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facebook/deit-base-patch16-384 | facebook | 2022-07-13T11:41:03Z | 349 | 1 | transformers | [
"transformers",
"pytorch",
"tf",
"vit",
"image-classification",
"dataset:imagenet-1k",
"arxiv:2012.12877",
"arxiv:2006.03677",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| image-classification | 2022-03-02T23:29:05Z | ---
license: apache-2.0
tags:
- image-classification
datasets:
- imagenet-1k
---
# Data-efficient Image Transformer (base-sized model)
Data-efficient Image Transformer (DeiT) model pre-trained and fine-tuned on ImageNet-1k (1 million images, 1,000 classes) at resolution 384x384. It was first introduced in the paper [Training data-efficient image transformers & distillation through attention](https://arxiv.org/abs/2012.12877) by Touvron et al. and first released in [this repository](https://github.com/facebookresearch/deit). However, the weights were converted from the [timm repository](https://github.com/rwightman/pytorch-image-models) by Ross Wightman.
Disclaimer: The team releasing DeiT did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
This model is actually a more efficiently trained Vision Transformer (ViT).
The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pre-trained at resolution 224 and fine-tuned at resolution 384 on a large collection of images in a supervised fashion, namely ImageNet-1k.
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=facebook/deit) to look for
fine-tuned versions on a task that interests you.
### How to use
Since this model is a more efficiently trained ViT model, you can plug it into ViTModel or ViTForImageClassification. Note that the model expects the data to be prepared using DeiTFeatureExtractor. Here we use AutoFeatureExtractor, which will automatically use the appropriate feature extractor given the model name.
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 AutoFeatureExtractor, 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 = AutoFeatureExtractor.from_pretrained('facebook/deit-base-patch16-384')
model = ViTForImageClassification.from_pretrained('facebook/deit-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.
## Training data
The ViT model was pretrained on [ImageNet-1k](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/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L78).
At inference time, images are resized/rescaled to the same resolution (438x438), center-cropped at 384x384 and normalized across the RGB channels with the ImageNet mean and standard deviation.
### Pretraining
The model was trained on a single 8-GPU node for 3 days. Pre-training resolution is 224. For all hyperparameters (such as batch size and learning rate) we refer to table 9 of the original paper.
## Evaluation results
| Model | ImageNet top-1 accuracy | ImageNet top-5 accuracy | # params | URL |
|---------------------------------------|-------------------------|-------------------------|----------|------------------------------------------------------------------|
| DeiT-tiny | 72.2 | 91.1 | 5M | https://huggingface.co/facebook/deit-tiny-patch16-224 |
| DeiT-small | 79.9 | 95.0 | 22M | https://huggingface.co/facebook/deit-small-patch16-224 |
| DeiT-base | 81.8 | 95.6 | 86M | https://huggingface.co/facebook/deit-base-patch16-224 |
| DeiT-tiny distilled | 74.5 | 91.9 | 6M | https://huggingface.co/facebook/deit-tiny-distilled-patch16-224 |
| DeiT-small distilled | 81.2 | 95.4 | 22M | https://huggingface.co/facebook/deit-small-distilled-patch16-224 |
| DeiT-base distilled | 83.4 | 96.5 | 87M | https://huggingface.co/facebook/deit-base-distilled-patch16-224 |
| **DeiT-base 384** | **82.9** | **96.2** | **87M** | **https://huggingface.co/facebook/deit-base-patch16-384** |
| DeiT-base distilled 384 (1000 epochs) | 85.2 | 97.2 | 88M | https://huggingface.co/facebook/deit-base-distilled-patch16-384 |
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{touvron2021training,
title={Training data-efficient image transformers & distillation through attention},
author={Hugo Touvron and Matthieu Cord and Matthijs Douze and Francisco Massa and Alexandre Sablayrolles and Hervé Jégou},
year={2021},
eprint={2012.12877},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
```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}
}
``` |
facebook/deit-base-patch16-224 | facebook | 2022-07-13T11:40:44Z | 144,060 | 13 | transformers | [
"transformers",
"pytorch",
"tf",
"vit",
"image-classification",
"dataset:imagenet-1k",
"arxiv:2012.12877",
"arxiv:2006.03677",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| image-classification | 2022-03-02T23:29:05Z | ---
license: apache-2.0
tags:
- image-classification
datasets:
- imagenet-1k
---
# Data-efficient Image Transformer (base-sized model)
Data-efficient Image Transformer (DeiT) model pre-trained and fine-tuned on ImageNet-1k (1 million images, 1,000 classes) at resolution 224x224. It was first introduced in the paper [Training data-efficient image transformers & distillation through attention](https://arxiv.org/abs/2012.12877) by Touvron et al. and first released in [this repository](https://github.com/facebookresearch/deit). However, the weights were converted from the [timm repository](https://github.com/rwightman/pytorch-image-models) by Ross Wightman.
Disclaimer: The team releasing DeiT did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
This model is actually a more efficiently trained Vision Transformer (ViT).
The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pre-trained and fine-tuned on a large collection of images in a supervised fashion, namely ImageNet-1k, 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.
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=facebook/deit) to look for
fine-tuned versions on a task that interests you.
### How to use
Since this model is a more efficiently trained ViT model, you can plug it into ViTModel or ViTForImageClassification. Note that the model expects the data to be prepared using DeiTFeatureExtractor. Here we use AutoFeatureExtractor, which will automatically use the appropriate feature extractor given the model name.
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 AutoFeatureExtractor, 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 = AutoFeatureExtractor.from_pretrained('facebook/deit-base-patch16-224')
model = ViTForImageClassification.from_pretrained('facebook/deit-base-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.
## Training data
The ViT model was pretrained on [ImageNet-1k](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/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L78).
At inference time, images are resized/rescaled to the same resolution (256x256), center-cropped at 224x224 and normalized across the RGB channels with the ImageNet mean and standard deviation.
### Pretraining
The model was trained on a single 8-GPU node for 3 days. Training resolution is 224. For all hyperparameters (such as batch size and learning rate) we refer to table 9 of the original paper.
## Evaluation results
| Model | ImageNet top-1 accuracy | ImageNet top-5 accuracy | # params | URL |
|---------------------------------------|-------------------------|-------------------------|----------|------------------------------------------------------------------|
| DeiT-tiny | 72.2 | 91.1 | 5M | https://huggingface.co/facebook/deit-tiny-patch16-224 |
| DeiT-small | 79.9 | 95.0 | 22M | https://huggingface.co/facebook/deit-small-patch16-224 |
| **DeiT-base** | **81.8** | **95.6** | **86M** | **https://huggingface.co/facebook/deit-base-patch16-224** |
| DeiT-tiny distilled | 74.5 | 91.9 | 6M | https://huggingface.co/facebook/deit-tiny-distilled-patch16-224 |
| DeiT-small distilled | 81.2 | 95.4 | 22M | https://huggingface.co/facebook/deit-small-distilled-patch16-224 |
| DeiT-base distilled | 83.4 | 96.5 | 87M | https://huggingface.co/facebook/deit-base-distilled-patch16-224 |
| DeiT-base 384 | 82.9 | 96.2 | 87M | https://huggingface.co/facebook/deit-base-patch16-384 |
| DeiT-base distilled 384 (1000 epochs) | 85.2 | 97.2 | 88M | https://huggingface.co/facebook/deit-base-distilled-patch16-384 |
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{touvron2021training,
title={Training data-efficient image transformers & distillation through attention},
author={Hugo Touvron and Matthieu Cord and Matthijs Douze and Francisco Massa and Alexandre Sablayrolles and Hervé Jégou},
year={2021},
eprint={2012.12877},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
```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}
}
``` |
facebook/deit-base-distilled-patch16-224 | facebook | 2022-07-13T11:39:38Z | 16,934 | 23 | transformers | [
"transformers",
"pytorch",
"tf",
"deit",
"image-classification",
"vision",
"dataset:imagenet",
"arxiv:2012.12877",
"arxiv:2006.03677",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| image-classification | 2022-03-02T23:29:05Z | ---
license: apache-2.0
tags:
- image-classification
- vision
datasets:
- imagenet
---
# Distilled Data-efficient Image Transformer (base-sized model)
Distilled data-efficient Image Transformer (DeiT) model pre-trained and fine-tuned on ImageNet-1k (1 million images, 1,000 classes) at resolution 224x224. It was first introduced in the paper [Training data-efficient image transformers & distillation through attention](https://arxiv.org/abs/2012.12877) by Touvron et al. and first released in [this repository](https://github.com/facebookresearch/deit). However, the weights were converted from the [timm repository](https://github.com/rwightman/pytorch-image-models) by Ross Wightman.
Disclaimer: The team releasing DeiT did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
This model is a distilled Vision Transformer (ViT). It uses a distillation token, besides the class token, to effectively learn from a teacher (CNN) during both pre-training and fine-tuning. The distillation token is learned through backpropagation, by interacting with the class ([CLS]) and patch tokens through the self-attention layers.
Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded.
## Intended uses & limitations
You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=facebook/deit) to look for
fine-tuned versions on a task that interests you.
### How to use
Since this model is a distilled ViT model, you can plug it into DeiTModel, DeiTForImageClassification or DeiTForImageClassificationWithTeacher. Note that the model expects the data to be prepared using DeiTFeatureExtractor. Here we use AutoFeatureExtractor, which will automatically use the appropriate feature extractor given the model name.
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 AutoFeatureExtractor, DeiTForImageClassificationWithTeacher
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 = AutoFeatureExtractor.from_pretrained('facebook/deit-base-distilled-patch16-224')
model = DeiTForImageClassificationWithTeacher.from_pretrained('facebook/deit-base-distilled-patch16-224')
inputs = feature_extractor(images=image, return_tensors="pt")
# forward pass
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.
## Training data
This model was pretrained and fine-tuned with distillation on [ImageNet-1k](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/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L78).
At inference time, images are resized/rescaled to the same resolution (256x256), center-cropped at 224x224 and normalized across the RGB channels with the ImageNet mean and standard deviation.
### Pretraining
The model was trained on a single 8-GPU node for 3 days. Training resolution is 224. For all hyperparameters (such as batch size and learning rate) we refer to table 9 of the original paper.
## Evaluation results
| Model | ImageNet top-1 accuracy | ImageNet top-5 accuracy | # params | URL |
|---------------------------------------|-------------------------|-------------------------|----------|------------------------------------------------------------------|
| DeiT-tiny | 72.2 | 91.1 | 5M | https://huggingface.co/facebook/deit-tiny-patch16-224 |
| DeiT-small | 79.9 | 95.0 | 22M | https://huggingface.co/facebook/deit-small-patch16-224 |
| DeiT-base | 81.8 | 95.6 | 86M | https://huggingface.co/facebook/deit-base-patch16-224 |
| DeiT-tiny distilled | 74.5 | 91.9 | 6M | https://huggingface.co/facebook/deit-tiny-distilled-patch16-224 |
| DeiT-small distilled | 81.2 | 95.4 | 22M | https://huggingface.co/facebook/deit-small-distilled-patch16-224 |
| **DeiT-base distilled** | **83.4** | **96.5** | **87M** | **https://huggingface.co/facebook/deit-base-distilled-patch16-224** |
| DeiT-base 384 | 82.9 | 96.2 | 87M | https://huggingface.co/facebook/deit-base-patch16-384 |
| DeiT-base distilled 384 (1000 epochs) | 85.2 | 97.2 | 88M | https://huggingface.co/facebook/deit-base-distilled-patch16-384 |
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{touvron2021training,
title={Training data-efficient image transformers & distillation through attention},
author={Hugo Touvron and Matthieu Cord and Matthijs Douze and Francisco Massa and Alexandre Sablayrolles and Hervé Jégou},
year={2021},
eprint={2012.12877},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
```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}
}
``` |
matjesg/deepflash2_demo | matjesg | 2022-07-13T10:54:35Z | 0 | 2 | null | [
"onnx",
"image-segmentation",
"semantic-segmentation",
"deepflash2",
"arxiv:2111.06693",
"license:apache-2.0",
"region:us"
]
| image-segmentation | 2022-05-31T09:43:39Z | ---
tags:
- image-segmentation
- semantic-segmentation
- deepflash2
license: apache-2.0
datasets:
- "cFOS in HC"
- "YFP in CTX"
---
# Demo models for
**Try in [Hugging Face Spaces](https://huggingface.co/spaces/matjesg/deepflash2)** 🤗🤗🤗
- **Task**: Image Segmentation / Semantic Segmentation
- **Paper**: The preprint of our paper is available on [arXiv](https://arxiv.org/pdf/2111.06693.pdf)
- **Data**: The cFOS in HC dataset ([Article](https://doi.org/10.7554/eLife.59780), [Data](https://doi.org/10.5061/dryad.4b8gtht9d)) describes the indirect immunofluorescent labeling of the transcription factor cFOS in different subregions of the hippocampus after behavioral testing of the mice.
