training Pairs. We benefited from efficient hardware infrastructure to run the project: 7 TPUs v3-8, as well as intervention from Googles Flax, JAX, and Cloud team member about efficient deep learning frameworks.
Intended uses
-------------
Our model is intented to be used for semantic search: It encodes queries / questions and text paragraphs in a dense vector space. It finds relevant documents for the given passages.
Note that there is a limit of 512 word pieces: Text longer than that will be truncated. Further note that the model was just trained on input text up to 250 word pieces. It might not work well for longer text.
Training procedure
------------------
The full training script is accessible in this current repository: 'train\_script.py'.
### Pre-training
We use the pretrained 'nreimers/MiniLM-L6-H384-uncased' model. Please refer to the model card for more detailed information about the pre-training procedure.
#### Training
We use the concatenation from multiple datasets to fine-tune our model. In total we have about 215M (question, answer) pairs.
We sampled each dataset given a weighted probability which configuration is detailed in the 'data\_config.json' file.
The model was trained with MultipleNegativesRankingLoss using Mean-pooling, cosine-similarity as similarity function, and a scale of 20.
|
[
"### Pre-training\n\n\nWe use the pretrained 'nreimers/MiniLM-L6-H384-uncased' model. Please refer to the model card for more detailed information about the pre-training procedure.",
"#### Training\n\n\nWe use the concatenation from multiple datasets to fine-tune our model. In total we have about 215M (question, answer) pairs.\nWe sampled each dataset given a weighted probability which configuration is detailed in the 'data\\_config.json' file.\n\n\nThe model was trained with MultipleNegativesRankingLoss using Mean-pooling, cosine-similarity as similarity function, and a scale of 20."
] |
[
"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #endpoints_compatible #region-us \n",
"### Pre-training\n\n\nWe use the pretrained 'nreimers/MiniLM-L6-H384-uncased' model. Please refer to the model card for more detailed information about the pre-training procedure.",
"#### Training\n\n\nWe use the concatenation from multiple datasets to fine-tune our model. In total we have about 215M (question, answer) pairs.\nWe sampled each dataset given a weighted probability which configuration is detailed in the 'data\\_config.json' file.\n\n\nThe model was trained with MultipleNegativesRankingLoss using Mean-pooling, cosine-similarity as similarity function, and a scale of 20."
] |
[
39,
48,
105
] |
[
"passage: TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #endpoints_compatible #region-us \n### Pre-training\n\n\nWe use the pretrained 'nreimers/MiniLM-L6-H384-uncased' model. Please refer to the model card for more detailed information about the pre-training procedure.#### Training\n\n\nWe use the concatenation from multiple datasets to fine-tune our model. In total we have about 215M (question, answer) pairs.\nWe sampled each dataset given a weighted probability which configuration is detailed in the 'data\\_config.json' file.\n\n\nThe model was trained with MultipleNegativesRankingLoss using Mean-pooling, cosine-similarity as similarity function, and a scale of 20."
] |
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] |
null | null |
transformers
|
<!-- 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-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1642
## 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.2251 | 1.0 | 5533 | 1.1707 |
| 0.9554 | 2.0 | 11066 | 1.1211 |
| 0.7645 | 3.0 | 16599 | 1.1642 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "distilbert-base-uncased-finetuned-squad", "results": []}]}
|
question-answering
|
hark99/distilbert-base-uncased-finetuned-squad
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
distilbert-base-uncased-finetuned-squad
=======================================
This model is a fine-tuned version of distilbert-base-uncased on the squad dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1642
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: 3
### Training results
### Framework versions
* Transformers 4.16.2
* Pytorch 1.10.0+cu111
* Datasets 1.18.3
* Tokenizers 0.11.0
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.16.2\n* Pytorch 1.10.0+cu111\n* Datasets 1.18.3\n* Tokenizers 0.11.0"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.16.2\n* Pytorch 1.10.0+cu111\n* Datasets 1.18.3\n* Tokenizers 0.11.0"
] |
[
56,
98,
4,
35
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3### Training results### Framework versions\n\n\n* Transformers 4.16.2\n* Pytorch 1.10.0+cu111\n* Datasets 1.18.3\n* Tokenizers 0.11.0"
] |
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null | null |
transformers
|
<!-- 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-ingredients
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the ingredients_yes_no dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0105
- Precision: 0.9899
- Recall: 0.9932
- F1: 0.9915
- Accuracy: 0.9978
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 47 | 0.2783 | 0.4 | 0.5492 | 0.4629 | 0.8910 |
| No log | 2.0 | 94 | 0.1089 | 0.8145 | 0.8780 | 0.8450 | 0.9718 |
| No log | 3.0 | 141 | 0.0273 | 0.9865 | 0.9932 | 0.9899 | 0.9973 |
| No log | 4.0 | 188 | 0.0168 | 0.9865 | 0.9932 | 0.9899 | 0.9973 |
| No log | 5.0 | 235 | 0.0156 | 0.9865 | 0.9898 | 0.9882 | 0.9957 |
| No log | 6.0 | 282 | 0.0129 | 0.9865 | 0.9932 | 0.9899 | 0.9973 |
| No log | 7.0 | 329 | 0.0121 | 0.9899 | 0.9932 | 0.9915 | 0.9978 |
| No log | 8.0 | 376 | 0.0115 | 0.9899 | 0.9932 | 0.9915 | 0.9978 |
| No log | 9.0 | 423 | 0.0108 | 0.9899 | 0.9932 | 0.9915 | 0.9978 |
| No log | 10.0 | 470 | 0.0105 | 0.9899 | 0.9932 | 0.9915 | 0.9978 |
### Framework versions
- Transformers 4.10.2
- Pytorch 1.9.0+cu102
- Datasets 1.11.0
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["ingredients_yes_no"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-ingredients", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "ingredients_yes_no", "type": "ingredients_yes_no", "args": "IngredientsYesNo"}, "metrics": [{"type": "precision", "value": 0.9898648648648649, "name": "Precision"}, {"type": "recall", "value": 0.9932203389830508, "name": "Recall"}, {"type": "f1", "value": 0.9915397631133671, "name": "F1"}, {"type": "accuracy", "value": 0.9978308026030369, "name": "Accuracy"}]}]}]}
|
token-classification
|
harr/distilbert-base-uncased-finetuned-ingredients
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:ingredients_yes_no",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-ingredients_yes_no #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
|
distilbert-base-uncased-finetuned-ingredients
=============================================
This model is a fine-tuned version of distilbert-base-uncased on the ingredients\_yes\_no dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0105
* Precision: 0.9899
* Recall: 0.9932
* F1: 0.9915
* Accuracy: 0.9978
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10
### Training results
### Framework versions
* Transformers 4.10.2
* Pytorch 1.9.0+cu102
* Datasets 1.11.0
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.10.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.11.0\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-ingredients_yes_no #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.10.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.11.0\n* Tokenizers 0.10.3"
] |
[
77,
98,
4,
34
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-ingredients_yes_no #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10### Training results### Framework versions\n\n\n* Transformers 4.10.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.11.0\n* Tokenizers 0.10.3"
] |
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] |
null | null | null |
Simple Sentiment Ananlysis
|
{}
| null |
harsh2040/sentiment_ananlysis
|
[
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#region-us
|
Simple Sentiment Ananlysis
|
[] |
[
"TAGS\n#region-us \n"
] |
[
6
] |
[
"passage: TAGS\n#region-us \n"
] |
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] |
null | null |
transformers
|
# Wav2Vec2-Large-LV60-TIMIT
Fine-tuned [facebook/wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60)
on the [timit_asr dataset](https://huggingface.co/datasets/timit_asr).
