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huggingtweets/coffee__burger
ca8f5e0c262ae77d1b9198007589167dd5fcb932
2022-03-01T09:06:14.000Z
[ "pytorch", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]
text-generation
false
huggingtweets
null
huggingtweets/coffee__burger
0
null
transformers
36,400
--- language: en thumbnail: http://www.huggingtweets.com/coffee__burger/1646125569654/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(&#39;https://pbs.twimg.com/profile_images/794725967948181506/Zn4x_F6i_400x400.jpg&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </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">Coffee Burger</div> <div style="text-align: center; font-size: 14px;">@coffee__burger</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. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) 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 Coffee Burger. | Data | Coffee Burger | | --- | --- | | Tweets downloaded | 2471 | | Retweets | 525 | | Short tweets | 337 | | Tweets kept | 1609 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3ad82qis/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 @coffee__burger's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1kxzm2oz) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1kxzm2oz/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/coffee__burger') 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* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
huggingtweets/berniesanders-cnn-dril
575e2ad494733509ce6742c0d8e210c974e0ceca
2022-03-01T09:43:27.000Z
[ "pytorch", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]
text-generation
false
huggingtweets
null
huggingtweets/berniesanders-cnn-dril
0
null
transformers
36,401
--- language: en thumbnail: http://www.huggingtweets.com/berniesanders-cnn-dril/1646127802129/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(&#39;https://pbs.twimg.com/profile_images/1097820307388334080/9ddg5F6v_400x400.png&#39;)"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/847818629840228354/VXyQHfn0_400x400.jpg&#39;)"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/1278259160644227073/MfCyF7CG_400x400.jpg&#39;)"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Bernie Sanders & wint & CNN</div> <div style="text-align: center; font-size: 14px;">@berniesanders-cnn-dril</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. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) 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 Bernie Sanders & wint & CNN. | Data | Bernie Sanders | wint | CNN | | --- | --- | --- | --- | | Tweets downloaded | 3250 | 3229 | 3250 | | Retweets | 429 | 473 | 30 | | Short tweets | 10 | 300 | 6 | | Tweets kept | 2811 | 2456 | 3214 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1yapgpjj/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 @berniesanders-cnn-dril's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1hmm651a) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1hmm651a/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/berniesanders-cnn-dril') 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* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
huggingtweets/berniesanders-dril
33afaa0d841cd7a3b56fd8e491ec80a255ada2b0
2022-03-01T10:13:41.000Z
[ "pytorch", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]
text-generation
false
huggingtweets
null
huggingtweets/berniesanders-dril
0
null
transformers
36,402
--- 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(&#39;https://pbs.twimg.com/profile_images/847818629840228354/VXyQHfn0_400x400.jpg&#39;)"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/1097820307388334080/9ddg5F6v_400x400.png&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">wint & Bernie Sanders</div> <div style="text-align: center; font-size: 14px;">@berniesanders-dril</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. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) 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 wint & Bernie Sanders. | Data | wint | Bernie Sanders | | --- | --- | --- | | Tweets downloaded | 3229 | 3250 | | Retweets | 473 | 429 | | Short tweets | 300 | 10 | | Tweets kept | 2456 | 2811 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/yw6378l1/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 @berniesanders-dril's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3pydufi9) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3pydufi9/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/berniesanders-dril') 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* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
huggingtweets/janieclone
1a6d8a7aa7fd819487b7d4d248791de48524737a
2022-07-13T17:02:02.000Z
[ "pytorch", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]
text-generation
false
huggingtweets
null
huggingtweets/janieclone
0
null
transformers
36,403
--- language: en thumbnail: http://www.huggingtweets.com/janieclone/1657731718034/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(&#39;https://pbs.twimg.com/profile_images/1536389142287892481/N6kCwACw_400x400.jpg&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </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">Columbine Janie</div> <div style="text-align: center; font-size: 14px;">@janieclone</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. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) 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 Columbine Janie. | Data | Columbine Janie | | --- | --- | | Tweets downloaded | 2409 | | Retweets | 1025 | | Short tweets | 332 | | Tweets kept | 1052 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1jcqf2hu/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 @janieclone's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/u7quubhw) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/u7quubhw/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/janieclone') 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* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
xdmason/pretrainedCas
d66136319fbd11c6544dad149765829297facd60
2022-03-02T00:58:13.000Z
[ "pytorch", "gpt2", "transformers", "conversational" ]
conversational
false
xdmason
null
xdmason/pretrainedCas
0
null
transformers
36,404
--- tags: - conversational --- # pretrained Cas Model
jiobiala24/wav2vec2-base-checkpoint-14