- **Library**: See [github](https://github.com/matjesg/deepflash2/)
|
fxmarty/20220713-h10m20s05_example_conll2003 | fxmarty | 2022-07-13T10:20:11Z | 0 | 0 | null | [
"tensorboard",
"distilbert",
"token-classification",
"dataset:conll2003",
"region:us"
]
| token-classification | 2022-07-13T10:20:05Z | ---
pipeline_tag: token-classification
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
tags:
- distilbert
---
**task**: `token-classification`
**Backend:** `sagemaker-training`
**Backend args:** `{'instance_type': 'ml.g4dn.2xlarge', 'supported_instructions': None}`
**Number of evaluation samples:** `All dataset`
Fixed parameters:
* **model_name_or_path**: `elastic/distilbert-base-uncased-finetuned-conll03-english`
* **dataset**:
* **path**: `conll2003`
* **eval_split**: `validation`
* **data_keys**: `{'primary': 'tokens'}`
* **ref_keys**: `['ner_tags']`
* **calibration_split**: `train`
* **quantization_approach**: `static`
* **operators_to_quantize**: `['Add', 'MatMul']`
* **per_channel**: `False`
* **calibration**:
* **method**: `minmax`
* **num_calibration_samples**: `100`
* **framework**: `onnxruntime`
* **framework_args**:
* **opset**: `11`
* **optimization_level**: `1`
* **aware_training**: `False`
Benchmarked parameters:
* **node_exclusion**: `[]`, `['layernorm', 'gelu', 'residual', 'gather', 'softmax']`
# Evaluation
## Non-time metrics
| node_exclusion | | precision (original) | precision (optimized) | | recall (original) | recall (optimized) | | f1 (original) | f1 (optimized) | | accuracy (original) | accuracy (optimized) |
| :------------------------------------------------------: | :-: | :------------------: | :-------------------: | :-: | :---------------: | :----------------: | :-: | :-----------: | :------------: | :-: | :-----------------: | :------------------: |
| `['layernorm', 'gelu', 'residual', 'gather', 'softmax']` | \| | 0.936 | 0.904 | \| | 0.944 | 0.921 | \| | 0.940 | 0.912 | \| | 0.988 | 0.984 |
| `[]` | \| | 0.936 | 0.065 | \| | 0.944 | 0.243 | \| | 0.940 | 0.103 | \| | 0.988 | 0.357 |
## Time metrics
Time benchmarks were run for 15 seconds per config.
Below, time metrics for batch size = 4, input length = 64.
| node_exclusion | | latency_mean (original, ms) | latency_mean (optimized, ms) | | throughput (original, /s) | throughput (optimized, /s) |
| :------------------------------------------------------: | :-: | :-------------------------: | :--------------------------: | :-: | :-----------------------: | :------------------------: |
| `['layernorm', 'gelu', 'residual', 'gather', 'softmax']` | \| | 120.53 | 46.41 | \| | 8.33 | 21.60 |
| `[]` | \| | 119.97 | 59.50 | \| | 8.40 | 16.87 |
|
nawta/wav2vec2-onomatopoeia-finetune_smalldata_ESC50pretrained_2 | nawta | 2022-07-13T10:11:43Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2022-07-13T09:25:20Z | ---
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-onomatopoeia-finetune_smalldata_ESC50pretrained_2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-onomatopoeia-finetune_smalldata_ESC50pretrained_2
This model is a fine-tuned version of [/root/workspace/wav2vec2-pretrained_with_ESC50_10000epochs_32batch_2022-07-09_22-16-46/pytorch_model.bin](https://huggingface.co//root/workspace/wav2vec2-pretrained_with_ESC50_10000epochs_32batch_2022-07-09_22-16-46/pytorch_model.bin) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6235
- Cer: 0.8973
## 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: 64
- eval_batch_size: 16
- 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 | Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 5.0097 | 23.81 | 500 | 2.6235 | 0.8973 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.1+cu111
- Datasets 1.13.3
- Tokenizers 0.10.3
|
hugginglearners/flowers_101_convnext_model | hugginglearners | 2022-07-13T09:58:32Z | 0 | 3 | fastai | [
"fastai",
"image-classification",
"region:us"
]
| image-classification | 2022-07-04T00:50:48Z | ---
tags:
- fastai
- image-classification
---
# Model card
## Model description
This model has been trained with convnext_tiny_in22k with [Flowers-101 datasets in Kaggle](https://www.kaggle.com/competitions/tpu-getting-started).
**Useful graphs logged with wandb**
## Intended uses & limitations
- The model can be used be for classifying flowers only.
**Limitations**
- Even if the picture uploaded is not of a flower, you can can notice [it will be predicted as of flower](https://www.kaggle.com/competitions/tpu-getting-started).
- The model on validation dataset has accuracy of 94.23%
## Training and evaluation data
- The models has been trained and evaluated with [Flowers-101 datasets in Kaggle](https://www.kaggle.com/competitions/tpu-getting-started).
- We used a Random Splitter to train and evaluate data
|
casasdorjunior/t5-small-finetuned-cc-news-es-titles | casasdorjunior | 2022-07-13T08:52:55Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:cc-news-es-titles",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-07-13T07:38:26Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- cc-news-es-titles
metrics:
- rouge
model-index:
- name: t5-small-finetuned-cc-news-es-titles
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: cc-news-es-titles
type: cc-news-es-titles
args: default
metrics:
- name: Rouge1
type: rouge
value: 16.701
---
<!-- 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-cc-news-es-titles
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the cc-news-es-titles dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6383
- Rouge1: 16.701
- Rouge2: 4.1265
- Rougel: 14.8175
- Rougelsum: 14.8193
- Gen Len: 18.9159
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:-------:|:---------:|:-------:|
| 2.8439 | 1.0 | 23133 | 2.6383 | 16.701 | 4.1265 | 14.8175 | 14.8193 | 18.9159 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
loz/Test | loz | 2022-07-13T08:11:37Z | 0 | 0 | null | [
"region:us"
]
| null | 2022-07-13T08:08:54Z | me on a bike
going into the sunset
at night
with my dog running along side me |
Loc/lucky-model | Loc | 2022-07-13T07:06:05Z | 53 | 0 | transformers | [
"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",
"region:us"
]
| image-classification | 2022-07-13T03:43:48Z | ---
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
---
# 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 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-patch16-224')
model = ViTForImageClassification.from_pretrained('google/vit-base-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])
```
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}
}
``` |
jason9693/soongsil-bert-small | jason9693 | 2022-07-13T05:33:10Z | 10 | 0 | transformers | [
"transformers",
"pytorch",
"jax",
"roberta",
"fill-mask",
"ko",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| fill-mask | 2022-03-02T23:29:05Z | ---
language: ko
widget:
- 숭실대학교 글로벌<mask>학부
--- |
AdiKompella/testpyramidsrnd | AdiKompella | 2022-07-13T05:18:15Z | 4 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
]
| reinforcement-learning | 2022-07-13T05:18:07Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
library_name: ml-agents
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids
2. Step 1: Write your model_id: AdiKompella/testpyramidsrnd
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
huggingtweets/majigglydoobers | huggingtweets | 2022-07-13T02:58:05Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2022-07-13T02:56:45Z | ---
language: en
thumbnail: http://www.huggingtweets.com/majigglydoobers/1657681081092/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1542204712455241729/6E7rxSrt_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">doobers 👻❤️🩹</div>
<div style="text-align: center; font-size: 14px;">@majigglydoobers</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from doobers 👻❤️🩹.
| Data | doobers 👻❤️🩹 |
| --- | --- |
| Tweets downloaded | 3249 |
| Retweets | 2046 |
| Short tweets | 199 |
| Tweets kept | 1004 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/36h6xok5/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @majigglydoobers's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/emkivtny) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/emkivtny/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/majigglydoobers')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/kitsune__spirit | huggingtweets | 2022-07-13T02:51:17Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2022-03-02T23:29:05Z | ---
language: en
thumbnail: http://www.huggingtweets.com/kitsune__spirit/1657680673292/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1523268231833739266/foV-CaZh_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">KitsuneSpirit Mei 💝🦊「 YOKOMESHI 」</div>
<div style="text-align: center; font-size: 14px;">@kitsune__spirit</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from KitsuneSpirit Mei 💝🦊「 YOKOMESHI 」.
| Data | KitsuneSpirit Mei 💝🦊「 YOKOMESHI 」 |
| --- | --- |
| Tweets downloaded | 3248 |
| Retweets | 67 |
| Short tweets | 820 |
| Tweets kept | 2361 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3uiy3sjw/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @kitsune__spirit's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1hdne87l) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1hdne87l/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/kitsune__spirit')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
ariesutiono/scibert-lm-const-finetuned-20 | ariesutiono | 2022-07-13T00:15:55Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"dataset:conll2003",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| fill-mask | 2022-07-12T23:32:22Z | ---
tags:
- generated_from_trainer
datasets:
- conll2003
model-index:
- name: scibert-lm-const-finetuned-20
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# scibert-lm-const-finetuned-20
This model is a fine-tuned version of [allenai/scibert_scivocab_cased](https://huggingface.co/allenai/scibert_scivocab_cased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0099
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.6081 | 1.0 | 118 | 2.9156 |
| 2.7954 | 2.0 | 236 | 2.5940 |
| 2.5762 | 3.0 | 354 | 2.5017 |
| 2.4384 | 4.0 | 472 | 2.3923 |
| 2.3391 | 5.0 | 590 | 2.2996 |
| 2.2417 | 6.0 | 708 | 2.3180 |
| 2.2161 | 7.0 | 826 | 2.2336 |
| 2.1918 | 8.0 | 944 | 2.2465 |
| 2.1494 | 9.0 | 1062 | 2.1871 |
| 2.1215 | 10.0 | 1180 | 2.1566 |
| 2.1015 | 11.0 | 1298 | 2.1849 |
| 2.05 | 12.0 | 1416 | 2.1092 |
| 2.0653 | 13.0 | 1534 | 2.2221 |
| 2.0261 | 14.0 | 1652 | 2.1572 |
| 2.0117 | 15.0 | 1770 | 2.1452 |
| 1.9845 | 16.0 | 1888 | 2.1433 |
| 1.9791 | 17.0 | 2006 | 2.1225 |
| 1.9979 | 18.0 | 2124 | 2.0777 |
| 1.9688 | 19.0 | 2242 | 2.1765 |
| 1.9873 | 20.0 | 2360 | 2.0099 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
hugginglearners/multi-object-classification | hugginglearners | 2022-07-13T00:14:55Z | 0 | 2 | fastai | [
"fastai",
"image-classification",
"region:us"
]
| image-classification | 2022-07-04T04:34:10Z | ---
tags:
- fastai
- image-classification
---
## Model description
This repo contains the trained model for Multi-object classification
Full credits go to [Nhu Hoang](https://www.linkedin.com/in/nhu-hoang/)
Motivation: Classifying multiple objects is a challenging task without using an object detection algorithm. This model was trained on resnet34 backbone and achieved a good accuracy.