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
```python
import soundfile as sf
import torch
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
model_name = "hktayal345/wav2vec2-large-lv60-timit-asr"
processor = Wav2Vec2Processor.from_pretrained(model_name)
model = Wav2Vec2ForCTC.from_pretrained(model_name)
model.eval()
dataset = load_dataset("timit_asr", split="test").shuffle().select(range(10))
char_translations = str.maketrans({"-": " ", ",": "", ".": "", "?": ""})
def prepare_example(example):
example["speech"], _ = sf.read(example["file"])
example["text"] = example["text"].translate(char_translations)
example["text"] = " ".join(example["text"].split()) # clean up whitespaces
example["text"] = example["text"].lower()
return example
dataset = dataset.map(prepare_example, remove_columns=["file"])
inputs = processor(dataset["speech"], sampling_rate=16000, return_tensors="pt", padding="longest")
with torch.no_grad():
predicted_ids = torch.argmax(model(inputs.input_values).logits, dim=-1)
predicted_ids[predicted_ids == -100] = processor.tokenizer.pad_token_id # see fine-tuning script
predicted_transcripts = processor.tokenizer.batch_decode(predicted_ids)
for reference, predicted in zip(dataset["text"], predicted_transcripts):
print("reference:", reference)
print("predicted:", predicted)
print("--")
```
Here's the output:
```
reference: the emblem depicts the acropolis all aglow
predicted: the amblum depicts the acropolis all a glo
--
reference: don't ask me to carry an oily rag like that
predicted: don't ask me to carry an oily rag like that
--
reference: they enjoy it when i audition
predicted: they enjoy it when i addition
--
reference: set aside to dry with lid on sugar bowl
predicted: set aside to dry with a litt on shoogerbowl
--
reference: a boring novel is a superb sleeping pill
predicted: a bor and novel is a suberb sleeping peel
--
reference: only the most accomplished artists obtain popularity
predicted: only the most accomplished artists obtain popularity
--
reference: he has never himself done anything for which to be hated which of us has
predicted: he has never himself done anything for which to be hated which of us has
--
reference: the fish began to leap frantically on the surface of the small lake
predicted: the fish began to leap frantically on the surface of the small lake
--
reference: or certain words or rituals that child and adult go through may do the trick
predicted: or certain words or rituals that child an adult go through may do the trick
--
reference: are your grades higher or lower than nancy's
predicted: are your grades higher or lower than nancies
--
```
## Fine-Tuning Script
You can find the script used to produce this model
[here](https://colab.research.google.com/drive/1gVaZhFuIXxBDN2pD0esW490azlbQtQ7C?usp=sharing).
**Note:** This model can be fine-tuned further;
[trainer_state.json](https://huggingface.co/harshit345/wav2vec2-large-lv60-timit/blob/main/trainer_state.json)
shows useful details, namely the last state (this checkpoint):
```json
{
"epoch": 29.51,
"eval_loss": 25.424150466918945,
"eval_runtime": 182.9499,
"eval_samples_per_second": 9.183,
"eval_wer": 0.1351704233095107,
"step": 8500
}
```
|
{"language": "en", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech"], "datasets": ["timit_asr"]}
|
automatic-speech-recognition
|
harshit345/wav2vec2-large-lv60-timit
|
[
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"en",
"dataset:timit_asr",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #en #dataset-timit_asr #license-apache-2.0 #endpoints_compatible #region-us
|
# Wav2Vec2-Large-LV60-TIMIT
Fine-tuned facebook/wav2vec2-large-lv60
on the timit_asr dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
Here's the output:
## Fine-Tuning Script
You can find the script used to produce this model
here.
Note: This model can be fine-tuned further;
trainer_state.json
shows useful details, namely the last state (this checkpoint):
|
[
"# Wav2Vec2-Large-LV60-TIMIT\n\nFine-tuned facebook/wav2vec2-large-lv60\non the timit_asr dataset.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:\n\n\n\nHere's the output:",
"## Fine-Tuning Script\n\nYou can find the script used to produce this model\nhere.\n\nNote: This model can be fine-tuned further;\ntrainer_state.json\nshows useful details, namely the last state (this checkpoint):"
] |
[
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #en #dataset-timit_asr #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-LV60-TIMIT\n\nFine-tuned facebook/wav2vec2-large-lv60\non the timit_asr dataset.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:\n\n\n\nHere's the output:",
"## Fine-Tuning Script\n\nYou can find the script used to produce this model\nhere.\n\nNote: This model can be fine-tuned further;\ntrainer_state.json\nshows useful details, namely the last state (this checkpoint):"
] |
[
64,
60,
26,
50
] |
[
"passage: TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #en #dataset-timit_asr #license-apache-2.0 #endpoints_compatible #region-us \n# Wav2Vec2-Large-LV60-TIMIT\n\nFine-tuned facebook/wav2vec2-large-lv60\non the timit_asr dataset.\nWhen using this model, make sure that your speech input is sampled at 16kHz.## Usage\n\nThe model can be used directly (without a language model) as follows:\n\n\n\nHere's the output:## Fine-Tuning Script\n\nYou can find the script used to produce this model\nhere.\n\nNote: This model can be fine-tuned further;\ntrainer_state.json\nshows useful details, namely the last state (this checkpoint):"
] |
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] |
null | null |
transformers
|
# Wav2Vec2-Large-XLSR-53-greek
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on greek using the [Common Voice](https://huggingface.co/datasets/common_voice) and [CSS10 Greek: Single Speaker Speech Dataset](https://www.kaggle.com/bryanpark/greek-single-speaker-speech-dataset).
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
```python
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
test_dataset = load_dataset("common_voice", "el", split="test[:2%]") #TODO: replace {lang_id} in your language code here. Make sure the code is one of the *ISO codes* of [this](https://huggingface.co/languages) site.
processor = Wav2Vec2Processor.from_pretrained("vasilis/wav2vec2-large-xlsr-53-greek") #TODO: replace {model_id} with your model id. The model id consists of {your_username}/{your_modelname}, *e.g.* `elgeish/wav2vec2-large-xlsr-53-arabic`
model = Wav2Vec2ForCTC.from_pretrained("vasilis/wav2vec2-large-xlsr-53-greek") #TODO: replace {model_id} with your model id. The model id consists of {your_username}/{your_modelname}, *e.g.* `elgeish/wav2vec2-large-xlsr-53-arabic`
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
predicted_ids = torch.argmax(logits, dim=-1)
print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset["sentence"][:2])
```
| Reference | Prediction |
| ------------- | ------------- |