9031ee79209a12fa11467679412f99eefbfdd2af
2022-03-02T15:13:04.000Z
[ "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "dataset:common_voice", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
automatic-speech-recognition
false
jiobiala24
null
jiobiala24/wav2vec2-base-checkpoint-14
0
null
transformers
36,405
--- license: apache-2.0 tags: - generated_from_trainer datasets: - common_voice model-index: - name: wav2vec2-base-checkpoint-14 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-checkpoint-14 This model is a fine-tuned version of [jiobiala24/wav2vec2-base-checkpoint-13](https://huggingface.co/jiobiala24/wav2vec2-base-checkpoint-13) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 1.2822 - Wer: 0.4068 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 0.1996 | 1.59 | 1000 | 0.7181 | 0.4079 | | 0.1543 | 3.17 | 2000 | 0.7735 | 0.4113 | | 0.1171 | 4.76 | 3000 | 0.8152 | 0.4045 | | 0.0969 | 6.35 | 4000 | 0.8575 | 0.4142 | | 0.082 | 7.94 | 5000 | 0.9005 | 0.4124 | | 0.074 | 9.52 | 6000 | 0.9232 | 0.4151 | | 0.0653 | 11.11 | 7000 | 0.9680 | 0.4223 | | 0.0587 | 12.7 | 8000 | 1.0633 | 0.4232 | | 0.0551 | 14.29 | 9000 | 1.0875 | 0.4171 | | 0.0498 | 15.87 | 10000 | 1.0281 | 0.4105 | | 0.0443 | 17.46 | 11000 | 1.2164 | 0.4274 | | 0.0421 | 19.05 | 12000 | 1.1868 | 0.4191 | | 0.0366 | 20.63 | 13000 | 1.1678 | 0.4173 | | 0.0366 | 22.22 | 14000 | 1.2444 | 0.4187 | | 0.0346 | 23.81 | 15000 | 1.2042 | 0.4169 | | 0.0316 | 25.4 | 16000 | 1.3019 | 0.4127 | | 0.0296 | 26.98 | 17000 | 1.2001 | 0.4081 | | 0.0281 | 28.57 | 18000 | 1.2822 | 0.4068 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.13.3 - Tokenizers 0.10.3
prk/roberta-base-squad2-finetuned-squad
15b151de471fcc120a3fecf27c4d2891c0b01336
2022-03-03T10:26:14.000Z
[ "pytorch", "tensorboard", "roberta", "question-answering", "dataset:squad_v2", "transformers", "generated_from_trainer", "license:cc-by-4.0", "model-index", "autotrain_compatible" ]
question-answering
false
prk
null
prk/roberta-base-squad2-finetuned-squad
0
null
transformers
36,406
--- license: cc-by-4.0 tags: - generated_from_trainer datasets: - squad_v2 model-index: - name: roberta-base-squad2-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. --> # roberta-base-squad2-finetuned-squad This model is a fine-tuned version of [deepset/roberta-base-squad2](https://huggingface.co/deepset/roberta-base-squad2) on a custom dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 8 | 0.1894 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.6
nimrah/wav2vec2-large-xls-r-300m-turkish-colab
0f3b3b889009da84a585add22e109e41053b2e46
2022-03-02T08:18:47.000Z
[ "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "dataset:common_voice", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
automatic-speech-recognition
false
nimrah
null
nimrah/wav2vec2-large-xls-r-300m-turkish-colab
0
null
transformers
36,407
--- license: apache-2.0 tags: - generated_from_trainer datasets: - common_voice model-index: - name: wav2vec2-large-xls-r-300m-turkish-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-large-xls-r-300m-turkish-colab This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 3.2970 - Wer: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.1 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:---:| | 6.1837 | 3.67 | 400 | 3.2970 | 1.0 | | 0.0 | 7.34 | 800 | 3.2970 | 1.0 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.10.3
facebook/maskformer-swin-tiny-ade
80bb6d935ed12f2f2dfabbf44772a33821aac9f0
2022-04-04T16:02:00.000Z
[ "pytorch", "maskformer", "dataset:ade-20k", "arxiv:2107.06278", "transformers", "vision", "image-segmentatiom", "license:apache-2.0" ]
null
false
facebook
null
facebook/maskformer-swin-tiny-ade
0
null
transformers
36,408
--- license: apache-2.0 tags: - vision - image-segmentatiom datasets: - ade-20k widget: - src: https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg example_title: House - src: https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000002.jpg example_title: Castle --- # Mask Mask model trained on ade-20k. It was introduced in the paper [Per-Pixel Classification is Not All You Need for Semantic Segmentation](https://arxiv.org/abs/2107.06278) and first released in [this repository](https://github.com/facebookresearch/MaskFormer/blob/da3e60d85fdeedcb31476b5edd7d328826ce56cc/mask_former/modeling/criterion.py#L169). Disclaimer: The team releasing Mask did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description MaskFormer addresses semantic segmentation with a mask classification paradigm instead. ![model image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/maskformer_architecture.png) ## Intended uses & limitations You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=maskformer) to look for fine-tuned versions on a task that interests you. ### How to use Here is how to use this model: ```python >>> from transformers import MaskFormerFeatureExtractor, MaskFormerForInstanceSegmentation >>> 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 = MaskFormerFeatureExtractor.from_pretrained("facebook/maskformer-swin-base-ade") >>> inputs = feature_extractor(images=image, return_tensors="pt") >>> model = MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-base-ade") >>> outputs = model(**inputs) >>> # model predicts class_queries_logits of shape `(batch_size, num_queries)` >>> # and masks_queries_logits of shape `(batch_size, num_queries, height, width)` >>> class_queries_logits = outputs.class_queries_logits >>> masks_queries_logits = outputs.masks_queries_logits >>> # you can pass them to feature_extractor for postprocessing >>> output = feature_extractor.post_process_segmentation(outputs) >>> output = feature_extractor.post_process_semantic_segmentation(outputs) >>> output = feature_extractor.post_process_panoptic_segmentation(outputs) ``` For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/maskformer).
nimrah/wav2vec2-large-xls-r-300m-turkish-colab-4
d597872df47dad4f9b80e88d855689c1929a9f4f
2022-03-02T15:54:07.000Z
[ "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "dataset:common_voice", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
automatic-speech-recognition
false
nimrah
null
nimrah/wav2vec2-large-xls-r-300m-turkish-colab-4
0
null
transformers
36,409