## Training and evaluation data
### Training hyperparameters
The following hyperparameters were used during training:
| Hyperparameters | Value |
| :-- | :-- |
| name | Adam |
| learning_rate | 3e-3 |
| training_precision | float16 |
|
AntiSquid/Reinforce-pix-5 | AntiSquid | 2022-07-12T23:21:37Z | 0 | 0 | null | [
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-07-12T23:21:12Z | ---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-pix-5
results:
- metrics:
- type: mean_reward
value: 20.30 +/- 17.44
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
|
AntiSquid/Reinforce-model-666 | AntiSquid | 2022-07-12T21:52:02Z | 0 | 0 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-07-12T21:51:51Z | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-model-666
results:
- metrics:
- type: mean_reward
value: 117.10 +/- 4.85
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
|
Shaier/medqa_fine_tuned_generic_bert | Shaier | 2022-07-12T20:33:17Z | 1 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"multiple-choice",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| multiple-choice | 2022-07-12T19:49:52Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: medqa_fine_tuned_generic_bert
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# medqa_fine_tuned_generic_bert
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4239
- Accuracy: 0.2869
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 318 | 1.3851 | 0.2594 |
| 1.3896 | 2.0 | 636 | 1.3805 | 0.2807 |
| 1.3896 | 3.0 | 954 | 1.3852 | 0.2948 |
| 1.3629 | 4.0 | 1272 | 1.3996 | 0.2980 |
| 1.3068 | 5.0 | 1590 | 1.4239 | 0.2869 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0
- Datasets 2.3.2
- Tokenizers 0.11.0
|
huggingtweets/ydouright | huggingtweets | 2022-07-12T20:15:17Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2022-07-12T20:13:42Z | ---
language: en
thumbnail: http://www.huggingtweets.com/ydouright/1657656913047/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1506510453286924293/NXf3sNMH_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">ethans.data</div>
<div style="text-align: center; font-size: 14px;">@ydouright</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from ethans.data.
| Data | ethans.data |
| --- | --- |
| Tweets downloaded | 3245 |
| Retweets | 119 |
| Short tweets | 572 |
| Tweets kept | 2554 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1vfnsep8/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @ydouright's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3f5l1flk) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3f5l1flk/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/ydouright')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
ychenNLP/arabic-ner-ace | ychenNLP | 2022-07-12T20:02:24Z | 15 | 2 | transformers | [
"transformers",
"pytorch",
"tf",
"bert",
"text-classification",
"BERT",
"token-classification",
"sequence-tagger-model",
"ar",
"en",
"dataset:ACE2005",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| token-classification | 2022-06-29T18:45:26Z | ---
tags:
- BERT
- token-classification
- sequence-tagger-model
language:
- ar
- en
license: mit
datasets:
- ACE2005
---
# Arabic NER Model
- [Github repo](https://github.com/edchengg/GigaBERT)
- NER BIO tagging model based on [GigaBERTv4](https://huggingface.co/lanwuwei/GigaBERT-v4-Arabic-and-English).
- ACE2005 Training data: English + Arabic
- [NER tags](https://www.ldc.upenn.edu/sites/www.ldc.upenn.edu/files/english-entities-guidelines-v6.6.pdf) including: PER, VEH, GPE, WEA, ORG, LOC, FAC
## Hyperparameters
- learning_rate=2e-5
- num_train_epochs=10
- weight_decay=0.01
## ACE2005 Evaluation results (F1)
| Language | Arabic | English |
|:----:|:-----------:|:----:|
| | 89.4 | 88.8 |
## How to use
```python
>>> from transformers import pipeline, AutoModelForTokenClassification, AutoTokenizer
>>> ner_model = AutoModelForTokenClassification.from_pretrained("ychenNLP/arabic-ner-ace")
>>> ner_tokenizer = AutoTokenizer.from_pretrained("ychenNLP/arabic-ner-ace")
>>> ner_pip = pipeline("ner", model=ner_model, tokenizer=ner_tokenizer, grouped_entities=True)
>>> output = ner_pip('Protests break out across the US after Supreme Court overturns.')
>>> print(output)
[{'entity_group': 'GPE', 'score': 0.9979881, 'word': 'us', 'start': 30, 'end': 32}, {'entity_group': 'ORG', 'score': 0.99898684, 'word': 'supreme court', 'start': 39, 'end': 52}]
>>> output = ner_pip('قال وزير العدل التركي بكير بوزداغ إن أنقرة تريد 12 مشتبهاً بهم من فنلندا و 21 من السويد')
>>> print(output)
[{'entity_group': 'PER', 'score': 0.9996214, 'word': 'وزير', 'start': 4, 'end': 8}, {'entity_group': 'ORG', 'score': 0.9952383, 'word': 'العدل', 'start': 9, 'end': 14}, {'entity_group': 'GPE', 'score': 0.9996675, 'word': 'التركي', 'start': 15, 'end': 21}, {'entity_group': 'PER', 'score': 0.9978992, 'word': 'بكير بوزداغ', 'start': 22, 'end': 33}, {'entity_group': 'GPE', 'score': 0.9997154, 'word': 'انقرة', 'start': 37, 'end': 42}, {'entity_group': 'PER', 'score': 0.9946885, 'word': 'مشتبها بهم', 'start': 51, 'end': 62}, {'entity_group': 'GPE', 'score': 0.99967396, 'word': 'فنلندا', 'start': 66, 'end': 72}, {'entity_group': 'PER', 'score': 0.99694425, 'word': '21', 'start': 75, 'end': 77}, {'entity_group': 'GPE', 'score': 0.99963355, 'word': 'السويد', 'start': 81, 'end': 87}]
```
### BibTeX entry and citation info
```bibtex
@inproceedings{lan2020gigabert,
author = {Lan, Wuwei and Chen, Yang and Xu, Wei and Ritter, Alan},
title = {Giga{BERT}: Zero-shot Transfer Learning from {E}nglish to {A}rabic},
booktitle = {Proceedings of The 2020 Conference on Empirical Methods on Natural Language Processing (EMNLP)},
year = {2020}
}
```
|
MichalRoztocki/finetuning-sentiment-model-3000-samples | MichalRoztocki | 2022-07-12T19:48:42Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2022-07-12T19:35:30Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-3000-samples
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.8766666666666667
- name: F1
type: f1
value: 0.877887788778878
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3085
- Accuracy: 0.8767
- F1: 0.8779
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
huggingtweets/masonhaggerty | huggingtweets | 2022-07-12T17:17:06Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2022-07-12T16:48:40Z | ---
language: en
thumbnail: http://www.huggingtweets.com/masonhaggerty/1657646221015/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1410026132121047041/LiYev7vQ_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Mason Haggerty</div>
<div style="text-align: center; font-size: 14px;">@masonhaggerty</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Mason Haggerty.
| Data | Mason Haggerty |
| --- | --- |
| Tweets downloaded | 785 |
| Retweets | 71 |
| Short tweets | 82 |
| Tweets kept | 632 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/jpav9nmg/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @masonhaggerty's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/bs6k2tzz) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/bs6k2tzz/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/masonhaggerty')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
zluvolyote/s288cExpressionPrediction_k6 | zluvolyote | 2022-07-12T16:54:43Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2022-07-12T16:02:01Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: s288cExpressionPrediction_k6
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# s288cExpressionPrediction_k6
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4418
- Accuracy: 0.8067
- F1: 0.7882
## 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: 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 | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 58 | 0.5315 | 0.7278 | 0.7572 |
| No log | 2.0 | 116 | 0.4604 | 0.7853 | 0.7841 |
| No log | 3.0 | 174 | 0.4418 | 0.8067 | 0.7882 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
reachrkr/TEST2ppo-LunarLander-v2 | reachrkr | 2022-07-12T16:20:36Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-07-12T16:20:08Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 266.96 +/- 25.94
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
andy-0v0/orcs-and-friends | andy-0v0 | 2022-07-12T16:03:57Z | 53 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| image-classification | 2022-07-12T15:50:36Z | ---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: orcs-and-friends
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.522522509098053
---
# orcs-and-friends
Five-way classifier for orcs and their friends
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### goblin
#### gremlin
#### ogre
#### orc
#### troll
 |
MarLac/wav2vec2-base-timit-demo-google-colab | MarLac | 2022-07-12T15:41:51Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2022-07-12T08:24:30Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-timit-demo-google-colab
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-demo-google-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: 0.5816
- Wer: 0.3533
## 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: 4
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 2.243 | 0.5 | 500 | 1.0798 | 0.7752 |
| 0.834 | 1.01 | 1000 | 0.6206 | 0.5955 |
| 0.5503 | 1.51 | 1500 | 0.5387 | 0.5155 |
| 0.4548 | 2.01 | 2000 | 0.4660 | 0.4763 |
| 0.3412 | 2.51 | 2500 | 0.8381 | 0.4836 |
| 0.3128 | 3.02 | 3000 | 0.4818 | 0.4519 |
| 0.2547 | 3.52 | 3500 | 0.4415 | 0.4230 |
| 0.2529 | 4.02 | 4000 | 0.4624 | 0.4219 |
| 0.2103 | 4.52 | 4500 | 0.4714 | 0.4096 |
| 0.2102 | 5.03 | 5000 | 0.4968 | 0.4087 |
| 0.1838 | 5.53 | 5500 | 0.4643 | 0.4131 |
| 0.1721 | 6.03 | 6000 | 0.4676 | 0.3979 |
| 0.1548 | 6.53 | 6500 | 0.4765 | 0.4085 |
| 0.1595 | 7.04 | 7000 | 0.4797 | 0.3941 |
| 0.1399 | 7.54 | 7500 | 0.4753 | 0.3902 |
| 0.1368 | 8.04 | 8000 | 0.4697 | 0.3945 |
| 0.1276 | 8.54 | 8500 | 0.5438 | 0.3869 |
| 0.1255 | 9.05 | 9000 | 0.5660 | 0.3841 |
| 0.1077 | 9.55 | 9500 | 0.4964 | 0.3947 |
| 0.1197 | 10.05 | 10000 | 0.5349 | 0.3849 |
| 0.1014 | 10.55 | 10500 | 0.5558 | 0.3883 |
| 0.0949 | 11.06 | 11000 | 0.5673 | 0.3785 |
| 0.0882 | 11.56 | 11500 | 0.5589 | 0.3955 |
| 0.0906 | 12.06 | 12000 | 0.5752 | 0.4120 |
| 0.1064 | 12.56 | 12500 | 0.5080 | 0.3727 |
| 0.0854 | 13.07 | 13000 | 0.5398 | 0.3798 |
| 0.0754 | 13.57 | 13500 | 0.5237 | 0.3816 |
| 0.0791 | 14.07 | 14000 | 0.4967 | 0.3725 |
| 0.0731 | 14.57 | 14500 | 0.5287 | 0.3744 |
| 0.0719 | 15.08 | 15000 | 0.5633 | 0.3596 |
| 0.062 | 15.58 | 15500 | 0.5399 | 0.3752 |
| 0.0681 | 16.08 | 16000 | 0.5151 | 0.3759 |
| 0.0559 | 16.58 | 16500 | 0.5564 | 0.3709 |
| 0.0533 | 17.09 | 17000 | 0.5933 | 0.3743 |
| 0.0563 | 17.59 | 17500 | 0.5381 | 0.3670 |
| 0.0527 | 18.09 | 18000 | 0.5685 | 0.3731 |
| 0.0492 | 18.59 | 18500 | 0.5728 | 0.3725 |
| 0.0509 | 19.1 | 19000 | 0.6074 | 0.3807 |
| 0.0436 | 19.6 | 19500 | 0.5762 | 0.3628 |
| 0.0434 | 20.1 | 20000 | 0.6721 | 0.3729 |
| 0.0416 | 20.6 | 20500 | 0.5842 | 0.3700 |
| 0.0431 | 21.11 | 21000 | 0.5374 | 0.3607 |
| 0.037 | 21.61 | 21500 | 0.5556 | 0.3667 |
| 0.036 | 22.11 | 22000 | 0.5608 | 0.3592 |
| 0.04 | 22.61 | 22500 | 0.5272 | 0.3637 |
| 0.047 | 23.12 | 23000 | 0.5234 | 0.3625 |
| 0.0506 | 23.62 | 23500 | 0.5427 | 0.3629 |
| 0.0418 | 24.12 | 24000 | 0.5590 | 0.3626 |
| 0.037 | 24.62 | 24500 | 0.5615 | 0.3555 |
| 0.0429 | 25.13 | 25000 | 0.5806 | 0.3616 |
| 0.045 | 25.63 | 25500 | 0.5777 | 0.3639 |
| 0.0283 | 26.13 | 26000 | 0.5987 | 0.3617 |
| 0.0253 | 26.63 | 26500 | 0.5671 | 0.3551 |
| 0.032 | 27.14 | 27000 | 0.5464 | 0.3582 |
| 0.0321 | 27.64 | 27500 | 0.5634 | 0.3573 |
| 0.0274 | 28.14 | 28000 | 0.5513 | 0.3575 |
| 0.0245 | 28.64 | 28500 | 0.5745 | 0.3537 |
| 0.0251 | 29.15 | 29000 | 0.5759 | 0.3547 |
| 0.0222 | 29.65 | 29500 | 0.5816 | 0.3533 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 1.18.3
- Tokenizers 0.12.1
|
andreaschandra/xlm-roberta-base-finetuned-panx-fr | andreaschandra | 2022-07-12T15:30:15Z | 6 | 0 | transformers | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| token-classification | 2022-07-12T15:15:58Z | ---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-fr
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.fr
metrics:
- name: F1
type: f1
value: 0.9275221167113059
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1059
- F1: 0.9275
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.5416 | 1.0 | 191 | 0.2322 | 0.8378 |
| 0.2614 | 2.0 | 382 | 0.1544 | 0.8866 |
| 0.1758 | 3.0 | 573 | 0.1059 | 0.9275 |
### Framework versions
- Transformers 4.19.4
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
zluvolyote/CUBERT | zluvolyote | 2022-07-12T15:09:51Z | 16 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| fill-mask | 2022-06-15T18:09:44Z | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: CUBERT
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# CUBERT
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 5.2203
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 58 | 5.5281 |
| No log | 2.0 | 116 | 5.2508 |
| No log | 3.0 | 174 | 5.2203 |
### Framework versions
- Transformers 4.19.4
- Pytorch 1.11.0+cu113
- Datasets 2.3.1
- Tokenizers 0.12.1
|
huggingtweets/scottduncanwx | huggingtweets | 2022-07-12T14:43:36Z | 3 | 1 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2022-07-12T14:37:59Z | ---
language: en
thumbnail: http://www.huggingtweets.com/scottduncanwx/1657637010818/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1535379125296418821/ntSMv4LC_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Scott Duncan</div>
<div style="text-align: center; font-size: 14px;">@scottduncanwx</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Scott Duncan.