| ΤΟ ΒΑΣΙΛΌΠΟΥΛΟ, ΠΟΥ ΜΟΙΆΖΕΙ ΛΕΟΝΤΑΡΆΚΙ ΚΑΙ ΑΕΤΟΥΔΆΚΙ | ΤΟ ΒΑΣΙΛΌΠΟΥΛΟ ΠΟΥ ΜΙΑΣΕ ΛΙΟΝΤΑΡΑΚΉ ΚΑΙ ΑΪΤΟΥΔΆΚΙ |
| ΣΥΝΆΜΑ ΞΕΠΡΌΒΑΛΑΝ ΑΠΌ ΜΈΣΑ ΑΠΌ ΤΑ ΔΈΝΤΡΑ, ΔΕΞΙΆ, ΑΡΜΑΤΩΜΈΝΟΙ ΚΑΒΑΛΑΡΈΟΙ. | ΣΥΝΆΜΑ ΚΑΙ ΤΡΌΒΑΛΑΝ ΑΠΌ ΜΈΣΑ ΑΠΌ ΤΑ ΔΈΝΤΡΑ ΔΕΞΙΆ ΑΡΜΑΤΩΜΈΝΟΙ ΚΑΒΑΛΑΡΈΟΙ |
| ΤΑ ΣΥΣΚΕΥΑΣΜΈΝΑ ΒΙΟΛΟΓΙΚΆ ΛΑΧΑΝΙΚΆ ΔΕΝ ΠΕΡΙΈΧΟΥΝ ΣΥΝΤΗΡΗΤΙΚΆ ΚΑΙ ΟΡΜΌΝΕΣ | ΤΑ ΣΥΣΚΕΦΑΣΜΈΝΑ ΒΙΟΛΟΓΙΚΆ ΛΑΧΑΝΙΚΆ ΔΕΝ ΠΕΡΙΈΧΟΥΝ ΣΙΔΗΡΗΤΙΚΆ ΚΑΙ ΟΡΜΌΝΕΣ |
| ΑΚΟΛΟΥΘΉΣΕΤΕ ΜΕ! | ΑΚΟΛΟΥΘΉΣΤΕ ΜΕ |
| ΚΑΙ ΠΟΎ ΜΠΟΡΏ ΝΑ ΤΟΝ ΒΡΩ; | Ε ΠΟΎ ΜΠΟΡΏ ΝΑ ΤΙ ΕΒΡΩ |
| ΝΑΙ! ΑΠΟΚΡΊΘΗΚΕ ΤΟ ΠΑΙΔΊ | ΝΑΙ ΑΠΟΚΡΊΘΗΚΕ ΤΟ ΠΑΙΔΊ |
| ΤΟ ΠΑΛΆΤΙ ΜΟΥ ΤΟ ΠΡΟΜΉΘΕΥΕ. | ΤΟ ΠΑΛΆΤΙ ΜΟΥ ΤΟ ΠΡΟΜΉΘΕΥΕ |
| ΉΛΘΕ ΜΉΝΥΜΑ ΑΠΌ ΤΟ ΘΕΊΟ ΒΑΣΙΛΙΆ; | ΉΛΘΑ ΜΕΊΝΕΙ ΜΕ ΑΠΌ ΤΟ ΘΕΊΟ ΒΑΣΊΛΙΑ |
| ΠΑΡΑΚΆΤΩ, ΈΝΑ ΡΥΆΚΙ ΜΟΥΡΜΟΎΡΙΖΕ ΓΛΥΚΆ, ΚΥΛΏΝΤΑΣ ΤΑ ΚΡΥΣΤΑΛΛΈΝΙΑ ΝΕΡΆ ΤΟΥ ΑΝΆΜΕΣΑ ΣΤΑ ΠΥΚΝΆ ΧΑΜΌΔΕΝΤΡΑ. | ΠΑΡΑΚΆΤΩ ΈΝΑ ΡΥΆΚΙ ΜΟΥΡΜΟΎΡΙΖΕ ΓΛΥΚΆ ΚΥΛΏΝΤΑΣ ΤΑ ΚΡΥΣΤΑΛΛΈΝΙΑ ΝΕΡΆ ΤΟΥ ΑΝΆΜΕΣΑ ΣΤΑ ΠΥΚΡΆ ΧΑΜΌΔΕΝΤΡΑ |
| ΠΡΆΓΜΑΤΙ, ΕΊΝΑΙ ΑΣΤΕΊΟ ΝΑ ΠΆΡΕΙ Ο ΔΙΆΒΟΛΟΣ | ΠΡΆΓΜΑΤΗ ΕΊΝΑΙ ΑΣΤΕΊΟ ΝΑ ΠΆΡΕΙ Ο ΔΙΆΒΟΛΟΣ |
## Evaluation
The model can be evaluated as follows on the greek test data of Common Voice.
```python
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re
test_dataset = load_dataset("common_voice", "el", split="test") #TODO: replace {lang_id} in your language code here. Make sure the code is one of the *ISO codes* of [this](https://huggingface.co/languages) site.
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("vasilis/wav2vec2-large-xlsr-53-greek") #TODO: replace {model_id} with your model id. The model id consists of {your_username}/{your_modelname}, *e.g.* `elgeish/wav2vec2-large-xlsr-53-arabic`
model = Wav2Vec2ForCTC.from_pretrained("vasilis/wav2vec2-large-xlsr-53-greek") #TODO: replace {model_id} with your model id. The model id consists of {your_username}/{your_modelname}, *e.g.* `elgeish/wav2vec2-large-xlsr-53-arabic`
model.to("cuda")
chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“]' # TODO: adapt this list to include all special characters you removed from the data
normalize_greek_letters = {"ς": "σ"}
# normalize_greek_letters = {"ά": "α", "έ": "ε", "ί": "ι", 'ϊ': "ι", "ύ": "υ", "ς": "σ", "ΐ": "ι", 'ϋ': "υ", "ή": "η", "ώ": "ω", 'ό': "ο"}
remove_chars_greek = {"a": "", "h": "", "n": "", "g": "", "o": "", "v": "", "e": "", "r": "", "t": "", "«": "", "»": "", "m": "", '́': '', "·": "", "’": "", '´': ""}
replacements = {**normalize_greek_letters, **remove_chars_greek}
resampler = {
48_000: torchaudio.transforms.Resample(48_000, 16_000),
44100: torchaudio.transforms.Resample(44100, 16_000),
32000: torchaudio.transforms.Resample(32000, 16_000)
}
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
for key, value in replacements.items():
batch["sentence"] = batch["sentence"].replace(key, value)
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler[sampling_rate](speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def evaluate(batch):
inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
pred_ids = torch.argmax(logits, dim=-1)
batch["pred_strings"] = processor.batch_decode(pred_ids)
return batch
result = test_dataset.map(evaluate, batched=True, batch_size=8)
print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
print("CER: {:2f}".format(100 * wer.compute(predictions=[" ".join(list(entry)) for entry in result["pred_strings"]], references=[" ".join(list(entry)) for entry in result["sentence"]])))
```
**Test Result**: 18.996669 %
## Training
The Common Voice train dataset was used for training. Also all of `CSS10 Greek` was used using the normalized transcripts.
During text preprocessing letter `ς` is normalized to `σ` the reason is that both letters sound the same with `ς` only used as the ending character of words. So, the change can be mapped up to proper dictation easily. I tried removing all accents from letters as well that improved `WER` significantly. The model was reaching `17%` WER easily without having converged. However, the text preprocessing needed to do after to fix transcrtiptions would be more complicated. A language model should fix things easily though. Another thing that could be tried out would be to change all of `ι`, `η` ... etc to a single character since all sound the same. similar for `o` and `ω` these should help the acoustic model part significantly since all these characters map to the same sound. But further text normlization would be needed.
|
{"language": "el", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer", "cer"], "model-index": [{"name": "V XLSR Wav2Vec2 Large 53 - greek", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Common Voice el", "type": "common_voice", "args": "el"}, "metrics": [{"type": "wer", "value": 18.996669, "name": "Test WER"}, {"type": "cer", "value": 5.781874, "name": "Test CER"}]}]}]}
|
automatic-speech-recognition
|
harshit345/xlsr-53-wav2vec-greek
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"el",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"el"
] |
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #el #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
Wav2Vec2-Large-XLSR-53-greek
============================
Fine-tuned facebook/wav2vec2-large-xlsr-53 on greek using the Common Voice and CSS10 Greek: Single Speaker Speech Dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
Usage
-----
The model can be used directly (without a language model) as follows:
Evaluation
----------
The model can be evaluated as follows on the greek test data of Common Voice.
Test Result: 18.996669 %
Training
--------
The Common Voice train dataset was used for training. Also all of 'CSS10 Greek' was used using the normalized transcripts.
During text preprocessing letter 'ς' is normalized to 'σ' the reason is that both letters sound the same with 'ς' only used as the ending character of words. So, the change can be mapped up to proper dictation easily. I tried removing all accents from letters as well that improved 'WER' significantly. The model was reaching '17%' WER easily without having converged. However, the text preprocessing needed to do after to fix transcrtiptions would be more complicated. A language model should fix things easily though. Another thing that could be tried out would be to change all of 'ι', 'η' ... etc to a single character since all sound the same. similar for 'o' and 'ω' these should help the acoustic model part significantly since all these characters map to the same sound. But further text normlization would be needed.
|
[] |
[
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #el #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n"
] |
[
77
] |
[
"passage: TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #el #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
# Wav2Vec2-Large-XLSR-53-hindi
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) hindi using the [Multilingual and code-switching ASR challenges for low resource Indian languages](https://navana-tech.github.io/IS21SS-indicASRchallenge/data.html).