--- license: apache-2.0 tags: - generated_from_trainer datasets: - common_voice model-index: - name: wav2vec2-large-xls-r-300m-turkish-colab-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. --> # wav2vec2-large-xls-r-300m-turkish-colab-4 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.1 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.10.3
mcdzwil/distilbert-base-uncased-finetuned-ner
bb59e31745413ef43c63e8461b4a671649fa2e70
2022-03-02T16:35:26.000Z
[ "pytorch", "tensorboard", "distilbert", "token-classification", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]
token-classification
false
mcdzwil
null
mcdzwil/distilbert-base-uncased-finetuned-ner
0
null
transformers
36,410
--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: distilbert-base-uncased-finetuned-ner 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-ner 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: 0.1830 - Precision: 0.9171 - Recall: 0.7099 - F1: 0.8003 - Accuracy: 0.9316 ## 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 | 48 | 0.2903 | 0.7952 | 0.7063 | 0.7481 | 0.9136 | | No log | 2.0 | 96 | 0.2015 | 0.9154 | 0.7075 | 0.7981 | 0.9298 | | No log | 3.0 | 144 | 0.1830 | 0.9171 | 0.7099 | 0.8003 | 0.9316 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.6
repro-rights-amicus-briefs/legal-bert-base-uncased-finetuned-RRamicus
af97cbc05339b4c75862c20d8bb04f499c610741
2022-03-03T20:21:45.000Z
[ "pytorch", "tensorboard", "bert", "fill-mask", "transformers", "generated_from_trainer", "license:cc-by-sa-4.0", "model-index", "autotrain_compatible" ]
fill-mask
false
repro-rights-amicus-briefs
null
repro-rights-amicus-briefs/legal-bert-base-uncased-finetuned-RRamicus
0
null
transformers
36,411
--- license: cc-by-sa-4.0 tags: - generated_from_trainer model-index: - name: legal-bert-base-uncased-finetuned-RRamicus 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. --> # legal-bert-base-uncased-finetuned-RRamicus This model is a fine-tuned version of [nlpaueb/legal-bert-base-uncased](https://huggingface.co/nlpaueb/legal-bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.1520 ## 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: 928 - 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 | |:-------------:|:-----:|:-----:|:---------------:| | 1.021 | 1.0 | 1118 | 1.3393 | | 1.2272 | 2.0 | 2236 | 1.2612 | | 1.2467 | 3.0 | 3354 | 1.2403 | | 1.2149 | 4.0 | 4472 | 1.2276 | | 1.1855 | 5.0 | 5590 | 1.2101 | | 1.1674 | 6.0 | 6708 | 1.2020 | | 1.1508 | 7.0 | 7826 | 1.1893 | | 1.1386 | 8.0 | 8944 | 1.1870 | | 1.129 | 9.0 | 10062 | 1.1794 | | 1.1193 | 10.0 | 11180 | 1.1759 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.6
huggingtweets/xqc
3b78597ad334ae43c3f557b9daef464464345613
2022-03-03T04:24:49.000Z
[ "pytorch", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]
text-generation
false
huggingtweets
null
huggingtweets/xqc
0
null
transformers
36,412
--- language: en thumbnail: http://www.huggingtweets.com/xqc/1646281436978/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(&#39;https://pbs.twimg.com/profile_images/1188911868863221772/fpcyKuIW_400x400.jpg&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </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">xQc</div> <div style="text-align: center; font-size: 14px;">@xqc</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. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) 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 xQc. | Data | xQc | | --- | --- | | Tweets downloaded | 3203 | | Retweets | 128 | | Short tweets | 406 | | Tweets kept | 2669 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1w7gqt7r/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 @xqc's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3j2p63io) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3j2p63io/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/xqc') 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* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
mmaguero/gn-bert-base-cased
9d03ff9190236e4b6732bb87d1b9e67f875a2f38
2022-03-06T08:05:18.000Z
[ "pytorch", "bert", "fill-mask", "gn", "dataset:wikipedia", "dataset:wiktionary", "transformers", "license:mit", "autotrain_compatible" ]
fill-mask
false
mmaguero
null
mmaguero/gn-bert-base-cased
0
null
transformers
36,413
--- language: gn license: mit datasets: - wikipedia - wiktionary widget: - text: "Paraguay ha'e peteĩ táva oĩva [MASK] retãme " --- # BERT-i-base-cased (gnBERT-base-cased) A pre-trained BERT model for **Guarani** (12 layers, cased). Trained on Wikipedia + Wiktionary (~800K tokens).
tiot07/wav2vec2-base-timit-demo-colab-large
b9b08abfe84a6bad1ed2d66445e05b24968caaf1
2022-03-04T09:34:23.000Z
[ "pytorch", "wav2vec2", "automatic-speech-recognition", "transformers" ]
automatic-speech-recognition
false
tiot07
null
tiot07/wav2vec2-base-timit-demo-colab-large
0
null
transformers
36,414
Entry not found
nimrah/wav2vec2-large-xls-r-300m-hindi_home-colab-11
a918b00fa991213a5a23a5c20448c006a994fe27
2022-03-04T16:41:25.000Z
[ "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "dataset:common_voice", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
automatic-speech-recognition
false
nimrah
null
nimrah/wav2vec2-large-xls-r-300m-hindi_home-colab-11
0
null
transformers
36,415
--- license: apache-2.0 tags: - generated_from_trainer datasets: - common_voice model-index: - name: wav2vec2-large-xls-r-300m-hindi_home-colab-11 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-large-xls-r-300m-hindi_home-colab-11 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 3.7649 - Wer: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.03 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 50 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:---:| | 5.5971 | 44.43 | 400 | 3.7649 | 1.0 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.13.3 - Tokenizers 0.10.3
nimrah/wav2vec2-large-xls-r-300m-turkish-colab-9
8935c0128bfdaed4737e783700cfdd2d4db85325
2022-03-04T18:24:21.000Z
[ "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "dataset:common_voice", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
automatic-speech-recognition
false
nimrah
null
nimrah/wav2vec2-large-xls-r-300m-turkish-colab-9
0
null
transformers
36,416
--- license: apache-2.0 tags: - generated_from_trainer datasets: - common_voice model-index: - name: wav2vec2-large-xls-r-300m-turkish-colab-9 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-large-xls-r-300m-turkish-colab-9 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.03 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.10.3