| Data | Scott Duncan |
| --- | --- |
| Tweets downloaded | 3250 |
| Retweets | 186 |
| Short tweets | 223 |
| Tweets kept | 2841 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/tziokng8/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @scottduncanwx's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2swonujn) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2swonujn/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/scottduncanwx')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
Kuro96/q-FrozenLake-v1-4x4-noSlippery | Kuro96 | 2022-07-12T14:35:27Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-07-12T14:35:21Z | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="Kuro96/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
Kuro96/q-Taxi-v3 | Kuro96 | 2022-07-12T14:25:59Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-07-12T14:25:52Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- metrics:
- type: mean_reward
value: 7.50 +/- 2.76
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
---
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
huggingtweets/piotrikonowicz1 | huggingtweets | 2022-07-12T14:00:31Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2022-07-12T14:00:22Z | ---
language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/770622589664460802/bgUHfTNZ_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Piotr Ikonowicz</div>
<div style="text-align: center; font-size: 14px;">@piotrikonowicz1</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Piotr Ikonowicz.
| Data | Piotr Ikonowicz |
| --- | --- |
| Tweets downloaded | 133 |
| Retweets | 3 |
| Short tweets | 13 |
| Tweets kept | 117 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/156jwrd1/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @piotrikonowicz1's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/w029u281) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/w029u281/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/piotrikonowicz1')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
andreaschandra/xlm-roberta-base-finetuned-panx-de | andreaschandra | 2022-07-12T13:52:44Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| token-classification | 2022-07-12T13:28:29Z | ---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.de
metrics:
- name: F1
type: f1
value: 0.8620945214069894
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1372
- F1: 0.8621
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2575 | 1.0 | 525 | 0.1621 | 0.8292 |
| 0.1287 | 2.0 | 1050 | 0.1378 | 0.8526 |
| 0.0831 | 3.0 | 1575 | 0.1372 | 0.8621 |
### Framework versions
- Transformers 4.19.4
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
workRL/TEST2ppo-CarRacing-v0 | workRL | 2022-07-12T13:31:15Z | 3 | 0 | stable-baselines3 | [
"stable-baselines3",
"CarRacing-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-07-12T13:29:34Z | ---
library_name: stable-baselines3
tags:
- CarRacing-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: -69.53 +/- 1.56
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CarRacing-v0
type: CarRacing-v0
---
# **PPO** Agent playing **CarRacing-v0**
This is a trained model of a **PPO** agent playing **CarRacing-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
hugginglearners/grapevine_leaves_classification | hugginglearners | 2022-07-12T13:27:44Z | 0 | 1 | fastai | [
"fastai",
"image-classification",
"region:us"
]
| image-classification | 2022-07-08T13:16:44Z | ---
tags:
- fastai
- image-classification
---
## Model description
This repo contains the trained model for grapevine leaves image classification
Full credits go to [Vu Minh Chien](https://www.linkedin.com/in/vumichien/)
Motivation: The main product of grapevines is grapes that are consumed fresh or processed. In addition, grapevine leaves are harvested once a year as a by-product. The species of grapevine leaves are important in terms of price and taste. In this repo, deep learning-based classification is conducted by using images of grapevine leaves
## Intended uses & limitations
Images of 500 vine leaves belonging to 5 species were taken with a special self-illuminating system. Later, this number was increased to 2500 with data augmentation methods
## Training and evaluation data
### Training hyperparameters
The following hyperparameters were used during training:
| Hyperparameters | Value |
| :-- | :-- |
| name | Adam |
| learning_rate | e-3 |
| freeze_epochs| 3 |
| unfreeze_epochs| 10|
| training_precision | float16 |
|
hugginglearners/pokemon-card-checker | hugginglearners | 2022-07-12T13:21:57Z | 0 | 1 | fastai | [
"fastai",
"resnet",
"computer-vision",
"classification",
"image-classification",
"binary-classification",
"license:cc0-1.0",
"region:us"
]
| image-classification | 2022-06-20T14:02:07Z | ---
tags:
- fastai
- resnet
- computer-vision
- classification
- image-classification
- binary-classification
license:
- cc0-1.0
---
# Resnet34 Pokemon Card Classifier
## Model Description
This is a resnet34 model fine-tuned with fastai to [classify real and fake Pokemon cards (dataset)](https://www.kaggle.com/datasets/ongshujian/real-and-fake-pokemon-cards).
Here is a colab notebook that shows how the model was trained and pushed to the hub: [link](https://github.com/mindwrapped/pokemon-card-checker/blob/main/pokemon_card_checker.ipynb).
## Intended uses & limitation
This model is trained to identify real vs fake cards based on the backs of the cards, not the front.
## How to use
```python
from huggingface_hub import from_pretrained_fastai
# Pull model from hub
learn = from_pretrained_fastai('hugginglearners/pokemon-card-checker')
# Get prediction for this image
pred_label, _, scores = learn.predict(img)
```
## Training data
Dataset located here: [link](https://www.kaggle.com/datasets/ongshujian/real-and-fake-pokemon-cards).
|
xuantsh/distilroberta-base-Mark_example | xuantsh | 2022-07-12T13:13:45Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| fill-mask | 2022-07-12T12:57:18Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilroberta-base-Mark_example
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilroberta-base-Mark_example
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6043
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 2.8299 | 1.0 | 744 | 2.6322 |
| 2.7034 | 2.0 | 1488 | 2.6514 |
| 2.5616 | 3.0 | 2232 | 2.6596 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
suc155/distilbert-base-uncased-finetuned-sst2 | suc155 | 2022-07-12T12:43:16Z | 6 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2022-07-12T12:22:16Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-sst2
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: sst2
metrics:
- name: Accuracy
type: accuracy
value: 0.9151376146788991
---
<!-- 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-sst2
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: 0.3056
- Accuracy: 0.9151
## 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 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.1827 | 1.0 | 4210 | 0.3056 | 0.9151 |
| 0.1235 | 2.0 | 8420 | 0.3575 | 0.9071 |
| 0.1009 | 3.0 | 12630 | 0.3896 | 0.9071 |
| 0.0561 | 4.0 | 16840 | 0.4810 | 0.9060 |
| 0.0406 | 5.0 | 21050 | 0.5375 | 0.9048 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
mohammedbriman/t5-small-finetuned-cnn-dm-test | mohammedbriman | 2022-07-12T12:38:05Z | 3 | 0 | transformers | [
"transformers",
"tf",
"t5",
"text2text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-07-12T09:51:25Z | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: t5-small-finetuned-cnn-dm-test
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-cnn-dm-test
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 2.4521
- Validation Loss: 2.1296
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5.6e-05, 'decay_steps': 408096, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 2.4521 | 2.1296 | 0 |
### Framework versions
- Transformers 4.20.1
- TensorFlow 2.8.2
- Datasets 2.3.2
- Tokenizers 0.12.1
|
xyma/PROP-marco-step400k | xyma | 2022-07-12T11:53:02Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"pretraining",
"PROP",
"Pretrain4IR",
"en",
"dataset:msmarco",
"arxiv:2010.10137",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| null | 2022-07-12T09:06:57Z | ---
language: en
tags:
- PROP
- Pretrain4IR
license: apache-2.0
datasets:
- msmarco
---
# PROP-marco-step400k
**PROP**, **P**re-training with **R**epresentative w**O**rds **P**rediction, is a new pre-training method tailored for ad-hoc retrieval. PROP is inspired by the classical statistical language model for IR, specifically the query likelihood model, which assumes that the query is generated as the piece of text representative of the “ideal” document. Based on this idea, we construct the representative words prediction (ROP) task for pre-training. The full paper can be found [here](https://arxiv.org/pdf/2010.10137.pdf).
This model is pre-trained with more steps than [PROP-marco](https://huggingface.co/xyma/PROP-marco) on MS MARCO document corpus, and used at the MS MARCO Document Ranking Leaderboard where we reached 1st place.
# Citation
If you find our work useful, please consider citing our paper:
```bibtex
@inproceedings{DBLP:conf/wsdm/MaGZFJC21,
author = {Xinyu Ma and
Jiafeng Guo and
Ruqing Zhang and
Yixing Fan and
Xiang Ji and
Xueqi Cheng},
editor = {Liane Lewin{-}Eytan and
David Carmel and
Elad Yom{-}Tov and
Eugene Agichtein and
Evgeniy Gabrilovich},
title = {{PROP:} Pre-training with Representative Words Prediction for Ad-hoc
Retrieval},
booktitle = {{WSDM} '21, The Fourteenth {ACM} International Conference on Web Search
and Data Mining, Virtual Event, Israel, March 8-12, 2021},
pages = {283--291},
publisher = {{ACM}},
year = {2021},
url = {https://doi.org/10.1145/3437963.3441777},
doi = {10.1145/3437963.3441777},
timestamp = {Wed, 07 Apr 2021 16:17:44 +0200},
biburl = {https://dblp.org/rec/conf/wsdm/MaGZFJC21.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
``` |
Vikasbhandari/wav2vec2-train | Vikasbhandari | 2022-07-12T11:51:48Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"tf",
"jax",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"speech",
"audio",
"hf-asr-leaderboard",
"en",
"dataset:librispeech_asr",
"arxiv:2010.11430",
"arxiv:2006.11477",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2022-07-12T11:11:37Z | ---
language: en
datasets:
- librispeech_asr
tags:
- speech
- audio
- automatic-speech-recognition
- hf-asr-leaderboard
license: apache-2.0
model-index:
- name: wav2vec2-large-960h-lv60
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: LibriSpeech (clean)
type: librispeech_asr
config: clean
split: test
args:
language: en
metrics:
- name: Test WER
type: wer
value: 1.9
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: LibriSpeech (other)
type: librispeech_asr
config: other
split: test
args:
language: en
metrics:
- name: Test WER
type: wer
value: 3.9
---
# Wav2Vec2-Large-960h-Lv60 + Self-Training
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/)
The large model pretrained and fine-tuned on 960 hours of Libri-Light and Librispeech on 16kHz sampled speech audio. Model was trained with [Self-Training objective](https://arxiv.org/abs/2010.11430). When using the model make sure that your speech input is also sampled at 16Khz.
[Paper](https://arxiv.org/abs/2006.11477)
Authors: Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Auli
**Abstract**
We show for the first time that learning powerful representations from speech audio alone followed by fine-tuning on transcribed speech can outperform the best semi-supervised methods while being conceptually simpler. wav2vec 2.0 masks the speech input in the latent space and solves a contrastive task defined over a quantization of the latent representations which are jointly learned. Experiments using all labeled data of Librispeech achieve 1.8/3.3 WER on the clean/other test sets. When lowering the amount of labeled data to one hour, wav2vec 2.0 outperforms the previous state of the art on the 100 hour subset while using 100 times less labeled data. Using just ten minutes of labeled data and pre-training on 53k hours of unlabeled data still achieves 4.8/8.2 WER. This demonstrates the feasibility of speech recognition with limited amounts of labeled data.