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
```python
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
test_dataset = load_dataset("common_voice", "hi", split="test[:2%]")
processor = Wav2Vec2Processor.from_pretrained("theainerd/Wav2Vec2-large-xlsr-hindi")
model = Wav2Vec2ForCTC.from_pretrained("theainerd/Wav2Vec2-large-xlsr-hindi")
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
predicted_ids = torch.argmax(logits, dim=-1)
print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset["sentence"][:2])
```
## Evaluation
The model can be evaluated as follows on the hindi test data of Common Voice.
```python
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re
test_dataset = load_dataset("common_voice", "hi", split="test")
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("theainerd/Wav2Vec2-large-xlsr-hindi")
model = Wav2Vec2ForCTC.from_pretrained("theainerd/Wav2Vec2-large-xlsr-hindi")
model.to("cuda")
resampler = torchaudio.transforms.Resample(48_000, 16_000)
chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“]'
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def evaluate(batch):
inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
pred_ids = torch.argmax(logits, dim=-1)
batch["pred_strings"] = processor.batch_decode(pred_ids)
return batch
result = test_dataset.map(evaluate, batched=True, batch_size=8)
print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
```
**Test Result**:20.22 %
## Training
The script used for training can be found [Hindi ASR Fine Tuning Wav2Vec2](https://colab.research.google.com/drive/1nY5WMj1oNlexD_qDeNYL7ZM427A021CV?usp=sharing)
|
{"language": "hi", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["Interspeech 2021"], "metrics": ["wer"], "model-index": [{"name": "XLSR Wav2Vec2 Hindi by Shyam Sunder Kumar", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Common Voice hi", "type": "common_voice", "args": "hi"}, "metrics": [{"type": "wer", "value": 20.22, "name": "Test WER"}]}]}]}
|
automatic-speech-recognition
|
harshit345/xlsr-53-wav2vec-hi
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"hi",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"hi"
] |
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #hi #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53-hindi
Fine-tuned facebook/wav2vec2-large-xlsr-53 hindi using the Multilingual and code-switching ASR challenges for low resource Indian languages.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
## Evaluation
The model can be evaluated as follows on the hindi test data of Common Voice.
Test Result:20.22 %
## Training
The script used for training can be found Hindi ASR Fine Tuning Wav2Vec2
|
[
"# Wav2Vec2-Large-XLSR-53-hindi\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 hindi using the Multilingual and code-switching ASR challenges for low resource Indian languages.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model can be evaluated as follows on the hindi test data of Common Voice. \n\n\n\n\nTest Result:20.22 %",
"## Training\n\nThe script used for training can be found Hindi ASR Fine Tuning Wav2Vec2"
] |
[
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #hi #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-hindi\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 hindi using the Multilingual and code-switching ASR challenges for low resource Indian languages.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model can be evaluated as follows on the hindi test data of Common Voice. \n\n\n\n\nTest Result:20.22 %",
"## Training\n\nThe script used for training can be found Hindi ASR Fine Tuning Wav2Vec2"
] |
[
68,
76,
20,
27,
22
] |
[
"passage: TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #hi #license-apache-2.0 #model-index #endpoints_compatible #region-us \n# Wav2Vec2-Large-XLSR-53-hindi\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 hindi using the Multilingual and code-switching ASR challenges for low resource Indian languages.\nWhen using this model, make sure that your speech input is sampled at 16kHz.## Usage\n\nThe model can be used directly (without a language model) as follows:## Evaluation\n\nThe model can be evaluated as follows on the hindi test data of Common Voice. \n\n\n\n\nTest Result:20.22 %## Training\n\nThe script used for training can be found Hindi ASR Fine Tuning Wav2Vec2"
] |
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] |
null | null |
transformers
|
~~~
# requirement packages
!pip install git+https://github.com/huggingface/datasets.git
!pip install git+https://github.com/huggingface/transformers.git
!pip install torchaudio
!pip install librosa
~~~
# prediction
~~~
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchaudio
from transformers import AutoConfig, Wav2Vec2FeatureExtractor
import librosa
import IPython.display as ipd
import numpy as np
import pandas as pd
~~~
~~~
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model_name_or_path = "harshit345/xlsr-wav2vec-speech-emotion-recognition"
config = AutoConfig.from_pretrained(model_name_or_path)
feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(model_name_or_path)
sampling_rate = feature_extractor.sampling_rate
model = Wav2Vec2ForSpeechClassification.from_pretrained(model_name_or_path).to(device)
~~~
~~~
def speech_file_to_array_fn(path, sampling_rate):
speech_array, _sampling_rate = torchaudio.load(path)
resampler = torchaudio.transforms.Resample(_sampling_rate)
speech = resampler(speech_array).squeeze().numpy()
return speech
def predict(path, sampling_rate):
speech = speech_file_to_array_fn(path, sampling_rate)
inputs = feature_extractor(speech, sampling_rate=sampling_rate, return_tensors="pt", padding=True)
inputs = {key: inputs[key].to(device) for key in inputs}
with torch.no_grad():
logits = model(**inputs).logits
scores = F.softmax(logits, dim=1).detach().cpu().numpy()[0]
outputs = [{"Emotion": config.id2label[i], "Score": f"{round(score * 100, 3):.1f}%"} for i, score in enumerate(scores)]
return outputs
~~~
# prediction
~~~
# path for a sample
path = '/data/jtes_v1.1/wav/f01/ang/f01_ang_01.wav'
outputs = predict(path, sampling_rate)
~~~
~~~
[{'Emotion': 'anger', 'Score': '78.3%'},
{'Emotion': 'disgust', 'Score': '11.7%'},
{'Emotion': 'fear', 'Score': '5.4%'},
{'Emotion': 'happiness', 'Score': '4.1%'},
{'Emotion': 'sadness', 'Score': '0.5%'}]
~~~
## Evaluation
The following tables summarize the scores obtained by model overall and per each class.
| Emotions | precision | recall | f1-score | accuracy |
|-----------|-----------|--------|----------|----------|
| anger | 0.82 | 1.00 | 0.81 | |
| disgust | 0.85 | 0.96 | 0.85 | |
| fear | 0.78 | 0.88 | 0.80 | |
| happiness | 0.84 | 0.71 | 0.78 | |
| sadness | 0.86 | 1.00 | 0.79 | |
| | | | Overall | 0.806 |
##
Colab Notebook
https://colab.research.google.com/drive/1aPPb_ZVS5dlFVZySly8Q80a44La1XjJu?usp=sharing
|
{"language": "en", "license": "apache-2.0", "tags": ["audio", "audio-classification", "speech"], "datasets": ["aesdd"]}
|
audio-classification
|
harshit345/xlsr-wav2vec-speech-emotion-recognition
|
[
"transformers",
"pytorch",
"wav2vec2",
"audio",
"audio-classification",
"speech",
"en",
"dataset:aesdd",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #wav2vec2 #audio #audio-classification #speech #en #dataset-aesdd #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
```
# requirement packages
!pip install git+URL
!pip install git+URL
!pip install torchaudio
!pip install librosa
```
prediction
==========
```
import torch
import URL as nn
import URL.functional as F
import torchaudio
from transformers import AutoConfig, Wav2Vec2FeatureExtractor
import librosa
import IPython.display as ipd
import numpy as np
import pandas as pd
```
```
device = URL("cuda" if URL.is_available() else "cpu")
model_name_or_path = "harshit345/xlsr-wav2vec-speech-emotion-recognition"
config = AutoConfig.from_pretrained(model_name_or_path)
feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(model_name_or_path)
sampling_rate = feature_extractor.sampling_rate
model = Wav2Vec2ForSpeechClassification.from_pretrained(model_name_or_path).to(device)
```
```
def speech_file_to_array_fn(path, sampling_rate):
speech_array, _sampling_rate = URL(path)
resampler = torchaudio.transforms.Resample(_sampling_rate)
speech = resampler(speech_array).squeeze().numpy()
return speech
def predict(path, sampling_rate):
speech = speech_file_to_array_fn(path, sampling_rate)
inputs = feature_extractor(speech, sampling_rate=sampling_rate, return_tensors="pt", padding=True)
inputs = {key: inputs[key].to(device) for key in inputs}
with torch.no_grad():
logits = model(inputs).logits
scores = F.softmax(logits, dim=1).detach().cpu().numpy()[0]
outputs = [{"Emotion": config.id2label[i], "Score": f"{round(score * 100, 3):.1f}%"} for i, score in enumerate(scores)]
return outputs
```
prediction
==========
```
# path for a sample
path = '/data/jtes_v1.1/wav/f01/ang/f01_ang_01.wav'
outputs = predict(path, sampling_rate)
```
```
[{'Emotion': 'anger', 'Score': '78.3%'},
{'Emotion': 'disgust', 'Score': '11.7%'},
{'Emotion': 'fear', 'Score': '5.4%'},
{'Emotion': 'happiness', 'Score': '4.1%'},
{'Emotion': 'sadness', 'Score': '0.5%'}]
```
Evaluation
----------
The following tables summarize the scores obtained by model overall and per each class.