petrichorRainbow/mrf-T5
403dc9990544b8fd803c2cbc0d4690c4bdd5c6f8
2022-03-07T18:59:39.000Z
[ "pytorch", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
petrichorRainbow
null
petrichorRainbow/mrf-T5
0
null
transformers
36,417
Entry not found
infinitylyj/DialogGPT-small-rick
a76452c69f5a4a0c6c1bf20e8dd235b3c6571895
2022-03-05T06:55:42.000Z
[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]
conversational
false
infinitylyj
null
infinitylyj/DialogGPT-small-rick
0
null
transformers
36,418
--- tags: - conversational --- # Rick DialogGPT Model
naam/xlm-roberta-base-finetuned-panx-de
9674c14b9cfbb6f7c0c97de5b204e4994ca8342a
2022-03-05T13:48:33.000Z
[ "pytorch", "tensorboard", "xlm-roberta", "token-classification", "dataset:xtreme", "transformers", "generated_from_trainer", "license:mit", "model-index", "autotrain_compatible" ]
token-classification
false
naam
null
naam/xlm-roberta-base-finetuned-panx-de
0
null
transformers
36,419
--- 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: 0.8594910162670748 --- <!-- 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: 0.1348 - F1: 0.8595 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2556 | 1.0 | 525 | 0.1629 | 0.8218 | | 0.1309 | 2.0 | 1050 | 0.1378 | 0.8522 | | 0.0812 | 3.0 | 1575 | 0.1348 | 0.8595 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.9.1 - Datasets 1.16.1 - Tokenizers 0.10.3
infinitylyj/DialogGPT-medium-general
a4c065d70fc00ceeca9265886b46876924b03975
2022-03-05T13:45:57.000Z
[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]
conversational
false
infinitylyj
null
infinitylyj/DialogGPT-medium-general
0
null
transformers
36,420
--- tags: - conversational --- # General DialogGPT Model
nimrah/my-wav2vec2-base-timit-demo-colab-my
6d864f73896c0afcd833cb6d1fb787c50ab66c6a
2022-03-05T17:06:37.000Z
[ "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
automatic-speech-recognition
false
nimrah
null
nimrah/my-wav2vec2-base-timit-demo-colab-my
0
null
transformers
36,421
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: my-wav2vec2-base-timit-demo-colab-my results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # my-wav2vec2-base-timit-demo-colab-my 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: 0.5569 - Wer: 0.3481 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.4083 | 4.0 | 500 | 1.0932 | 0.7510 | | 0.5536 | 8.0 | 1000 | 0.4965 | 0.4819 | | 0.2242 | 12.0 | 1500 | 0.4779 | 0.4077 | | 0.1249 | 16.0 | 2000 | 0.4921 | 0.4006 | | 0.0844 | 20.0 | 2500 | 0.4809 | 0.3753 | | 0.0613 | 24.0 | 3000 | 0.5307 | 0.3680 | | 0.0459 | 28.0 | 3500 | 0.5569 | 0.3481 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.10.3
huggingtweets/ragnar_furup
de6725c9b840c44248a33362e3898e8a6f894ac2
2022-03-05T18:34:56.000Z
[ "pytorch", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]
text-generation
false
huggingtweets
null
huggingtweets/ragnar_furup
0
null
transformers
36,422
--- language: en thumbnail: http://www.huggingtweets.com/ragnar_furup/1646505291174/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(&#39;https://pbs.twimg.com/profile_images/1500138558765608969/Qgc4pMtC_400x400.jpg&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </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">R4 G4.mp3🌻</div> <div style="text-align: center; font-size: 14px;">@ragnar_furup</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. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) 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 R4 G4.mp3🌻. | Data | R4 G4.mp3🌻 | | --- | --- | | Tweets downloaded | 1695 | | Retweets | 889 | | Short tweets | 104 | | Tweets kept | 702 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3eum19q4/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 @ragnar_furup's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/30kqu5u4) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/30kqu5u4/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/ragnar_furup') 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* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
sunitha/CV_Merge_DS
a17c761d54f9a8c00f9732197cab9ff97a9f2113
2022-03-06T05:09:45.000Z
[ "pytorch", "roberta", "question-answering", "transformers", "autotrain_compatible" ]
question-answering
false
sunitha
null
sunitha/CV_Merge_DS
0
null
transformers
36,423
Entry not found
lilitket/wav2vec2-large-xls-r-300m-hy-colab
3a2b5dd220468147023c6a5ba666e2090e5e558d
2022-03-06T10:17:25.000Z
[ "pytorch", "wav2vec2", "automatic-speech-recognition", "transformers" ]
automatic-speech-recognition
false
lilitket
null
lilitket/wav2vec2-large-xls-r-300m-hy-colab
0
null
transformers
36,424
Entry not found
lilitket/wav2vec2-large-xls-r-300m-hypy-colab
6182d8179eb267e89868912ee616001e1af834d1
2022-03-09T18:55:56.000Z
[ "pytorch", "wav2vec2", "automatic-speech-recognition", "transformers" ]
automatic-speech-recognition
false
lilitket
null
lilitket/wav2vec2-large-xls-r-300m-hypy-colab
0
null
transformers
36,425
Entry not found
osanseviero/xlm-roberta-base-finetuned-panx-de-fr
5910b67637bec88e50820f01988dbd4109895377
2022-03-06T21:30:10.000Z
[ "pytorch", "xlm-roberta", "token-classification", "transformers", "generated_from_trainer", "license:mit", "model-index", "autotrain_compatible" ]
token-classification
false
osanseviero
null
osanseviero/xlm-roberta-base-finetuned-panx-de-fr
0
null
transformers
36,426
--- license: mit tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de-fr results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-finetuned-panx-de-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1754 - F1: 0.8616 ## 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: 12 - eval_batch_size: 12 - 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 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2815 | 1.0 | 1430 | 0.2079 | 0.8067 | | 0.1521 | 2.0 | 2860 | 0.1759 | 0.8525 | | 0.093 | 3.0 | 4290 | 0.1754 | 0.8616 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1 - Datasets 1.18.0 - Tokenizers 0.10.3
tau/fewsion_debug
2f56b0dc9e7a8f777e016c69870eacb124be50b3
2022-03-07T10:56:41.000Z
[ "pytorch", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
tau
null
tau/fewsion_debug
0
null
transformers
36,427
Entry not found
voidful/speechmix_eed_fixed
f87da2b979118fe8d3a984f8c3cd72ffceddec4a
2022-03-07T14:17:04.000Z
[ "pytorch" ]