The original model can be found under https://github.com/pytorch/fairseq/tree/master/examples/wav2vec#wav2vec-20.
# Usage
To transcribe audio files the model can be used as a standalone acoustic model as follows:
```python
from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
from datasets import load_dataset
import torch
# load model and processor
processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-large-960h-lv60-self")
model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-large-960h-lv60-self")
# load dummy dataset and read soundfiles
ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
# tokenize
input_values = processor(ds[0]["audio"]["array"], return_tensors="pt", padding="longest").input_values
# retrieve logits
logits = model(input_values).logits
# take argmax and decode
predicted_ids = torch.argmax(logits, dim=-1)
transcription = processor.batch_decode(predicted_ids)
```
## Evaluation
This code snippet shows how to evaluate **facebook/wav2vec2-large-960h-lv60-self** on LibriSpeech's "clean" and "other" test data.
```python
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import torch
from jiwer import wer
librispeech_eval = load_dataset("librispeech_asr", "clean", split="test")
model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-large-960h-lv60-self").to("cuda")
processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-large-960h-lv60-self")
def map_to_pred(batch):
inputs = processor(batch["audio"]["array"], return_tensors="pt", padding="longest")
input_values = inputs.input_values.to("cuda")
attention_mask = inputs.attention_mask.to("cuda")
with torch.no_grad():
logits = model(input_values, attention_mask=attention_mask).logits
predicted_ids = torch.argmax(logits, dim=-1)
transcription = processor.batch_decode(predicted_ids)
batch["transcription"] = transcription
return batch
result = librispeech_eval.map(map_to_pred, remove_columns=["audio"])
print("WER:", wer(result["text"], result["transcription"]))
```
*Result (WER)*:
| "clean" | "other" |
|---|---|
| 1.9 | 3.9 | |
MiguelCosta/finetuning-sentiment-model-24000-samples | MiguelCosta | 2022-07-12T10:48:14Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2022-07-12T06:17:23Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-24000-samples
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.9266666666666666
- name: F1
type: f1
value: 0.9273927392739274
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-sentiment-model-24000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3505
- Accuracy: 0.9267
- F1: 0.9274
## 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: 4
### Training results
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
nawta/wav2vec2-onomatopoeia-finetune_smalldata_ESC50pretrained | nawta | 2022-07-12T10:20:53Z | 15 | 0 | transformers | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2022-07-12T05:31:38Z | ---
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-onomatopoeia-finetune_smalldata_ESC50pretrained
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-onomatopoeia-finetune_smalldata_ESC50pretrained
This model is a fine-tuned version of [/root/workspace/wav2vec2-pretrained_with_ESC50_10000epochs_32batch_2022-07-09_22-16-46/pytorch_model.bin](https://huggingface.co//root/workspace/wav2vec2-pretrained_with_ESC50_10000epochs_32batch_2022-07-09_22-16-46/pytorch_model.bin) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2963
- Cer: 0.9002
## 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: 64
- eval_batch_size: 16
- 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 | Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 4.3287 | 23.81 | 500 | 2.2963 | 0.9002 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.1+cu111
- Datasets 1.13.3
- Tokenizers 0.10.3
|
luke-thorburn/suggest-reasons-bias-only | luke-thorburn | 2022-07-12T10:07:19Z | 8 | 0 | transformers | [
"transformers",
"pytorch",
"gpt_neo",
"text-generation",
"argumentation",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-generation | 2022-03-02T23:29:05Z | ---
language:
- en
tags:
- argumentation
license: apache-2.0
metrics:
- perplexity
---
# Generate reasons that support a claim
This model is a version of [`gpt-neo-2.7B`](https://huggingface.co/EleutherAI/gpt-neo-2.7B), where some parameters (only the bias parameters, not weights) have been finetuned on the task of generating reasons that support a claim, optionally given some example reasons. It was trained as part of a University of Melbourne [research project](https://github.com/Hunt-Laboratory/language-model-optimization) evaluating how large language models can best be optimized to perform argumentative reasoning tasks.
Code used for optimization and evaluation can be found in the project [GitHub repository](https://github.com/Hunt-Laboratory/language-model-optimization). A paper reporting on model evaluation is currently under review.
# Prompt Template
```
List reasons why: [original claim]
Reasons:
* [reason 1]
* [reason 2]
...
* [reason n]
* [generated reason]
```
# Dataset
The parameters were finetuned using argument maps scraped from the crowdsourced argument-mapping platform [Kialo](https://kialo.com/).
# Limitations and Biases
The model is a finetuned version of [`gpt-neo-2.7B`](https://huggingface.co/EleutherAI/gpt-neo-2.7B), so likely has many of the same limitations and biases. Additionally, note that while the goal of the model is to produce coherent and valid reasoning, many generated model outputs will be illogical or nonsensical and should not be relied upon.
# Acknowledgements
This research was funded by the Australian Department of Defence and the Office of National Intelligence under the AI for Decision Making Program, delivered in partnership with the Defence Science Institute in Victoria, Australia. |
luke-thorburn/suggest-reasons-full-finetune | luke-thorburn | 2022-07-12T10:04:57Z | 10 | 0 | transformers | [
"transformers",
"pytorch",
"gpt_neo",
"text-generation",
"argumentation",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-generation | 2022-03-02T23:29:05Z | ---
language:
- en
tags:
- argumentation
license: apache-2.0
metrics:
- perplexity
---
# Generate reasons that support a claim
This model is a version of [`gpt-neo-2.7B`](https://huggingface.co/EleutherAI/gpt-neo-2.7B), where all parameters (both weights and biases) have been finetuned on the task of generating reasons that support a claim, optionally given some example reasons. It was trained as part of a University of Melbourne [research project](https://github.com/Hunt-Laboratory/language-model-optimization) evaluating how large language models can best be optimized to perform argumentative reasoning tasks.
Code used for optimization and evaluation can be found in the project [GitHub repository](https://github.com/Hunt-Laboratory/language-model-optimization). A paper reporting on model evaluation is currently under review.
# Prompt Template
```
List reasons why: [original claim]
Reasons:
* [reason 1]
* [reason 2]
...
* [reason n]
* [generated reason]
```
# Dataset
The parameters were finetuned using argument maps scraped from the crowdsourced argument-mapping platform [Kialo](https://kialo.com/).
# Limitations and Biases
The model is a finetuned version of [`gpt-neo-2.7B`](https://huggingface.co/EleutherAI/gpt-neo-2.7B), so likely has many of the same limitations and biases. Additionally, note that while the goal of the model is to produce coherent and valid reasoning, many generated model outputs will be illogical or nonsensical and should not be relied upon.
# Acknowledgements
This research was funded by the Australian Department of Defence and the Office of National Intelligence under the AI for Decision Making Program, delivered in partnership with the Defence Science Institute in Victoria, Australia. |
luke-thorburn/suggest-objections-full-finetune | luke-thorburn | 2022-07-12T09:54:28Z | 11 | 0 | transformers | [
"transformers",
"pytorch",
"gpt_neo",
"text-generation",
"argumentation",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-generation | 2022-03-02T23:29:05Z | ---
language:
- en
tags:
- argumentation
license: apache-2.0
metrics:
- perplexity
---
# Generate objections to a claim
This model is a version of [`gpt-neo-2.7B`](https://huggingface.co/EleutherAI/gpt-neo-2.7B), where all parameters (both weights and biases) have been finetuned on the task of generating the objections to a claim, optionally given some example objections to that claim. It was trained as part of a University of Melbourne [research project](https://github.com/Hunt-Laboratory/language-model-optimization) evaluating how large language models can best be optimized to perform argumentative reasoning tasks.
Code used for optimization and evaluation can be found in the project [GitHub repository](https://github.com/Hunt-Laboratory/language-model-optimization). A paper reporting on model evaluation is currently under review.
# Prompt Template
```
List objections to the claim that: [original claim]
Objections:
* [objection 1]
* [objection 2]
...
* [objection n]
* [generated objection]
```
# Dataset
The parameters were finetuned using argument maps scraped from the crowdsourced argument-mapping platform [Kialo](https://kialo.com/).
# Limitations and Biases
The model is a finetuned version of [`gpt-neo-2.7B`](https://huggingface.co/EleutherAI/gpt-neo-2.7B), so likely has many of the same limitations and biases. Additionally, note that while the goal of the model is to produce coherent and valid reasoning, many generated model outputs will be illogical or nonsensical and should not be relied upon.
# Acknowledgements
This research was funded by the Australian Department of Defence and the Office of National Intelligence under the AI for Decision Making Program, delivered in partnership with the Defence Science Institute in Victoria, Australia. |
moonzi/distilbert-base-uncased-finetuned-cola | moonzi | 2022-07-12T09:35:36Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2022-07-12T09:23:29Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: distilbert-base-uncased-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.5383825234212567
---
<!-- 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: 0.5608
- Matthews Correlation: 0.5384
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5217 | 1.0 | 535 | 0.5248 | 0.4152 |
| 0.3479 | 2.0 | 1070 | 0.5000 | 0.4855 |
| 0.2345 | 3.0 | 1605 | 0.5608 | 0.5384 |
| 0.1843 | 4.0 | 2140 | 0.7651 | 0.5224 |
| 0.1304 | 5.0 | 2675 | 0.8071 | 0.5370 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
AntiSquid/TEST2ppo-LunarLander-v2 | AntiSquid | 2022-07-12T07:10:57Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-07-06T21:53:51Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 285.66 +/- 15.86
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
sun1638650145/Reinforce-Pixelcopter-PLE-v0 | sun1638650145 | 2022-07-12T07:06:35Z | 0 | 0 | null | [
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-07-12T07:06:22Z | ---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter-PLE-v0
results:
- metrics:
- type: mean_reward
value: 13.30 +/- 9.12
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
---
# 使用**Reinforce**智能体来玩**Pixelcopter-PLE-v0**
这是一个使用**Reinforce**训练有素的模型玩**Pixelcopter-PLE-v0**.
要学习使用这个模型并训练你的模型, 请查阅深度强化学习课程第5单元: https://github.com/huggingface/deep-rl-class/tree/main/unit5
|
thunlp/Lawformer | thunlp | 2022-07-12T06:23:13Z | 155 | 19 | transformers | [
"transformers",
"pytorch",
"longformer",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| fill-mask | 2022-03-02T23:29:05Z | ## Lawformer
### Introduction
This repository provides the source code and checkpoints of the paper "Lawformer: A Pre-trained Language Model forChinese Legal Long Documents". You can download the checkpoint from the [huggingface model hub](https://huggingface.co/xcjthu/Lawformer) or from [here](https://data.thunlp.org/legal/Lawformer.zip).
### Easy Start
We have uploaded our model to the huggingface model hub. Make sure you have installed transformers.
```python
>>> from transformers import AutoModel, AutoTokenizer
>>> tokenizer = AutoTokenizer.from_pretrained("thunlp/Lawformer")
>>> model = AutoModel.from_pretrained("thunlp/Lawformer")
>>> inputs = tokenizer("任某提起诉讼，请求判令解除婚姻关系并对夫妻共同财产进行分割。", return_tensors="pt")
>>> outputs = model(**inputs)
```
### Cite
If you use the pre-trained models, please cite this paper:
```
@article{xiao2021lawformer,
title={Lawformer: A Pre-trained Language Model forChinese Legal Long Documents},
author={Xiao, Chaojun and Hu, Xueyu and Liu, Zhiyuan and Tu, Cunchao and Sun, Maosong},
year={2021}
}
```
|
reecejocumsenbb/testfield-finetuned-imdb | reecejocumsenbb | 2022-07-12T06:02:47Z | 5 | 0 | transformers | [
"transformers",
"tf",
"distilbert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| fill-mask | 2022-07-12T04:23:21Z | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: reecejocumsenbb/testfield-finetuned-imdb
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# reecejocumsenbb/testfield-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 4.0451
- Validation Loss: 3.9664
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': -993, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 4.0451 | 3.9664 | 0 |
### Framework versions
- Transformers 4.20.1
- TensorFlow 2.9.1
- Datasets 2.3.2
- Tokenizers 0.12.1
|
Evelyn18/legalectra-small-spanish-becasv3-4 | Evelyn18 | 2022-07-12T04:38:19Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"electra",
"question-answering",
"generated_from_trainer",
"dataset:becasv2",
"endpoints_compatible",
"region:us"
]
| question-answering | 2022-07-12T04:36:14Z | ---
tags:
- generated_from_trainer
datasets:
- becasv2
model-index:
- name: legalectra-small-spanish-becasv3-4
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# legalectra-small-spanish-becasv3-4
This model is a fine-tuned version of [mrm8488/legalectra-small-spanish](https://huggingface.co/mrm8488/legalectra-small-spanish) on the becasv2 dataset.