Colab Notebook
URL
|
[
"# requirement packages\n!pip install git+URL\n!pip install git+URL\n!pip install torchaudio\n!pip install librosa\n\n\n```\n\nprediction\n==========\n\n\n\n```\nimport torch\nimport URL as nn\nimport URL.functional as F\nimport torchaudio\nfrom transformers import AutoConfig, Wav2Vec2FeatureExtractor\nimport librosa\nimport IPython.display as ipd\nimport numpy as np\nimport pandas as pd\n\n```\n\n\n```\ndevice = URL(\"cuda\" if URL.is_available() else \"cpu\")\nmodel_name_or_path = \"harshit345/xlsr-wav2vec-speech-emotion-recognition\"\nconfig = AutoConfig.from_pretrained(model_name_or_path)\nfeature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(model_name_or_path)\nsampling_rate = feature_extractor.sampling_rate\nmodel = Wav2Vec2ForSpeechClassification.from_pretrained(model_name_or_path).to(device)\n\n```\n\n\n```\ndef speech_file_to_array_fn(path, sampling_rate):\n speech_array, _sampling_rate = URL(path)\n resampler = torchaudio.transforms.Resample(_sampling_rate)\n speech = resampler(speech_array).squeeze().numpy()\n return speech\ndef predict(path, sampling_rate):\n speech = speech_file_to_array_fn(path, sampling_rate)\n inputs = feature_extractor(speech, sampling_rate=sampling_rate, return_tensors=\"pt\", padding=True)\n inputs = {key: inputs[key].to(device) for key in inputs}\n with torch.no_grad():\n logits = model(inputs).logits\n scores = F.softmax(logits, dim=1).detach().cpu().numpy()[0]\n outputs = [{\"Emotion\": config.id2label[i], \"Score\": f\"{round(score * 100, 3):.1f}%\"} for i, score in enumerate(scores)]\n return outputs\n\n```\n\nprediction\n==========\n\n\n\n```",
"# path for a sample\npath = '/data/jtes_v1.1/wav/f01/ang/f01_ang_01.wav' \noutputs = predict(path, sampling_rate)\n\n```\n\n\n```\n[{'Emotion': 'anger', 'Score': '78.3%'},\n {'Emotion': 'disgust', 'Score': '11.7%'},\n {'Emotion': 'fear', 'Score': '5.4%'},\n {'Emotion': 'happiness', 'Score': '4.1%'},\n {'Emotion': 'sadness', 'Score': '0.5%'}]\n\n```\n\nEvaluation\n----------\n\n\nThe following tables summarize the scores obtained by model overall and per each class.\n\n\n\n\nColab Notebook\nURL"
] |
[
"TAGS\n#transformers #pytorch #wav2vec2 #audio #audio-classification #speech #en #dataset-aesdd #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# requirement packages\n!pip install git+URL\n!pip install git+URL\n!pip install torchaudio\n!pip install librosa\n\n\n```\n\nprediction\n==========\n\n\n\n```\nimport torch\nimport URL as nn\nimport URL.functional as F\nimport torchaudio\nfrom transformers import AutoConfig, Wav2Vec2FeatureExtractor\nimport librosa\nimport IPython.display as ipd\nimport numpy as np\nimport pandas as pd\n\n```\n\n\n```\ndevice = URL(\"cuda\" if URL.is_available() else \"cpu\")\nmodel_name_or_path = \"harshit345/xlsr-wav2vec-speech-emotion-recognition\"\nconfig = AutoConfig.from_pretrained(model_name_or_path)\nfeature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(model_name_or_path)\nsampling_rate = feature_extractor.sampling_rate\nmodel = Wav2Vec2ForSpeechClassification.from_pretrained(model_name_or_path).to(device)\n\n```\n\n\n```\ndef speech_file_to_array_fn(path, sampling_rate):\n speech_array, _sampling_rate = URL(path)\n resampler = torchaudio.transforms.Resample(_sampling_rate)\n speech = resampler(speech_array).squeeze().numpy()\n return speech\ndef predict(path, sampling_rate):\n speech = speech_file_to_array_fn(path, sampling_rate)\n inputs = feature_extractor(speech, sampling_rate=sampling_rate, return_tensors=\"pt\", padding=True)\n inputs = {key: inputs[key].to(device) for key in inputs}\n with torch.no_grad():\n logits = model(inputs).logits\n scores = F.softmax(logits, dim=1).detach().cpu().numpy()[0]\n outputs = [{\"Emotion\": config.id2label[i], \"Score\": f\"{round(score * 100, 3):.1f}%\"} for i, score in enumerate(scores)]\n return outputs\n\n```\n\nprediction\n==========\n\n\n\n```",
"# path for a sample\npath = '/data/jtes_v1.1/wav/f01/ang/f01_ang_01.wav' \noutputs = predict(path, sampling_rate)\n\n```\n\n\n```\n[{'Emotion': 'anger', 'Score': '78.3%'},\n {'Emotion': 'disgust', 'Score': '11.7%'},\n {'Emotion': 'fear', 'Score': '5.4%'},\n {'Emotion': 'happiness', 'Score': '4.1%'},\n {'Emotion': 'sadness', 'Score': '0.5%'}]\n\n```\n\nEvaluation\n----------\n\n\nThe following tables summarize the scores obtained by model overall and per each class.\n\n\n\n\nColab Notebook\nURL"
] |
[
60,
567,
195
] |
[
"passage: TAGS\n#transformers #pytorch #wav2vec2 #audio #audio-classification #speech #en #dataset-aesdd #license-apache-2.0 #endpoints_compatible #has_space #region-us \n"
] |
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] |
null | null |
transformers
|
# Wav2vec2-Large-English
Fine-tuned [facebook/wav2vec2-large](https://huggingface.co/facebook/wav2vec2-large) on English using the [Common Voice](https://huggingface.co/datasets/common_voice).
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows...
Using the [ASRecognition](https://github.com/jonatasgrosman/asrecognition) library:
```python
from asrecognition import ASREngine
asr = ASREngine("fr", model_path="jonatasgrosman/wav2vec2-large-english")
audio_paths = ["/path/to/file.mp3", "/path/to/another_file.wav"]
transcriptions = asr.transcribe(audio_paths)
```
Writing your own inference script:
```python
import torch
import librosa
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
LANG_ID = "en"
MODEL_ID = "jonatasgrosman/wav2vec2-large-english"
SAMPLES = 10
test_dataset = load_dataset("common_voice", LANG_ID, split=f"test[:{SAMPLES}]")
processor = Wav2Vec2Processor.from_pretrained(MODEL_ID)
model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID)