null
false
voidful
null
voidful/speechmix_eed_fixed
0
null
null
36,428
Entry not found
vocab-transformers/msmarco-distilbert-custom_word2vec256k
36e2bd2647762004a73e95f38f9aef9e03bfe696
2022-03-07T14:56:18.000Z
[ "pytorch", "distilbert", "feature-extraction", "transformers" ]
feature-extraction
false
vocab-transformers
null
vocab-transformers/msmarco-distilbert-custom_word2vec256k
0
null
transformers
36,429
Entry not found
peggyhuang/finetune-bert-base-v3
f4d4cda6123bb12e088e0192fc5830ea4a001262
2022-03-07T18:23:42.000Z
[ "pytorch", "bert", "question-answering", "transformers", "autotrain_compatible" ]
question-answering
false
peggyhuang
null
peggyhuang/finetune-bert-base-v3
0
null
transformers
36,430
Entry not found
rockmiin/QMSum-dpr-query-encoder
a402f2c77483d5c7429729ea080c46c2293c2759
2022-03-08T02:00:39.000Z
[ "pytorch", "bert", "feature-extraction", "transformers" ]
feature-extraction
false
rockmiin
null
rockmiin/QMSum-dpr-query-encoder
0
null
transformers
36,431
Entry not found
rockmiin/QMSum-dpr-passage-encoder
e35ac25b89869d432695fca742ef6c156b963aa4
2022-03-08T02:09:39.000Z
[ "pytorch", "bert", "feature-extraction", "transformers" ]
feature-extraction
false
rockmiin
null
rockmiin/QMSum-dpr-passage-encoder
0
null
transformers
36,432
Entry not found
oskrmiguel/t5-small-finetuned-es-to-pt
a5fdfeb64e1e0fc900c6aba6b0215c3b99ee484a
2022-03-08T03:15:16.000Z
[ "pytorch", "tensorboard", "t5", "text2text-generation", "dataset:tatoeba", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]
text2text-generation
false
oskrmiguel
null
oskrmiguel/t5-small-finetuned-es-to-pt
0
null
transformers
36,433
--- license: apache-2.0 tags: - generated_from_trainer datasets: - tatoeba metrics: - bleu model-index: - name: t5-small-finetuned-es-to-pt results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: tatoeba type: tatoeba args: es-pt metrics: - name: Bleu type: bleu value: 15.0473 --- <!-- 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-es-to-pt This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the tatoeba dataset. It achieves the following results on the evaluation set: - Loss: 1.5557 - Bleu: 15.0473 - Gen Len: 15.8693 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:| | 2.2027 | 1.0 | 1907 | 1.7884 | 11.6192 | 15.8829 | | 1.9296 | 2.0 | 3814 | 1.6034 | 14.201 | 15.8935 | | 1.8364 | 3.0 | 5721 | 1.5557 | 15.0473 | 15.8693 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 1.18.4 - Tokenizers 0.11.6
huggingtweets/fitdollar
7e2d3f0f7735b472bcb1fc1dc8d60078fdfa8bac
2022-03-08T05:18:01.000Z
[ "pytorch", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]
text-generation
false
huggingtweets
null
huggingtweets/fitdollar
0
null
transformers
36,434
--- language: en thumbnail: http://www.huggingtweets.com/fitdollar/1646716677087/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(&#39;https://pbs.twimg.com/profile_images/1421952831796350976/rFuw5k2v_400x400.jpg&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </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">Fit$</div> <div style="text-align: center; font-size: 14px;">@fitdollar</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Fit$. | Data | Fit$ | | --- | --- | | Tweets downloaded | 1235 | | Retweets | 139 | | Short tweets | 219 | | Tweets kept | 877 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1nxpnpfh/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 @fitdollar's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3f78vjfv) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3f78vjfv/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/fitdollar') 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* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
jiobiala24/wav2vec2-base-cv-10000
ca850d61e9bd27a5d5042ab2b1bc431a266a2549
2022-03-08T13:08:35.000Z
[ "pytorch", "wav2vec2", "automatic-speech-recognition", "dataset:common_voice", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
automatic-speech-recognition
false
jiobiala24
null
jiobiala24/wav2vec2-base-cv-10000
0
null
transformers
36,435
--- license: apache-2.0 tags: - generated_from_trainer datasets: - common_voice model-index: - name: wav2vec2-base-cv-10000 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-cv-10000 This model is a fine-tuned version of [jiobiala24/wav2vec2-base-cv](https://huggingface.co/jiobiala24/wav2vec2-base-cv) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 1.3393 - Wer: 0.3684 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 0.4243 | 1.6 | 1000 | 0.7742 | 0.4210 | | 0.3636 | 3.2 | 2000 | 0.8621 | 0.4229 | | 0.2638 | 4.8 | 3000 | 0.9328 | 0.4094 | | 0.2273 | 6.4 | 4000 | 0.9556 | 0.4087 | | 0.187 | 8.0 | 5000 | 0.9093 | 0.4019 | | 0.1593 | 9.6 | 6000 | 0.9842 | 0.4029 | | 0.1362 | 11.2 | 7000 | 1.0651 | 0.4077 | | 0.1125 | 12.8 | 8000 | 1.0550 | 0.3959 | | 0.103 | 14.4 | 9000 | 1.1919 | 0.4002 | | 0.0948 | 16.0 | 10000 | 1.1901 | 0.3983 | | 0.0791 | 17.6 | 11000 | 1.1091 | 0.3860 | | 0.0703 | 19.2 | 12000 | 1.2823 | 0.3904 | | 0.0641 | 20.8 | 13000 | 1.2625 | 0.3817 | | 0.057 | 22.4 | 14000 | 1.2821 | 0.3776 | | 0.0546 | 24.0 | 15000 | 1.2975 | 0.3770 | | 0.0457 | 25.6 | 16000 | 1.2998 | 0.3714 | | 0.0433 | 27.2 | 17000 | 1.3574 | 0.3721 | | 0.0423 | 28.8 | 18000 | 1.3393 | 0.3684 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.13.3 - Tokenizers 0.10.3
kevinjesse/roberta-MT4TS
348c5b28ff4ffd206d59c22b1073a0b2d697830d
2022-03-09T20:20:41.000Z
[ "pytorch", "roberta", "token-classification", "transformers", "autotrain_compatible" ]
token-classification
false
kevinjesse
null
kevinjesse/roberta-MT4TS
0
null
transformers
36,436
Entry not found
kevinjesse/polygot-MT4TS
e89b517f46214d5b8869c2ac71591f63d18ee042
2022-03-09T19:31:30.000Z
[ "pytorch", "roberta", "token-classification", "transformers", "autotrain_compatible" ]
token-classification
false
kevinjesse
null
kevinjesse/polygot-MT4TS
0
null
transformers
36,437
Entry not found
kevinjesse/graphpolygot-MT4TS
9263bb0cc9133c14037baed784b2657af7288385
2022-03-09T18:44:52.000Z
[ "pytorch", "roberta", "token-classification", "transformers", "autotrain_compatible" ]
token-classification
false
kevinjesse
null
kevinjesse/graphpolygot-MT4TS
0
null
transformers
36,438
Entry not found
huggingtweets/betonkoepfin-littlehorney-plusbibi1
3900535a143cbe4e05ce6dfb014b374fddc64f90
2022-03-08T07:46:04.000Z
[ "pytorch", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]