It achieves the following results on the evaluation set:
- Loss: 4.1290
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 5 | 5.6625 |
| No log | 2.0 | 10 | 5.4940 |
| No log | 3.0 | 15 | 5.3886 |
| No log | 4.0 | 20 | 5.3004 |
| No log | 5.0 | 25 | 5.2210 |
| No log | 6.0 | 30 | 5.1434 |
| No log | 7.0 | 35 | 5.0546 |
| No log | 8.0 | 40 | 4.9726 |
| No log | 9.0 | 45 | 4.9227 |
| No log | 10.0 | 50 | 4.8344 |
| No log | 11.0 | 55 | 4.7749 |
| No log | 12.0 | 60 | 4.7381 |
| No log | 13.0 | 65 | 4.7016 |
| No log | 14.0 | 70 | 4.6581 |
| No log | 15.0 | 75 | 4.6231 |
| No log | 16.0 | 80 | 4.5900 |
| No log | 17.0 | 85 | 4.5446 |
| No log | 18.0 | 90 | 4.5041 |
| No log | 19.0 | 95 | 4.4635 |
| No log | 20.0 | 100 | 4.4356 |
| No log | 21.0 | 105 | 4.3985 |
| No log | 22.0 | 110 | 4.3650 |
| No log | 23.0 | 115 | 4.3540 |
| No log | 24.0 | 120 | 4.3270 |
| No log | 25.0 | 125 | 4.2873 |
| No log | 26.0 | 130 | 4.2808 |
| No log | 27.0 | 135 | 4.2623 |
| No log | 28.0 | 140 | 4.2466 |
| No log | 29.0 | 145 | 4.2488 |
| No log | 30.0 | 150 | 4.2410 |
| No log | 31.0 | 155 | 4.2187 |
| No log | 32.0 | 160 | 4.2000 |
| No log | 33.0 | 165 | 4.1883 |
| No log | 34.0 | 170 | 4.1803 |
| No log | 35.0 | 175 | 4.1773 |
| No log | 36.0 | 180 | 4.1652 |
| No log | 37.0 | 185 | 4.1614 |
| No log | 38.0 | 190 | 4.1609 |
| No log | 39.0 | 195 | 4.1652 |
| No log | 40.0 | 200 | 4.1560 |
| No log | 41.0 | 205 | 4.1435 |
| No log | 42.0 | 210 | 4.1463 |
| No log | 43.0 | 215 | 4.1434 |
| No log | 44.0 | 220 | 4.1340 |
| No log | 45.0 | 225 | 4.1259 |
| No log | 46.0 | 230 | 4.1212 |
| No log | 47.0 | 235 | 4.1224 |
| No log | 48.0 | 240 | 4.1257 |
| No log | 49.0 | 245 | 4.1284 |
| No log | 50.0 | 250 | 4.1290 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
Evelyn18/legalectra-small-spanish-becasv3-3 | Evelyn18 | 2022-07-12T04:30:27Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"electra",
"question-answering",
"generated_from_trainer",
"dataset:becasv2",
"endpoints_compatible",
"region:us"
]
| question-answering | 2022-07-12T04:28:15Z | ---
tags:
- generated_from_trainer
datasets:
- becasv2
model-index:
- name: legalectra-small-spanish-becasv3-3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# legalectra-small-spanish-becasv3-3
This model is a fine-tuned version of [mrm8488/legalectra-small-spanish](https://huggingface.co/mrm8488/legalectra-small-spanish) on the becasv2 dataset.
It achieves the following results on the evaluation set:
- Loss: 4.4873
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 6e-05
- train_batch_size: 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: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 5 | 5.7608 |
| No log | 2.0 | 10 | 5.5991 |
| No log | 3.0 | 15 | 5.5162 |
| No log | 4.0 | 20 | 5.4370 |
| No log | 5.0 | 25 | 5.3521 |
| No log | 6.0 | 30 | 5.2657 |
| No log | 7.0 | 35 | 5.1771 |
| No log | 8.0 | 40 | 5.1024 |
| No log | 9.0 | 45 | 5.0248 |
| No log | 10.0 | 50 | 4.9609 |
| No log | 11.0 | 55 | 4.9167 |
| No log | 12.0 | 60 | 4.8487 |
| No log | 13.0 | 65 | 4.8175 |
| No log | 14.0 | 70 | 4.7646 |
| No log | 15.0 | 75 | 4.7276 |
| No log | 16.0 | 80 | 4.7003 |
| No log | 17.0 | 85 | 4.6518 |
| No log | 18.0 | 90 | 4.6240 |
| No log | 19.0 | 95 | 4.6033 |
| No log | 20.0 | 100 | 4.5601 |
| No log | 21.0 | 105 | 4.5433 |
| No log | 22.0 | 110 | 4.5279 |
| No log | 23.0 | 115 | 4.4981 |
| No log | 24.0 | 120 | 4.4831 |
| No log | 25.0 | 125 | 4.4745 |
| No log | 26.0 | 130 | 4.4607 |
| No log | 27.0 | 135 | 4.4528 |
| No log | 28.0 | 140 | 4.4348 |
| No log | 29.0 | 145 | 4.4418 |
| No log | 30.0 | 150 | 4.4380 |
| No log | 31.0 | 155 | 4.4205 |
| No log | 32.0 | 160 | 4.4373 |
| No log | 33.0 | 165 | 4.4302 |
| No log | 34.0 | 170 | 4.4468 |
| No log | 35.0 | 175 | 4.4512 |
| No log | 36.0 | 180 | 4.4225 |
| No log | 37.0 | 185 | 4.4303 |
| No log | 38.0 | 190 | 4.4562 |
| No log | 39.0 | 195 | 4.4671 |
| No log | 40.0 | 200 | 4.4869 |
| No log | 41.0 | 205 | 4.5046 |
| No log | 42.0 | 210 | 4.4990 |
| No log | 43.0 | 215 | 4.4847 |
| No log | 44.0 | 220 | 4.4770 |
| No log | 45.0 | 225 | 4.4786 |
| No log | 46.0 | 230 | 4.4741 |
| No log | 47.0 | 235 | 4.4797 |
| No log | 48.0 | 240 | 4.4830 |
| No log | 49.0 | 245 | 4.4845 |
| No log | 50.0 | 250 | 4.4873 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
Saraswati/q-FrozenLake-v1-4x4-noSlippery | Saraswati | 2022-07-12T04:25:49Z | 0 | 1 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-07-12T04:25:40Z | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="Saraswati/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
Evelyn18/legalectra-small-spanish-becasv3-1 | Evelyn18 | 2022-07-12T03:54:49Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"electra",
"question-answering",
"generated_from_trainer",
"dataset:becasv2",
"endpoints_compatible",
"region:us"
]
| question-answering | 2022-07-12T03:49:49Z | ---
tags:
- generated_from_trainer
datasets:
- becasv2
model-index:
- name: legalectra-small-spanish-becasv3-1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# legalectra-small-spanish-becasv3-1
This model is a fine-tuned version of [mrm8488/legalectra-small-spanish](https://huggingface.co/mrm8488/legalectra-small-spanish) on the becasv2 dataset.
It achieves the following results on the evaluation set:
- Loss: 5.5694
## 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: 10
- eval_batch_size: 10
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 8 | 5.8980 |
| No log | 2.0 | 16 | 5.8136 |
| No log | 3.0 | 24 | 5.7452 |
| No log | 4.0 | 32 | 5.6940 |
| No log | 5.0 | 40 | 5.6554 |
| No log | 6.0 | 48 | 5.6241 |
| No log | 7.0 | 56 | 5.5997 |
| No log | 8.0 | 64 | 5.5830 |
| No log | 9.0 | 72 | 5.5730 |
| No log | 10.0 | 80 | 5.5694 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
nateraw/yolov6s | nateraw | 2022-07-12T02:01:18Z | 0 | 0 | pytorch | [
"pytorch",
"object-detection",
"yolo",
"autogenerated-modelcard",
"en",
"arxiv:1910.09700",
"license:gpl-3.0",
"region:us"
]
| object-detection | 2022-07-08T04:01:40Z | ---
language: en
license: gpl-3.0
library_name: pytorch
tags:
- object-detection
- yolo
- autogenerated-modelcard
model_name: yolov6s
---
# Model Card for yolov6s
<!-- Provide a quick summary of what the model is/does. -->
# Table of Contents
1. [Model Details](#model-details)
2. [Uses](#uses)
3. [Bias, Risks, and Limitations](#bias-risks-and-limitations)
4. [Training Details](#training-details)
5. [Evaluation](#evaluation)
6. [Model Examination](#model-examination)
7. [Environmental Impact](#environmental-impact)
8. [Technical Specifications](#technical-specifications-optional)
9. [Citation](#citation)
10. [Glossary](#glossary-optional)
11. [More Information](#more-information-optional)
12. [Model Card Authors](#model-card-authors-optional)
13. [Model Card Contact](#model-card-contact)
14. [How To Get Started With the Model](#how-to-get-started-with-the-model)
# Model Details
## Model Description
<!-- Provide a longer summary of what this model is. -->
YOLOv6 is a single-stage object detection framework dedicated to industrial applications, with hardware-friendly efficient design and high performance.
- **Developed by:** [More Information Needed]
- **Shared by [Optional]:** [@nateraw](https://hf.co/nateraw)
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Related Models:** [yolov6t](https://hf.co/nateraw/yolov6t), [yolov6n](https://hf.co/nateraw/yolov6n)
- **Parent Model:** N/A
- **Resources for more information:** The [official GitHub Repository](https://github.com/meituan/YOLOv6)
# Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
## Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
This model is meant to be used as a general object detector.
## Downstream Use [Optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
You can fine-tune this model for your specific task
## Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
Don't be evil.
# Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
This model often classifies objects incorrectly, especially when applied to videos. It does not handle crowds very well.
## Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recomendations.
# Training Details
## Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
## Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
### Preprocessing
[More Information Needed]
### Speeds, Sizes, Times
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
# Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
## Testing Data, Factors & Metrics
### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
## Results
[More Information Needed]
# Model Examination
[More Information Needed]
# Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
# Technical Specifications [optional]
## Model Architecture and Objective
[More Information Needed]
## Compute Infrastructure
[More Information Needed]
### Hardware
[More Information Needed]
### Software
[More Information Needed]
# Citation
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
# Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
# More Information [optional]
Please refer to the [official GitHub Repository](https://github.com/meituan/YOLOv6)
# Model Card Authors [optional]
[@nateraw](https://hf.co/nateraw)
# Model Card Contact
[@nateraw](https://hf.co/nateraw) - please leave a note in the discussions tab here
# How to Get Started with the Model
Use the code below to get started with the model.
<details>
<summary> Click to expand </summary>
[More Information Needed]
</details> |
nateraw/yolov6n | nateraw | 2022-07-12T02:01:10Z | 0 | 0 | pytorch | [
"pytorch",
"object-detection",
"yolo",
"autogenerated-modelcard",
"en",
"arxiv:1910.09700",
"license:gpl-3.0",
"region:us"
]
| object-detection | 2022-07-08T04:01:21Z | ---
language: en
license: gpl-3.0
library_name: pytorch
tags:
- object-detection
- yolo
- autogenerated-modelcard
model_name: yolov6n
---
# Model Card for yolov6n
<!-- Provide a quick summary of what the model is/does. -->
# Table of Contents
1. [Model Details](#model-details)
2. [Uses](#uses)
3. [Bias, Risks, and Limitations](#bias-risks-and-limitations)
4. [Training Details](#training-details)
5. [Evaluation](#evaluation)
6. [Model Examination](#model-examination)
7. [Environmental Impact](#environmental-impact)
8. [Technical Specifications](#technical-specifications-optional)
9. [Citation](#citation)
10. [Glossary](#glossary-optional)
11. [More Information](#more-information-optional)
12. [Model Card Authors](#model-card-authors-optional)
13. [Model Card Contact](#model-card-contact)
14. [How To Get Started With the Model](#how-to-get-started-with-the-model)
# Model Details
## Model Description
<!-- Provide a longer summary of what this model is. -->
YOLOv6 is a single-stage object detection framework dedicated to industrial applications, with hardware-friendly efficient design and high performance.