# Preprocessing the datasets.
# We need to read the audio files as arrays
def speech_file_to_array_fn(batch):
speech_array, sampling_rate = librosa.load(batch["path"], sr=16_000)
batch["speech"] = speech_array
batch["sentence"] = batch["sentence"].upper()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
predicted_ids = torch.argmax(logits, dim=-1)
predicted_sentences = processor.batch_decode(predicted_ids)
for i, predicted_sentence in enumerate(predicted_sentences):
print("-" * 100)
print("Reference:", test_dataset[i]["sentence"])
print("Prediction:", predicted_sentence)
```
| Reference | Prediction |
| ------------- | ------------- |
| "SHE'LL BE ALL RIGHT." | SHELL BE ALL RIGHT |
| SIX | SIX |
| "ALL'S WELL THAT ENDS WELL." | ALLAS WELL THAT ENDS WELL |
| DO YOU MEAN IT? | W MEAN IT |
| THE NEW PATCH IS LESS INVASIVE THAN THE OLD ONE, BUT STILL CAUSES REGRESSIONS. | THE NEW PATCH IS LESS INVASIVE THAN THE OLD ONE BUT STILL CAUSES REGRESTION |
| HOW IS MOZILLA GOING TO HANDLE AMBIGUITIES LIKE QUEUE AND CUE? | HOW IS MOSILLA GOING TO BANDL AND BE WHIT IS LIKE QU AND QU |
| "I GUESS YOU MUST THINK I'M KINDA BATTY." | RUSTION AS HAME AK AN THE POT |
| NO ONE NEAR THE REMOTE MACHINE YOU COULD RING? | NO ONE NEAR THE REMOTE MACHINE YOU COULD RING |
| SAUCE FOR THE GOOSE IS SAUCE FOR THE GANDER. | SAUCE FOR THE GUCE IS SAUCE FOR THE GONDER |
| GROVES STARTED WRITING SONGS WHEN SHE WAS FOUR YEARS OLD. | GRAFS STARTED WRITING SONGS WHEN SHE WAS FOUR YEARS OLD |
## Evaluation
The model can be evaluated as follows on the English (en) test data of Common Voice.
```python
import torch
import re
import librosa
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
LANG_ID = "en"
MODEL_ID = "jonatasgrosman/wav2vec2-large-english"
DEVICE = "cuda"
CHARS_TO_IGNORE = [",", "?", "¿", ".", "!", "¡", ";", "；", ":", '""', "%", '"', "�", "ʿ", "·", "჻", "~", "՞",
"؟", "،", "।", "॥", "«", "»", "„", "“", "”", "「", "」", "‘", "’", "《", "》", "(", ")", "[", "]",
"{", "}", "=", "`", "_", "+", "<", ">", "…", "–", "°", "´", "ʾ", "‹", "›", "©", "®", "—", "→", "。",
"、", "﹂", "﹁", "‧", "～", "﹏", "，", "｛", "｝", "（", "）", "［", "］", "【", "】", "‥", "〽",
"『", "』", "〝", "〟", "⟨", "⟩", "〜", "：", "！", "？", "♪", "؛", "/", "\\", "º", "−", "^", "ʻ", "ˆ"]
test_dataset = load_dataset("common_voice", LANG_ID, split="test")
wer = load_metric("wer.py") # https://github.com/jonatasgrosman/wav2vec2-sprint/blob/main/wer.py
cer = load_metric("cer.py") # https://github.com/jonatasgrosman/wav2vec2-sprint/blob/main/cer.py
chars_to_ignore_regex = f"[{re.escape(''.join(CHARS_TO_IGNORE))}]"
processor = Wav2Vec2Processor.from_pretrained(MODEL_ID)
model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID)
model.to(DEVICE)
# Preprocessing the datasets.
# We need to read the audio files as arrays
def speech_file_to_array_fn(batch):
with warnings.catch_warnings():
warnings.simplefilter("ignore")
speech_array, sampling_rate = librosa.load(batch["path"], sr=16_000)
batch["speech"] = speech_array
batch["sentence"] = re.sub(chars_to_ignore_regex, "", batch["sentence"]).upper()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
# Preprocessing the datasets.
# We need to read the audio files as arrays
def evaluate(batch):
inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values.to(DEVICE), attention_mask=inputs.attention_mask.to(DEVICE)).logits
pred_ids = torch.argmax(logits, dim=-1)
batch["pred_strings"] = processor.batch_decode(pred_ids)
return batch
result = test_dataset.map(evaluate, batched=True, batch_size=8)
predictions = [x.upper() for x in result["pred_strings"]]
references = [x.upper() for x in result["sentence"]]
print(f"WER: {wer.compute(predictions=predictions, references=references, chunk_size=1000) * 100}")
print(f"CER: {cer.compute(predictions=predictions, references=references, chunk_size=1000) * 100}")
```
**Test Result**:
In the table below I report the Word Error Rate (WER) and the Character Error Rate (CER) of the model. I ran the evaluation script described above on other models as well. Note that the table below may show different results from those already reported, this may have been caused due to some specificity of the other evaluation scripts used.
| Model | WER | CER |
| ------------- | ------------- | ------------- |
| wav2vec2-large-xlsr-53-english | **18.98%** | **8.29%** |
| wav2vec2-large-xlsr-53-greek | 18.99% | 10.60% |
| wav2vec2-large-xlsr-53-hindi | 20.01% | 9.66% |
| wav2vec2-large-960h-lv60-english | 22.03% | 10.39% |
| wav2vec2-base-100h-lv60-english | 24.97% | 11.14% |
|
|
{"language": "en", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer", "cer"], "model-index": [{"name": "Wav2Vec2 English by Jonatas Grosman", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Common Voice en", "type": "common_voice", "args": "en"}, "metrics": [{"type": "wer", "value": 21.53, "name": "Test WER"}, {"type": "cer", "value": 9.66, "name": "Test CER"}]}]}]}
|
automatic-speech-recognition
|
harshit345/xlsr_wav2vec_english
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"en",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #en #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
Wav2vec2-Large-English
======================
Fine-tuned facebook/wav2vec2-large on English using the Common Voice.
When using this model, make sure that your speech input is sampled at 16kHz.
Usage
-----
The model can be used directly (without a language model) as follows...
Using the ASRecognition library:
Writing your own inference script:
Evaluation
----------
The model can be evaluated as follows on the English (en) test data of Common Voice.
Test Result:
In the table below I report the Word Error Rate (WER) and the Character Error Rate (CER) of the model. I ran the evaluation script described above on other models as well. Note that the table below may show different results from those already reported, this may have been caused due to some specificity of the other evaluation scripts used.
Model: wav2vec2-large-xlsr-53-english, WER: 18.98%, CER: 8.29%
Model: wav2vec2-large-xlsr-53-greek, WER: 18.99%, CER: 10.60%
Model: wav2vec2-large-xlsr-53-hindi, WER: 20.01%, CER: 9.66%
Model: wav2vec2-large-960h-lv60-english, WER: 22.03%, CER: 10.39%
Model: wav2vec2-base-100h-lv60-english, WER: 24.97%, CER: 11.14%
Model: , WER: , CER:
|
[] |
[
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #en #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n"
] |
[
77
] |
[
"passage: TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #en #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
## EsperBERTo: RoBERTa-like Language model trained on Esperanto
|
{"language": "eo", "thumbnail": "https://huggingface.co/blog/assets/01_how-to-train/EsperBERTo-thumbnail-v2.png", "widget": [{"text": "\u0108u vi paloras la <mask> Esperanto?"}]}
|
fill-mask
|
hashk1/EsperBERTo-malgranda
|
[
"transformers",
"pytorch",
"jax",
"roberta",
"fill-mask",
"eo",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"eo"
] |
TAGS
#transformers #pytorch #jax #roberta #fill-mask #eo #autotrain_compatible #endpoints_compatible #region-us
|
## EsperBERTo: RoBERTa-like Language model trained on Esperanto
|
[
"## EsperBERTo: RoBERTa-like Language model trained on Esperanto"
] |
[
"TAGS\n#transformers #pytorch #jax #roberta #fill-mask #eo #autotrain_compatible #endpoints_compatible #region-us \n",
"## EsperBERTo: RoBERTa-like Language model trained on Esperanto"
] |
[
42,
16
] |
[
"passage: TAGS\n#transformers #pytorch #jax #roberta #fill-mask #eo #autotrain_compatible #endpoints_compatible #region-us \n## EsperBERTo: RoBERTa-like Language model trained on Esperanto"
] |
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] |
null | null |
transformers
|
# Arabic Named Entity Recognition Model
Pretrained BERT-based ([arabic-bert-base](https://huggingface.co/asafaya/bert-base-arabic)) Named Entity Recognition model for Arabic.