text-generation
false
huggingtweets
null
huggingtweets/betonkoepfin-littlehorney-plusbibi1
0
null
transformers
36,439
--- language: en thumbnail: http://www.huggingtweets.com/betonkoepfin-littlehorney-plusbibi1/1646725560421/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(&#39;https://pbs.twimg.com/profile_images/1386970823681052680/oA_4HBKl_400x400.jpg&#39;)"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/1425205160578588673/LBMG1HOO_400x400.jpg&#39;)"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/1500892464772751365/6uhqt-Jx_400x400.jpg&#39;)"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Bibi und Anna & Betty S. & Vanny_Bunny™</div> <div style="text-align: center; font-size: 14px;">@betonkoepfin-littlehorney-plusbibi1</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. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) 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 Bibi und Anna & Betty S. & Vanny_Bunny™. | Data | Bibi und Anna | Betty S. | Vanny_Bunny™ | | --- | --- | --- | --- | | Tweets downloaded | 1818 | 3243 | 3185 | | Retweets | 9 | 213 | 494 | | Short tweets | 341 | 552 | 339 | | Tweets kept | 1468 | 2478 | 2352 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3nxb6yoh/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 @betonkoepfin-littlehorney-plusbibi1's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/365gy60z) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/365gy60z/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/betonkoepfin-littlehorney-plusbibi1') 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* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
kamilali/distilbert-base-uncased-finetuned-custom
eecdf367580c719ace3227bdd6ee80f8c7ec8446
2022-03-08T08:57:07.000Z
[ "pytorch", "tensorboard", "bert", "question-answering", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]
question-answering
false
kamilali
null
kamilali/distilbert-base-uncased-finetuned-custom
0
null
transformers
36,440
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: distilbert-base-uncased-finetuned-custom 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-custom This model is a fine-tuned version of [bert-large-uncased-whole-word-masking-finetuned-squad](https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7808 ## 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: 1 - eval_batch_size: 1 - 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 | 368 | 1.1128 | | 2.1622 | 2.0 | 736 | 0.8494 | | 1.2688 | 3.0 | 1104 | 0.7808 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 1.18.4 - Tokenizers 0.11.6
openclimatefix/graph-weather-forecaster-0.25deg
9343fc4999c12c6b335d77eb2ab41a652b22eb05
2022-03-09T16:19:40.000Z
[ "pytorch" ]
null
false
openclimatefix
null
openclimatefix/graph-weather-forecaster-0.25deg
0
null
null
36,441
Entry not found
openclimatefix/graph-weather-forecaster-0.5deg
e0c5813dfc61fe708b73927ad1a463a126fb75f1
2022-03-09T16:15:51.000Z
[ "pytorch" ]
null
false
openclimatefix
null
openclimatefix/graph-weather-forecaster-0.5deg
0
null
null
36,442
Entry not found
openclimatefix/graph-weather-forecaster-1.0deg
524b072a6e8fc6f712596778e3d732130f695fee
2022-07-04T06:24:35.000Z
[ "pytorch" ]
null
false
openclimatefix
null
openclimatefix/graph-weather-forecaster-1.0deg
0
null
null
36,443
Entry not found
gayanin/bart-med-term-mlm
8cebf37973de5866357347a909f7bfc125c8d12a
2022-03-08T15:46:48.000Z
[ "pytorch", "tensorboard", "bart", "text2text-generation", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]
text2text-generation
false
gayanin
null
gayanin/bart-med-term-mlm
0
null
transformers
36,444
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: bart-med-term-mlm 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. --> # bart-med-term-mlm This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2506 - Rouge2 Precision: 0.8338 - Rouge2 Recall: 0.6005 - Rouge2 Fmeasure: 0.6775 ## 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: 4 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure | |:-------------:|:-----:|:-----:|:---------------:|:----------------:|:-------------:|:---------------:| | 0.3426 | 1.0 | 15827 | 0.3029 | 0.8184 | 0.5913 | 0.6664 | | 0.2911 | 2.0 | 31654 | 0.2694 | 0.8278 | 0.5963 | 0.6727 | | 0.2571 | 3.0 | 47481 | 0.2549 | 0.8318 | 0.5985 | 0.6753 | | 0.2303 | 4.0 | 63308 | 0.2506 | 0.8338 | 0.6005 | 0.6775 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 1.18.4 - Tokenizers 0.11.6
huggingtweets/feufillet-greatestquotes-hostagekiller
64db1cdb4ca37b1625556d1f388b47ade20fec0b
2022-03-08T13:28:29.000Z
[ "pytorch", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]
text-generation
false
huggingtweets
null
huggingtweets/feufillet-greatestquotes-hostagekiller
0
null
transformers
36,445
--- language: en thumbnail: http://www.huggingtweets.com/feufillet-greatestquotes-hostagekiller/1646746104400/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(&#39;https://pbs.twimg.com/profile_images/1197820815636672513/JSCZmPDf_400x400.jpg&#39;)"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/1473236995497500675/FtwXDZld_400x400.jpg&#39;)"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/378800000520968918/d38fd96468e9ba14c1f9f022eb0c4e61_400x400.png&#39;)"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">sexy.funny.cute.pix & HUSSY2K. & Great Minds Quotes</div> <div style="text-align: center; font-size: 14px;">@feufillet-greatestquotes-hostagekiller</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. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) 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 sexy.funny.cute.pix & HUSSY2K. & Great Minds Quotes. | Data | sexy.funny.cute.pix | HUSSY2K. | Great Minds Quotes | | --- | --- | --- | --- | | Tweets downloaded | 3091 | 3191 | 3200 | | Retweets | 149 | 865 | 0 | | Short tweets | 576 | 374 | 2 | | Tweets kept | 2366 | 1952 | 3198 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3afdee2s/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 @feufillet-greatestquotes-hostagekiller's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/25fcmxer) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/25fcmxer/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/feufillet-greatestquotes-hostagekiller') 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* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