- **Developed by:** [More Information Needed]
- **Shared by [Optional]:** [@nateraw](https://hf.co/nateraw)
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Related Models:** [yolov6t](https://hf.co/nateraw/yolov6t), [yolov6s](https://hf.co/nateraw/yolov6s)
- **Parent Model:** N/A
- **Resources for more information:** The [official GitHub Repository](https://github.com/meituan/YOLOv6)
# Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
## Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
This model is meant to be used as a general object detector.
## Downstream Use [Optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
You can fine-tune this model for your specific task
## Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
Don't be evil.
# Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
This model often classifies objects incorrectly, especially when applied to videos. It does not handle crowds very well.
## Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recomendations.
# Training Details
## Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
## Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
### Preprocessing
[More Information Needed]
### Speeds, Sizes, Times
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
# Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
## Testing Data, Factors & Metrics
### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
## Results
[More Information Needed]
# Model Examination
[More Information Needed]
# Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
# Technical Specifications [optional]
## Model Architecture and Objective
[More Information Needed]
## Compute Infrastructure
[More Information Needed]
### Hardware
[More Information Needed]
### Software
[More Information Needed]
# Citation
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
# Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
# More Information [optional]
Please refer to the [official GitHub Repository](https://github.com/meituan/YOLOv6)
# Model Card Authors [optional]
[@nateraw](https://hf.co/nateraw)
# Model Card Contact
[@nateraw](https://hf.co/nateraw) - please leave a note in the discussions tab here
# How to Get Started with the Model
Use the code below to get started with the model.
<details>
<summary> Click to expand </summary>
[More Information Needed]
</details> |
ArthurBaia/xlm-roberta-base-squad-pt | ArthurBaia | 2022-07-11T22:42:37Z | 7 | 2 | transformers | [
"transformers",
"pytorch",
"xlm-roberta",
"question-answering",
"generated_from_trainer",
"dataset:squad_v1_pt",
"license:mit",
"endpoints_compatible",
"region:us"
]
| question-answering | 2022-07-11T16:59:16Z | ---
license: mit
tags:
- generated_from_trainer
datasets:
- squad_v1_pt
model-index:
- name: xlm-roberta-base-squad-pt
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-squad-pt
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the squad_v1_pt dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- distributed_type: tpu
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
- "epoch": 3.0,
- "eval_exact_match": 44.45600756859035,
- "eval_f1": 57.37953911779836,
- "eval_samples": 11095
### Framework versions
- Transformers 4.21.0.dev0
- Pytorch 1.9.0+cu102
- Datasets 2.3.2
- Tokenizers 0.12.1 |
AntiSquid/longTEST2ppo-LunarLander-v2 | AntiSquid | 2022-07-11T22:09:41Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-07-11T22:09:16Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 298.08 +/- 18.36
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
sahilrajpal121/train5a1e8w7-label-classification | sahilrajpal121 | 2022-07-11T20:11:11Z | 0 | 0 | sklearn | [
"sklearn",
"tabular-classification",
"baseline-trainer",
"license:apache-2.0",
"region:us"
]
| tabular-classification | 2022-07-11T20:11:07Z | ---
license: apache-2.0
library_name: sklearn
tags:
- tabular-classification
- baseline-trainer
---
## Baseline Model trained on train5a1e8w7 to apply classification on label
**Metrics of the best model:**
accuracy 0.693101
recall_macro 0.665973
precision_macro 0.657625
f1_macro 0.656998
Name: LogisticRegression(C=0.1, class_weight='balanced', max_iter=1000), dtype: float64
**See model plot below:**
<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-1" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[('easypreprocessor',EasyPreprocessor(types= continuous dirty_float low_card_int ... date free_string useless
v_21 False False False ... False False False
v_32 True False False ... False False False
v_15 False False False ... False False False
v_4 True False False ... False False False
v_1 False False False ... False False False
v_8 False False False ... False False False
v_12 False False Fa...
v_34 False False False ... False False False
v_35 True False False ... False False False
v_36 True False False ... False False False
v_37 True False False ... False False False
v_38 True False False ... False False False
v_39 True False False ... False False False
v_40 False False False ... False False False[40 rows x 7 columns])),('logisticregression',LogisticRegression(C=0.1, class_weight='balanced',max_iter=1000))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-1" type="checkbox" ><label for="sk-estimator-id-1" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[('easypreprocessor',EasyPreprocessor(types= continuous dirty_float low_card_int ... date free_string useless
v_21 False False False ... False False False
v_32 True False False ... False False False
v_15 False False False ... False False False
v_4 True False False ... False False False
v_1 False False False ... False False False
v_8 False False False ... False False False
v_12 False False Fa...
v_34 False False False ... False False False
v_35 True False False ... False False False
v_36 True False False ... False False False
v_37 True False False ... False False False
v_38 True False False ... False False False
v_39 True False False ... False False False
v_40 False False False ... False False False[40 rows x 7 columns])),('logisticregression',LogisticRegression(C=0.1, class_weight='balanced',max_iter=1000))])</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-2" type="checkbox" ><label for="sk-estimator-id-2" class="sk-toggleable__label sk-toggleable__label-arrow">EasyPreprocessor</label><div class="sk-toggleable__content"><pre>EasyPreprocessor(types= continuous dirty_float low_card_int ... date free_string useless
v_21 False False False ... False False False
v_32 True False False ... False False False
v_15 False False False ... False False False
v_4 True False False ... False False False
v_1 False False False ... False False False
v_8 False False False ... False False False
v_12 False False False ... False False False
v_25 True False Fa...
v_7 True False False ... False False False
v_2 True False False ... False False False
v_16 True False False ... False False False
v_34 False False False ... False False False
v_35 True False False ... False False False
v_36 True False False ... False False False
v_37 True False False ... False False False
v_38 True False False ... False False False
v_39 True False False ... False False False
v_40 False False False ... False False False[40 rows x 7 columns])</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-3" type="checkbox" ><label for="sk-estimator-id-3" class="sk-toggleable__label sk-toggleable__label-arrow">LogisticRegression</label><div class="sk-toggleable__content"><pre>LogisticRegression(C=0.1, class_weight='balanced', max_iter=1000)</pre></div></div></div></div></div></div></div>
**Disclaimer:** This model is trained with dabl library as a baseline, for better results, use [AutoTrain](https://huggingface.co/autotrain).
**Logs of training** including the models tried in the process can be found in logs.txt |
jonatasgrosman/exp_w2v2t_pt_r-wav2vec2_s732 | jonatasgrosman | 2022-07-11T19:54:54Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"pt",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2022-07-11T19:54:29Z | ---
language:
- pt
license: apache-2.0
tags:
- automatic-speech-recognition
- pt
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_pt_r-wav2vec2_s732
Fine-tuned [facebook/wav2vec2-large-robust](https://huggingface.co/facebook/wav2vec2-large-robust) for speech recognition using the train split of [Common Voice 7.0 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_pt_r-wav2vec2_s468 | jonatasgrosman | 2022-07-11T19:48:19Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"pt",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2022-07-11T19:47:54Z | ---
language:
- pt
license: apache-2.0
tags:
- automatic-speech-recognition
- pt
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_pt_r-wav2vec2_s468
Fine-tuned [facebook/wav2vec2-large-robust](https://huggingface.co/facebook/wav2vec2-large-robust) for speech recognition using the train split of [Common Voice 7.0 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_pt_xls-r_s689 | jonatasgrosman | 2022-07-11T19:41:36Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"pt",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2022-07-11T19:40:50Z | ---
language:
- pt
license: apache-2.0
tags:
- automatic-speech-recognition
- pt
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_pt_xls-r_s689
Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_pt_xls-r_s17 | jonatasgrosman | 2022-07-11T19:38:03Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"pt",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2022-07-11T19:37:21Z | ---
language:
- pt
license: apache-2.0
tags:
- automatic-speech-recognition
- pt
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_pt_xls-r_s17
Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
KD02/distilbert-base-uncased-finetuned-squad | KD02 | 2022-07-11T19:37:22Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| question-answering | 2022-07-11T14:14:25Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: distilbert-base-uncased-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [KD02/distilbert-base-uncased-finetuned-squad](https://huggingface.co/KD02/distilbert-base-uncased-finetuned-squad) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
jonatasgrosman/exp_w2v2t_pt_unispeech-sat_s103 | jonatasgrosman | 2022-07-11T19:34:07Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"unispeech-sat",
"automatic-speech-recognition",
"pt",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2022-07-11T19:33:36Z | ---
language:
- pt
license: apache-2.0
tags:
- automatic-speech-recognition
- pt
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_pt_unispeech-sat_s103
Fine-tuned [microsoft/unispeech-sat-large](https://huggingface.co/microsoft/unispeech-sat-large) for speech recognition using the train split of [Common Voice 7.0 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_pt_unispeech-sat_s377 | jonatasgrosman | 2022-07-11T19:30:24Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"unispeech-sat",
"automatic-speech-recognition",
"pt",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2022-07-11T19:29:59Z | ---
language:
- pt
license: apache-2.0
tags:
- automatic-speech-recognition
- pt
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_pt_unispeech-sat_s377
Fine-tuned [microsoft/unispeech-sat-large](https://huggingface.co/microsoft/unispeech-sat-large) for speech recognition using the train split of [Common Voice 7.0 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_pt_vp-nl_s783 | jonatasgrosman | 2022-07-11T19:23:52Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"pt",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2022-07-11T19:23:20Z | ---
language:
- pt
license: apache-2.0
tags:
- automatic-speech-recognition
- pt
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_pt_vp-nl_s783
Fine-tuned [facebook/wav2vec2-large-nl-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-nl-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_pt_vp-nl_s6 | jonatasgrosman | 2022-07-11T19:17:20Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"pt",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2022-07-11T19:16:53Z | ---
language:
- pt
license: apache-2.0
tags:
- automatic-speech-recognition
- pt
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_pt_vp-nl_s6
Fine-tuned [facebook/wav2vec2-large-nl-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-nl-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_pt_vp-nl_s833 | jonatasgrosman | 2022-07-11T19:13:31Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"pt",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2022-07-11T19:12:53Z | ---
language:
- pt
license: apache-2.0
tags:
- automatic-speech-recognition
- pt
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_pt_vp-nl_s833
Fine-tuned [facebook/wav2vec2-large-nl-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-nl-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_pt_vp-es_s291 | jonatasgrosman | 2022-07-11T19:09:42Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"pt",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2022-07-11T19:08:58Z | ---
language:
- pt
license: apache-2.0
tags:
- automatic-speech-recognition
- pt
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_pt_vp-es_s291
Fine-tuned [facebook/wav2vec2-large-es-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-es-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_pt_vp-es_s506 | jonatasgrosman | 2022-07-11T19:05:37Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"pt",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2022-07-11T19:04:54Z | ---
language:
- pt
license: apache-2.0
tags:
- automatic-speech-recognition
- pt
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_pt_vp-es_s506
Fine-tuned [facebook/wav2vec2-large-es-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-es-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jamie613/mt5_fill_puntuation | jamie613 | 2022-07-11T19:00:55Z | 16 | 0 | transformers | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-05-31T01:33:50Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: mt5_fill_puntuation
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mt5_fill_puntuation
This model is a fine-tuned version of [jamie613/mt5_fill_puntuation](https://huggingface.co/jamie613/mt5_fill_puntuation) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0717
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 5
- eval_batch_size: 5
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.0918 | 0.04 | 500 | 0.0803 |
| 0.0894 | 0.07 | 1000 | 0.0773 |
| 0.0905 | 0.11 | 1500 | 0.0822 |
| 0.0908 | 0.15 | 2000 | 0.0833 |
| 0.0868 | 0.18 | 2500 | 0.0840 |
| 0.09 | 0.22 | 3000 | 0.0811 |
| 0.0868 | 0.26 | 3500 | 0.0735 |
| 0.0869 | 0.29 | 4000 | 0.0805 |
| 0.0874 | 0.33 | 4500 | 0.0742 |
| 0.088 | 0.37 | 5000 | 0.0749 |
| 0.0884 | 0.4 | 5500 | 0.0730 |
| 0.0861 | 0.44 | 6000 | 0.0749 |
| 0.0804 | 0.48 | 6500 | 0.0739 |
| 0.0845 | 0.51 | 7000 | 0.0717 |
| 0.0861 | 0.55 | 7500 | 0.0743 |
| 0.0812 | 0.59 | 8000 | 0.0726 |
| 0.0824 | 0.62 | 8500 | 0.0729 |