The pre-trained model can recognize the following entities:
1. **PERSON**
- و هذا ما نفاه المعاون السياسي للرئيس ***نبيه بري*** ، النائب ***علي حسن خليل***
- لكن أوساط ***الحريري*** تعتبر أنه ضحى كثيرا في سبيل البلد
- و ستفقد الملكة ***إليزابيث الثانية*** بذلك سيادتها على واحدة من آخر ممالك الكومنولث
2. **ORGANIZATION**
- حسب أرقام ***البنك الدولي***
- أعلن ***الجيش العراقي***
- و نقلت وكالة ***رويترز*** عن ثلاثة دبلوماسيين في ***الاتحاد الأوروبي*** ، أن ***بلجيكا*** و ***إيرلندا*** و ***لوكسمبورغ*** تريد أيضاً مناقشة
- ***الحكومة الاتحادية*** و ***حكومة إقليم كردستان***
- و هو ما يثير الشكوك حول مشاركة النجم البرتغالي في المباراة المرتقبة أمام ***برشلونة*** الإسباني في
3. ***LOCATION***
- الجديد هو تمكين اللاجئين من “ مغادرة الجزيرة تدريجياً و بهدوء إلى ***أثينا*** ”
- ***جزيرة ساكيز*** تبعد 1 كم عن ***إزمير***
4. **DATE**
- ***غدا الجمعة***
- ***06 أكتوبر 2020***
- ***العام السابق***
5. **PRODUCT**
- عبر حسابه ب ***تطبيق “ إنستغرام ”***
- الجيل الثاني من ***نظارة الواقع الافتراضي أوكولوس كويست*** تحت اسم " ***أوكولوس كويست 2*** "
6. **COMPETITION**
- عدم المشاركة في ***بطولة فرنسا المفتوحة للتنس***
- في مباراة ***كأس السوبر الأوروبي***
7. **PRIZE**
- ***جائزة نوبل ل لآداب***
- الذي فاز ب ***جائزة “ إيمي ” لأفضل دور مساند***
8. **EVENT**
- تسجّل أغنية جديدة خاصة ب ***العيد الوطني السعودي***
- ***مهرجان المرأة يافوية*** في دورته الرابعة
9. **DISEASE**
- في مكافحة فيروس ***كورونا*** و عدد من الأمراض
- الأزمات المشابهة مثل “ ***انفلونزا الطيور*** ” و ” ***انفلونزا الخنازير***
## Example
[Find here a complete example to use this model](https://github.com/hatmimoha/arabic-ner)
## Training Corpus
The training corpus is made of 378.000 tokens (14.000 sentences) collected from the Web and annotated manually.
## Results
The results on a valid corpus made of 30.000 tokens shows an F-measure of ~87%.
|
{"language": "ar"}
|
token-classification
|
hatmimoha/arabic-ner
|
[
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"token-classification",
"ar",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"ar"
] |
TAGS
#transformers #pytorch #tf #jax #safetensors #bert #token-classification #ar #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Arabic Named Entity Recognition Model
Pretrained BERT-based (arabic-bert-base) Named Entity Recognition model for Arabic.
The pre-trained model can recognize the following entities:
1. PERSON
- و هذا ما نفاه المعاون السياسي للرئيس *نبيه بري* ، النائب *علي حسن خليل*
- لكن أوساط *الحريري* تعتبر أنه ضحى كثيرا في سبيل البلد
- و ستفقد الملكة *إليزابيث الثانية* بذلك سيادتها على واحدة من آخر ممالك الكومنولث
2. ORGANIZATION
- حسب أرقام *البنك الدولي*
- أعلن *الجيش العراقي*
- و نقلت وكالة *رويترز* عن ثلاثة دبلوماسيين في *الاتحاد الأوروبي* ، أن *بلجيكا* و *إيرلندا* و *لوكسمبورغ* تريد أيضاً مناقشة
- *الحكومة الاتحادية* و *حكومة إقليم كردستان*
- و هو ما يثير الشكوك حول مشاركة النجم البرتغالي في المباراة المرتقبة أمام *برشلونة* الإسباني في
3. *LOCATION*
- الجديد هو تمكين اللاجئين من “ مغادرة الجزيرة تدريجياً و بهدوء إلى *أثينا* ”
- *جزيرة ساكيز* تبعد 1 كم عن *إزمير*
4. DATE
- *غدا الجمعة*
- *06 أكتوبر 2020*
- *العام السابق*
5. PRODUCT
- عبر حسابه ب *تطبيق “ إنستغرام ”*
- الجيل الثاني من *نظارة الواقع الافتراضي أوكولوس كويست* تحت اسم " *أوكولوس كويست 2* "
6. COMPETITION
- عدم المشاركة في *بطولة فرنسا المفتوحة للتنس*
- في مباراة *كأس السوبر الأوروبي*
7. PRIZE
- *جائزة نوبل ل لآداب*
- الذي فاز ب *جائزة “ إيمي ” لأفضل دور مساند*
8. EVENT
- تسجّل أغنية جديدة خاصة ب *العيد الوطني السعودي*
- *مهرجان المرأة يافوية* في دورته الرابعة
9. DISEASE
- في مكافحة فيروس *كورونا* و عدد من الأمراض
- الأزمات المشابهة مثل “ *انفلونزا الطيور* ” و ” *انفلونزا الخنازير*
## Example
Find here a complete example to use this model
## Training Corpus
The training corpus is made of 378.000 tokens (14.000 sentences) collected from the Web and annotated manually.
## Results
The results on a valid corpus made of 30.000 tokens shows an F-measure of ~87%.
|
[
"# Arabic Named Entity Recognition Model\n\nPretrained BERT-based (arabic-bert-base) Named Entity Recognition model for Arabic.\n\nThe pre-trained model can recognize the following entities:\n1. PERSON\n\n- و هذا ما نفاه المعاون السياسي للرئيس *نبيه بري* ، النائب *علي حسن خليل* \n\n- لكن أوساط *الحريري* تعتبر أنه ضحى كثيرا في سبيل البلد \n\n- و ستفقد الملكة *إليزابيث الثانية* بذلك سيادتها على واحدة من آخر ممالك الكومنولث \n\n2. ORGANIZATION\n\n- حسب أرقام *البنك الدولي* \n\n- أعلن *الجيش العراقي* \n\n- و نقلت وكالة *رويترز* عن ثلاثة دبلوماسيين في *الاتحاد الأوروبي* ، أن *بلجيكا* و *إيرلندا* و *لوكسمبورغ* تريد أيضاً مناقشة \n\n- *الحكومة الاتحادية* و *حكومة إقليم كردستان* \n\n- و هو ما يثير الشكوك حول مشاركة النجم البرتغالي في المباراة المرتقبة أمام *برشلونة* الإسباني في \n\n\n3. *LOCATION*\n\n- الجديد هو تمكين اللاجئين من “ مغادرة الجزيرة تدريجياً و بهدوء إلى *أثينا* ” \n\n- *جزيرة ساكيز* تبعد 1 كم عن *إزمير* \n\n\n4. DATE\n\n- *غدا الجمعة* \n\n- *06 أكتوبر 2020* \n\n- *العام السابق* \n\n\n5. PRODUCT\n\n- عبر حسابه ب *تطبيق “ إنستغرام ”* \n\n- الجيل الثاني من *نظارة الواقع الافتراضي أوكولوس كويست* تحت اسم \" *أوكولوس كويست 2* \" \n\n\n6. COMPETITION\n\n- عدم المشاركة في *بطولة فرنسا المفتوحة للتنس* \n\n- في مباراة *كأس السوبر الأوروبي* \n\n7. PRIZE\n\n- *جائزة نوبل ل لآداب*\n\n- الذي فاز ب *جائزة “ إيمي ” لأفضل دور مساند*\n\n8. EVENT\n\n- تسجّل أغنية جديدة خاصة ب *العيد الوطني السعودي*\n\n- *مهرجان المرأة يافوية* في دورته الرابعة \n\n9. DISEASE\n\n- في مكافحة فيروس *كورونا* و عدد من الأمراض \n\n- الأزمات المشابهة مثل “ *انفلونزا الطيور* ” و ” *انفلونزا الخنازير*",
"## Example\n\nFind here a complete example to use this model",
"## Training Corpus\n\nThe training corpus is made of 378.000 tokens (14.000 sentences) collected from the Web and annotated manually.",
"## Results\n\nThe results on a valid corpus made of 30.000 tokens shows an F-measure of ~87%."