sh0416/clrcmd
43df4478803e2c2763a42b7cd0907200dfe5ba57
2022-03-08T14:28:09.000Z
[ "pytorch", "license:cc-by-nc-sa-4.0" ]
null
false
sh0416
null
sh0416/clrcmd
0
null
null
36,446
--- license: cc-by-nc-sa-4.0 ---
13hannes11/master_thesis_models
3ed3f87ac04b13c8c2659df55943ca1625e4631b
2022-06-28T21:14:01.000Z
[ "tensorboard", "focus-prediction", "microscopy", "pytorch", "license:mit" ]
null
false
13hannes11
null
13hannes11/master_thesis_models
0
null
null
36,447
--- name: "K-POP" license: "mit" metrics: - MAE - PLCC - SRCC - R2 tags: - focus-prediction - microscopy - pytorch --- # K-POP: Predicting Distance to Focal Plane for Kato-Katz Prepared Microscopy Slides Using Deep Learning <a href="https://pytorch.org/get-started/locally/"><img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-ee4c2c?logo=pytorch&logoColor=white"></a><a href="https://pytorchlightning.ai/"> <img alt="Lightning" src="https://img.shields.io/badge/-Lightning-792ee5?logo=pytorchlightning&logoColor=white"></a> <a href="https://hydra.cc/"><img alt="Config: Hydra" src="https://img.shields.io/badge/Config-Hydra-89b8cd"></a> ## Description This repository contains the models and training pipeline for my master thesis. The main repository is hosted on [GitHub](https://github.com/13hannes11/master_thesis_code). The project structure is based on the template by [ashleve](https://github.com/ashleve/lightning-hydra-template). The metadata is stored in `data/focus150/`. The relevant files are `test_metadata.csv`, `train_metadata.csv` and `validation_metadata.csv`. Image data (of 150 x 150 px images) is not published together with this repository therefore training runs are not possible to do without it. The layout of the metadata files is as follows ```csv ,image_path,scan_uuid,study_id,focus_height,original_filename,stack_id,obj_name 0,31/b0d4005e-57d0-4516-a239-abe02a8d0a67/I02413_X009_Y014_Z5107_750_300.jpg,b0d4005e-57d0-4516-a239-abe02a8d0a67,31,-0.013672000000000017,I02413_X009_Y014_Z5107.jpg,1811661,schistosoma 1,31/274d8969-aa7c-4ac0-be60-e753579393ad/I01981_X019_Y014_Z4931_450_0.jpg,274d8969-aa7c-4ac0-be60-e753579393ad,31,-0.029296999999999962,I01981_X019_Y014_Z4931.jpg,1661371,schistosoma ... ``` ## How to run Train model with chosen experiment configuration from `configs/experiment/` ```bash python train.py experiment=focusResNet_150 ``` Train with hyperparameter search from `configs/hparams_search/` ```bash python train.py -m hparams_search=focusResNetMSE_150 ``` You can override any parameter from command line like this ```bash python train.py trainer.max_epochs=20 datamodule.batch_size=64 ``` ## Jupyter notebooks Figures and other evaluation code was run in Jupyter notebooks. These are available at `notebooks/`
kevinjesse/codeberta-MT4TS
69bcf0d6d1aeb11ba321f24d6c454edd593a3008
2022-03-09T18:18:24.000Z
[ "pytorch", "roberta", "token-classification", "transformers", "autotrain_compatible" ]
token-classification
false
kevinjesse
null
kevinjesse/codeberta-MT4TS
0
null
transformers
36,448
Entry not found
kj141/distilbert-base-uncased-finetuned-squad
66bbd31d99ca681235b2a5ca3ec1fd2ad610946a
2022-03-23T19:48:03.000Z
[ "pytorch", "tensorboard", "distilbert", "question-answering", "dataset:squad", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]
question-answering
false
kj141
null
kj141/distilbert-base-uncased-finetuned-squad
0
null
transformers
36,449
--- 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 [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) 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 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6
huak95/mt-align-finetuned-LST-en-to-th
6bba8d437958f2f7421c4052b2941832d8fd0de2
2022-03-09T20:41:54.000Z
[ "pytorch", "marian", "text2text-generation", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]
text2text-generation
false
huak95
null
huak95/mt-align-finetuned-LST-en-to-th
0
null
transformers
36,450
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: mt-align-finetuned-LST-en-to-th 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. --> # mt-align-finetuned-LST-en-to-th This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-mul](https://huggingface.co/Helsinki-NLP/opus-mt-en-mul) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 128 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:| | No log | 1.0 | 77 | 1.6042 | 13.1732 | 26.144 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.2+cu113 - Datasets 1.18.4 - Tokenizers 0.11.6
huggingtweets/aniraster_
4710a24284b1df2462ba6b6abc86087af26ec27b
2022-03-09T09:03:20.000Z
[ "pytorch", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]
text-generation
false
huggingtweets
null
huggingtweets/aniraster_
0
null
transformers
36,451
--- language: en thumbnail: http://www.huggingtweets.com/aniraster_/1646816595677/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(&#39;https://pbs.twimg.com/profile_images/1460097593015472141/Yt6YwEU1_400x400.jpg&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </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">Aniraster</div> <div style="text-align: center; font-size: 14px;">@aniraster_</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. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) 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 Aniraster. | Data | Aniraster | | --- | --- | | Tweets downloaded | 2581 | | Retweets | 169 | | Short tweets | 660 | | Tweets kept | 1752 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3nr4gbjn/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 @aniraster_'s tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3g7h1bov) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3g7h1bov/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/aniraster_') 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* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
l53513955/PAQ_256
9d609fb6fae14b5488c9d9e56d8acd57a60718c5
2022-03-09T09:09:48.000Z
[ "pytorch", "albert", "feature-extraction", "transformers" ]
feature-extraction
false
l53513955
null
l53513955/PAQ_256
0
null
transformers
36,452
Entry not found
paopow/t5_base
bd0edc2c21f093fb5bfdda5b5b19bc107d894929
2022-03-09T14:47:49.000Z
[ "pytorch", "mt5", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
paopow
null
paopow/t5_base
0
null
transformers
36,453
Entry not found
petrichorRainbow/mrf-bert