| 0.0836 | 0.66 | 9000 | 0.0751 |
| 0.079 | 0.7 | 9500 | 0.0731 |
| 0.0806 | 0.73 | 10000 | 0.0725 |
| 0.0798 | 0.77 | 10500 | 0.0749 |
| 0.0794 | 0.81 | 11000 | 0.0725 |
| 0.0795 | 0.84 | 11500 | 0.0726 |
| 0.0755 | 0.88 | 12000 | 0.0732 |
| 0.0815 | 0.92 | 12500 | 0.0722 |
| 0.0776 | 0.95 | 13000 | 0.0719 |
| 0.0838 | 0.99 | 13500 | 0.0717 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
jonatasgrosman/exp_w2v2t_pt_vp-fr_s485 | jonatasgrosman | 2022-07-11T18:54:15Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"pt",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2022-07-11T18:53:30Z | ---
language:
- pt
license: apache-2.0
tags:
- automatic-speech-recognition
- pt
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_pt_vp-fr_s485
Fine-tuned [facebook/wav2vec2-large-fr-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-fr-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
quanxi/dqn-SpaceInvadersNoFrameskip-v4 | quanxi | 2022-07-11T18:32:52Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-07-11T18:32:11Z | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- metrics:
- type: mean_reward
value: 596.50 +/- 113.18
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
```
# Download model and save it into the logs/ folder
python -m utils.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga quanxi -f logs/
python enjoy.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m utils.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga quanxi
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', True),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
jonatasgrosman/exp_w2v2t_pt_unispeech-ml_s808 | jonatasgrosman | 2022-07-11T18:31:15Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"unispeech",
"automatic-speech-recognition",
"pt",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2022-07-11T18:30:46Z | ---
language:
- pt
license: apache-2.0
tags:
- automatic-speech-recognition
- pt
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_pt_unispeech-ml_s808
Fine-tuned [microsoft/unispeech-large-multi-lingual-1500h-cv](https://huggingface.co/microsoft/unispeech-large-multi-lingual-1500h-cv) for speech recognition using the train split of [Common Voice 7.0 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_pt_wavlm_s51 | jonatasgrosman | 2022-07-11T18:10:28Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"wavlm",
"automatic-speech-recognition",
"pt",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2022-07-11T18:09:52Z | ---
language:
- pt
license: apache-2.0
tags:
- automatic-speech-recognition
- pt
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_pt_wavlm_s51
Fine-tuned [microsoft/wavlm-large](https://huggingface.co/microsoft/wavlm-large) for speech recognition using the train split of [Common Voice 7.0 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_pt_no-pretraining_s34 | jonatasgrosman | 2022-07-11T18:06:01Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"pt",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2022-07-11T18:05:36Z | ---
language:
- pt
license: apache-2.0
tags:
- automatic-speech-recognition
- pt
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_pt_no-pretraining_s34
Fine-tuned randomly initialized wav2vec2 model for speech recognition using the train split of [Common Voice 7.0 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_pt_vp-sv_s894 | jonatasgrosman | 2022-07-11T17:54:51Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"pt",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2022-07-11T17:54:09Z | ---
language:
- pt
license: apache-2.0
tags:
- automatic-speech-recognition
- pt
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_pt_vp-sv_s894
Fine-tuned [facebook/wav2vec2-large-sv-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-sv-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_pt_vp-sv_s612 | jonatasgrosman | 2022-07-11T17:47:36Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"pt",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2022-07-11T17:47:09Z | ---
language:
- pt
license: apache-2.0
tags:
- automatic-speech-recognition
- pt
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_pt_vp-sv_s612
Fine-tuned [facebook/wav2vec2-large-sv-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-sv-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_pt_hubert_s486 | jonatasgrosman | 2022-07-11T17:43:15Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"hubert",
"automatic-speech-recognition",
"pt",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2022-07-11T17:42:50Z | ---
language:
- pt
license: apache-2.0
tags:
- automatic-speech-recognition
- pt
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_pt_hubert_s486
Fine-tuned [facebook/hubert-large-ll60k](https://huggingface.co/facebook/hubert-large-ll60k) for speech recognition using the train split of [Common Voice 7.0 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_pt_hubert_s301 | jonatasgrosman | 2022-07-11T17:40:03Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"hubert",
"automatic-speech-recognition",
"pt",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2022-07-11T17:39:41Z | ---
language:
- pt
license: apache-2.0
tags:
- automatic-speech-recognition
- pt
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_pt_hubert_s301
Fine-tuned [facebook/hubert-large-ll60k](https://huggingface.co/facebook/hubert-large-ll60k) for speech recognition using the train split of [Common Voice 7.0 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_pt_unispeech_s474 | jonatasgrosman | 2022-07-11T17:29:59Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"unispeech",
"automatic-speech-recognition",
"pt",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2022-07-11T17:29:33Z | ---
language:
- pt
license: apache-2.0
tags:
- automatic-speech-recognition
- pt
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_pt_unispeech_s474
Fine-tuned [microsoft/unispeech-large-1500h-cv](https://huggingface.co/microsoft/unispeech-large-1500h-cv) for speech recognition using the train split of [Common Voice 7.0 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
kinanmartin/xlm-roberta-large-ner-hrl-finetuned-ner | kinanmartin | 2022-07-11T17:29:06Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:toydata",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| token-classification | 2022-07-11T03:49:46Z | ---
tags:
- generated_from_trainer
datasets:
- toydata
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: xlm-roberta-large-ner-hrl-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: toydata
type: toydata
args: SDN
metrics:
- name: Precision
type: precision
value: 0.9132452695465905
- name: Recall
type: recall
value: 0.9205854126679462
- name: F1
type: f1
value: 0.9169006511739053
- name: Accuracy
type: accuracy
value: 0.9784804945824268
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-large-ner-hrl-finetuned-ner
This model is a fine-tuned version of [Davlan/xlm-roberta-large-ner-hrl](https://huggingface.co/Davlan/xlm-roberta-large-ner-hrl) on the toydata dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0944
- Precision: 0.9132
- Recall: 0.9206
- F1: 0.9169
- Accuracy: 0.9785
## 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 | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 408 | 0.0900 | 0.8508 | 0.9303 | 0.8888 | 0.9719 |
| 0.1087 | 2.0 | 816 | 0.0827 | 0.9043 | 0.9230 | 0.9136 | 0.9783 |
| 0.0503 | 3.0 | 1224 | 0.0944 | 0.9132 | 0.9206 | 0.9169 | 0.9785 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
jonatasgrosman/exp_w2v2t_pt_xlsr-53_s829 | jonatasgrosman | 2022-07-11T17:23:34Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"pt",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2022-07-11T17:23:00Z | ---
language:
- pt
license: apache-2.0
tags:
- automatic-speech-recognition
- pt
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_pt_xlsr-53_s829
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) for speech recognition using the train split of [Common Voice 7.0 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_pt_xlsr-53_s677 | jonatasgrosman | 2022-07-11T17:17:00Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"pt",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2022-07-11T17:16:33Z | ---
language:
- pt
license: apache-2.0
tags:
- automatic-speech-recognition
- pt
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_pt_xlsr-53_s677
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) for speech recognition using the train split of [Common Voice 7.0 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_pt_vp-100k_s69 | jonatasgrosman | 2022-07-11T17:13:45Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"pt",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2022-07-11T17:13:17Z | ---
language:
- pt
license: apache-2.0
tags:
- automatic-speech-recognition
- pt
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_pt_vp-100k_s69
Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_pt_vp-100k_s660 | jonatasgrosman | 2022-07-11T17:10:21Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"pt",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2022-07-11T17:09:38Z | ---
language:
- pt
license: apache-2.0
tags:
- automatic-speech-recognition
- pt
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_pt_vp-100k_s660
Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_pt_wav2vec2_s250 | jonatasgrosman | 2022-07-11T16:51:46Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"pt",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2022-07-11T16:51:14Z | ---
language:
- pt
license: apache-2.0
tags:
- automatic-speech-recognition
- pt
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_pt_wav2vec2_s250
Fine-tuned [facebook/wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60) for speech recognition using the train split of [Common Voice 7.0 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_es_vp-it_s179 | jonatasgrosman | 2022-07-11T16:44:55Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"es",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2022-07-11T16:44:09Z | ---
language:
- es
license: apache-2.0
tags:
- automatic-speech-recognition
- es
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_es_vp-it_s179
Fine-tuned [facebook/wav2vec2-large-it-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-it-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jorge-henao/gpt2-small-spanish-historias-conflicto-colpoetry-historias-conflicto-col | jorge-henao | 2022-07-11T16:43:58Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2022-07-11T16:29:51Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: gpt2-small-spanish-historias-conflicto-colpoetry-historias-conflicto-col
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt2-small-spanish-historias-conflicto-colpoetry-historias-conflicto-col
This model is a fine-tuned version of [jorge-henao/gpt2-small-spanish-historias-conflicto-col](https://huggingface.co/jorge-henao/gpt2-small-spanish-historias-conflicto-col) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 4.5017
## 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: 6
- eval_batch_size: 6
- 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 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
jonatasgrosman/exp_w2v2t_es_vp-it_s438 | jonatasgrosman | 2022-07-11T16:41:02Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"es",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2022-07-11T16:40:28Z | ---
language:
- es
license: apache-2.0
tags:
- automatic-speech-recognition
- es
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_es_vp-it_s438
Fine-tuned [facebook/wav2vec2-large-it-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-it-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
alefarasin/testpyramidsrnd | alefarasin | 2022-07-11T16:37:44Z | 5 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
]
| reinforcement-learning | 2022-07-11T16:37:35Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
library_name: ml-agents
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids
2. Step 1: Write your model_id: alefarasin/testpyramidsrnd
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
jonatasgrosman/exp_w2v2t_es_r-wav2vec2_s227 | jonatasgrosman | 2022-07-11T16:34:37Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"es",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2022-07-11T16:33:36Z | ---
language:
- es
license: apache-2.0
tags:
- automatic-speech-recognition
- es
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_es_r-wav2vec2_s227
Fine-tuned [facebook/wav2vec2-large-robust](https://huggingface.co/facebook/wav2vec2-large-robust) for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_es_r-wav2vec2_s870 | jonatasgrosman | 2022-07-11T16:30:36Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"es",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2022-07-11T16:29:58Z | ---
language:
- es
license: apache-2.0
tags:
- automatic-speech-recognition
- es
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_es_r-wav2vec2_s870
Fine-tuned [facebook/wav2vec2-large-robust](https://huggingface.co/facebook/wav2vec2-large-robust) for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_es_r-wav2vec2_s809 | jonatasgrosman | 2022-07-11T16:26:53Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"es",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2022-07-11T16:26:08Z | ---
language:
- es
license: apache-2.0
tags:
- automatic-speech-recognition
- es
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_es_r-wav2vec2_s809
Fine-tuned [facebook/wav2vec2-large-robust](https://huggingface.co/facebook/wav2vec2-large-robust) for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
AdiKompella/Reinforce-CartPole | AdiKompella | 2022-07-11T16:26:05Z | 0 | 0 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-07-11T16:25:53Z | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole
results:
- metrics:
- type: mean_reward
value: 276.70 +/- 57.60
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
|
jonatasgrosman/exp_w2v2t_es_xls-r_s691 | jonatasgrosman | 2022-07-11T16:19:22Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"es",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2022-07-11T16:18:30Z | ---
language:
- es
license: apache-2.0
tags:
- automatic-speech-recognition
- es
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_es_xls-r_s691
Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_es_unispeech-sat_s42 | jonatasgrosman | 2022-07-11T16:09:10Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"unispeech-sat",
"automatic-speech-recognition",
"es",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2022-07-11T16:08:25Z | ---
language:
- es
license: apache-2.0
tags:
- automatic-speech-recognition
- es
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_es_unispeech-sat_s42
Fine-tuned [microsoft/unispeech-sat-large](https://huggingface.co/microsoft/unispeech-sat-large) for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
Subsets and Splits