] |
[
"TAGS\n#transformers #pytorch #tf #jax #safetensors #bert #token-classification #ar #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Arabic Named Entity Recognition Model\n\nPretrained BERT-based (arabic-bert-base) Named Entity Recognition model for Arabic.\n\nThe pre-trained model can recognize the following entities:\n1. PERSON\n\n- و هذا ما نفاه المعاون السياسي للرئيس *نبيه بري* ، النائب *علي حسن خليل* \n\n- لكن أوساط *الحريري* تعتبر أنه ضحى كثيرا في سبيل البلد \n\n- و ستفقد الملكة *إليزابيث الثانية* بذلك سيادتها على واحدة من آخر ممالك الكومنولث \n\n2. ORGANIZATION\n\n- حسب أرقام *البنك الدولي* \n\n- أعلن *الجيش العراقي* \n\n- و نقلت وكالة *رويترز* عن ثلاثة دبلوماسيين في *الاتحاد الأوروبي* ، أن *بلجيكا* و *إيرلندا* و *لوكسمبورغ* تريد أيضاً مناقشة \n\n- *الحكومة الاتحادية* و *حكومة إقليم كردستان* \n\n- و هو ما يثير الشكوك حول مشاركة النجم البرتغالي في المباراة المرتقبة أمام *برشلونة* الإسباني في \n\n\n3. *LOCATION*\n\n- الجديد هو تمكين اللاجئين من “ مغادرة الجزيرة تدريجياً و بهدوء إلى *أثينا* ” \n\n- *جزيرة ساكيز* تبعد 1 كم عن *إزمير* \n\n\n4. DATE\n\n- *غدا الجمعة* \n\n- *06 أكتوبر 2020* \n\n- *العام السابق* \n\n\n5. PRODUCT\n\n- عبر حسابه ب *تطبيق “ إنستغرام ”* \n\n- الجيل الثاني من *نظارة الواقع الافتراضي أوكولوس كويست* تحت اسم \" *أوكولوس كويست 2* \" \n\n\n6. COMPETITION\n\n- عدم المشاركة في *بطولة فرنسا المفتوحة للتنس* \n\n- في مباراة *كأس السوبر الأوروبي* \n\n7. PRIZE\n\n- *جائزة نوبل ل لآداب*\n\n- الذي فاز ب *جائزة “ إيمي ” لأفضل دور مساند*\n\n8. EVENT\n\n- تسجّل أغنية جديدة خاصة ب *العيد الوطني السعودي*\n\n- *مهرجان المرأة يافوية* في دورته الرابعة \n\n9. DISEASE\n\n- في مكافحة فيروس *كورونا* و عدد من الأمراض \n\n- الأزمات المشابهة مثل “ *انفلونزا الطيور* ” و ” *انفلونزا الخنازير*",
"## Example\n\nFind here a complete example to use this model",
"## Training Corpus\n\nThe training corpus is made of 378.000 tokens (14.000 sentences) collected from the Web and annotated manually.",
"## Results\n\nThe results on a valid corpus made of 30.000 tokens shows an F-measure of ~87%."
] |
[
54,
484,
12,
30,
24
] |
[
"passage: TAGS\n#transformers #pytorch #tf #jax #safetensors #bert #token-classification #ar #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
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] |
null | null |
transformers
|
<!-- 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-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8508
- Matthews Correlation: 0.5452
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5221 | 1.0 | 535 | 0.5370 | 0.4246 |
| 0.3462 | 2.0 | 1070 | 0.5157 | 0.5183 |
| 0.2332 | 3.0 | 1605 | 0.6324 | 0.5166 |
| 0.1661 | 4.0 | 2140 | 0.7616 | 0.5370 |
| 0.1263 | 5.0 | 2675 | 0.8508 | 0.5452 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "distilbert-base-uncased-finetuned-cola", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "cola"}, "metrics": [{"type": "matthews_correlation", "value": 0.5451837431775948, "name": "Matthews Correlation"}]}]}]}
|
text-classification
|
hchc/distilbert-base-uncased-finetuned-cola
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
distilbert-base-uncased-finetuned-cola
======================================
This model is a fine-tuned version of distilbert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8508
* Matthews Correlation: 0.5452
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: 5
### Training results
### Framework versions
* Transformers 4.11.3
* Pytorch 1.9.0+cu111
* Datasets 1.14.0
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.9.0+cu111\n* Datasets 1.14.0\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.9.0+cu111\n* Datasets 1.14.0\n* Tokenizers 0.10.3"
] |
[
67,
98,
4,
34
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5### Training results### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.9.0+cu111\n* Datasets 1.14.0\n* Tokenizers 0.10.3"
] |
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null | null |
transformers
|
<!-- 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-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8657
- Matthews Correlation: 0.5472
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.527 | 1.0 | 535 | 0.5545 | 0.3893 |
| 0.3518 | 2.0 | 1070 | 0.5170 | 0.4970 |
| 0.2448 | 3.0 | 1605 | 0.6734 | 0.5142 |
| 0.1779 | 4.0 | 2140 | 0.7728 | 0.5466 |
| 0.1339 | 5.0 | 2675 | 0.8657 | 0.5472 |
### Framework versions
- Transformers 4.12.3
- Pytorch 1.9.0+cu111
- Datasets 1.15.1
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "distilbert-base-uncased-finetuned-cola", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "cola"}, "metrics": [{"type": "matthews_correlation", "value": 0.5471613867597194, "name": "Matthews Correlation"}]}]}]}
|
text-classification
|
hcjang1987/distilbert-base-uncased-finetuned-cola
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
distilbert-base-uncased-finetuned-cola
======================================
This model is a fine-tuned version of distilbert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8657
* Matthews Correlation: 0.5472
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: 5
### Training results
### Framework versions
* Transformers 4.12.3
* Pytorch 1.9.0+cu111
* Datasets 1.15.1
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.12.3\n* Pytorch 1.9.0+cu111\n* Datasets 1.15.1\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.12.3\n* Pytorch 1.9.0+cu111\n* Datasets 1.15.1\n* Tokenizers 0.10.3"
] |
[
67,
98,
4,
34
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5### Training results### Framework versions\n\n\n* Transformers 4.12.3\n* Pytorch 1.9.0+cu111\n* Datasets 1.15.1\n* Tokenizers 0.10.3"
] |
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] |
null | null |
transformers
|
<!-- 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-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2131
## 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
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.2672 | 1.0 | 5533 | 1.2131 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.6
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "distilbert-base-uncased-finetuned-squad", "results": []}]}
|
question-answering
|
hcy11/distilbert-base-uncased-finetuned-squad
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
distilbert-base-uncased-finetuned-squad
=======================================
This model is a fine-tuned version of distilbert-base-uncased on the squad dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2131
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
### Training results
### Framework versions
* Transformers 4.16.2
* Pytorch 1.10.0+cu111
* Datasets 1.18.3
* Tokenizers 0.11.6
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.16.2\n* Pytorch 1.10.0+cu111\n* Datasets 1.18.3\n* Tokenizers 0.11.6"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.16.2\n* Pytorch 1.10.0+cu111\n* Datasets 1.18.3\n* Tokenizers 0.11.6"
] |
[
56,
98,
4,
35
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1### Training results### Framework versions\n\n\n* Transformers 4.16.2\n* Pytorch 1.10.0+cu111\n* Datasets 1.18.3\n* Tokenizers 0.11.6"
] |
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