1d811b93ee4a1346bcdd5ee564725891c038e8d6
2022-03-09T17:12:06.000Z
[ "pytorch", "bert", "fill-mask", "transformers", "license:apache-2.0", "autotrain_compatible" ]
fill-mask
false
petrichorRainbow
null
petrichorRainbow/mrf-bert
0
null
transformers
36,454
--- license: apache-2.0 ---
petrichorRainbow/mrf-covid-bert
75848a3e0b2660c38cd16ed5cba68d7ff338da4c
2022-03-09T17:24:51.000Z
[ "pytorch", "bert", "transformers", "license:apache-2.0" ]
null
false
petrichorRainbow
null
petrichorRainbow/mrf-covid-bert
0
null
transformers
36,455
--- license: apache-2.0 ---
pong/opus-mt-en-mul-finetuned-en-to-th
982b3a991c31c9c1ced377cd888db23a882a8889
2022-03-09T18:01:13.000Z
[ "pytorch", "tensorboard", "marian", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
pong
null
pong/opus-mt-en-mul-finetuned-en-to-th
0
null
transformers
36,456
Entry not found
huak95/mt-align-finetuned-SUM3-th-to-en
73315f4d73c141692f30ab40ce0fcc26ddd44896
2022-03-09T20:51:21.000Z
[ "pytorch", "marian", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
huak95
null
huak95/mt-align-finetuned-SUM3-th-to-en
0
null
transformers
36,457
Entry not found
tiot07/0310
b3bde3621555d53102a423ae2a788cf86870af05
2022-03-10T06:39:22.000Z
[ "pytorch", "wav2vec2", "automatic-speech-recognition", "transformers" ]
automatic-speech-recognition
false
tiot07
null
tiot07/0310
0
null
transformers
36,458
Entry not found
huak95/mt-align-LST_classic-th-to-en-pt2
9fc1605167b4ad23a52439c3061221a02c438617
2022-03-10T09:13:38.000Z
[ "pytorch", "marian", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
huak95
null
huak95/mt-align-LST_classic-th-to-en-pt2
0
null
transformers
36,459
Entry not found
huak95/LST_classic-th-to-en-pt2.1
df12a09d1ed3811d7a41fe4c955559dac6979507
2022-03-10T09:19:24.000Z
[ "pytorch", "marian", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
huak95
null
huak95/LST_classic-th-to-en-pt2.1
0
null
transformers
36,460
Entry not found
spasis/distilbert-base-uncased-finetuned-imdb-accelerate
8e82bdacadfe25ea0d87278fdecc3ccbe7445dce
2022-03-10T12:04:06.000Z
[ "pytorch", "distilbert", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
spasis
null
spasis/distilbert-base-uncased-finetuned-imdb-accelerate
0
null
transformers
36,461
Entry not found
timkakhanovich/finetuned-asr
73d64f6e2504c7b4eea8d8545cf9808e632d6dbc
2022-03-10T10:53:21.000Z
[ "pytorch", "wav2vec2", "automatic-speech-recognition", "transformers" ]
automatic-speech-recognition
false
timkakhanovich
null
timkakhanovich/finetuned-asr
0
null
transformers
36,462
Entry not found
huak95/TNANA-attacut-th-to-en
87859e56b8929f990770230f2a41da535388bbe3
2022-03-10T15:40:30.000Z
[ "pytorch", "marian", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
huak95
null
huak95/TNANA-attacut-th-to-en
0
null
transformers
36,463
Entry not found
huggingtweets/atarifounders
ea560d60fa2eebbbbdaa2be2c3656ba64890f9ea
2022-03-26T03:45:11.000Z
[ "pytorch", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]
text-generation
false
huggingtweets
null
huggingtweets/atarifounders
0
null
transformers
36,464
--- language: en thumbnail: http://www.huggingtweets.com/atarifounders/1648266306699/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(&#39;https://pbs.twimg.com/profile_images/1507523916981583875/6n7ng67H_400x400.jpg&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </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">koala/claw/soppy</div> <div style="text-align: center; font-size: 14px;">@atarifounders</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. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) 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 koala/claw/soppy. | Data | koala/claw/soppy | | --- | --- | | Tweets downloaded | 3239 | | Retweets | 129 | | Short tweets | 883 | | Tweets kept | 2227 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2gsc0jwi/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 @atarifounders's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/tl1eu60e) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/tl1eu60e/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/atarifounders') 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* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
lijingxin/xlm-roberta-base-finetuned-panx-fr
75fe94e417bc22e5dd77d3a3fbf8d5b5d9b34916
2022-03-11T02:19:48.000Z
[ "pytorch", "xlm-roberta", "token-classification", "dataset:xtreme", "transformers", "generated_from_trainer", "license:mit", "model-index", "autotrain_compatible" ]
token-classification
false
lijingxin
null
lijingxin/xlm-roberta-base-finetuned-panx-fr
0
null
transformers
36,465
--- 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: 0.838255033557047 --- <!-- 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: 0.2691 - F1: 0.8383 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.5851 | 1.0 | 191 | 0.3202 | 0.8011 | | 0.256 | 2.0 | 382 | 0.2862 | 0.8344 | | 0.1725 | 3.0 | 573 | 0.2691 | 0.8383 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.9.1 - Datasets 1.16.1 - Tokenizers 0.10.3
huak95/TNANA_V2-attacut-th-to-en-pt2
1d1c1359298e83bbbf90ccf0927a5b8e922983f9
2022-03-11T17:29:07.000Z
[ "pytorch", "marian", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
huak95
null
huak95/TNANA_V2-attacut-th-to-en-pt2
0
null
transformers
36,466
Entry not found
zuppif/maskformer-swin-small-coco
81ccd61f1115c48ca4db493c3ec00cb3501f8f50
2022-03-11T14:23:35.000Z
[ "pytorch", "maskformer", "transformers" ]
null
false
zuppif
null
zuppif/maskformer-swin-small-coco
0
null
transformers
36,467
Entry not found
zuppif/maskformer-swin-large-ade
038c928b990e04a7f3433324bb9ee783c9b33004
2022-03-11T14:28:26.000Z
[ "pytorch", "maskformer", "transformers" ]
null
false
zuppif
null
zuppif/maskformer-swin-large-ade
0
null
transformers
36,468
Entry not found
zuppif/maskformer-swin-tiny-ade
dc866fbdeafe659f6ed8879e75892f77e9a9e751
2022-03-11T15:01:00.000Z
[ "pytorch", "maskformer", "transformers" ]
null
false
zuppif
null
zuppif/maskformer-swin-tiny-ade
0
null
transformers
36,469
Entry not found
huggingtweets/thed3linquent_
948e6f9133e95f9cab3f4baeae17613a8ca63df8
2022-03-11T22:57:28.000Z
[ "pytorch", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]
text-generation
false
huggingtweets
null
huggingtweets/thed3linquent_
0
null
transformers
36,470
--- 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(&#39;https://pbs.twimg.com/profile_images/1502166273064517632/RdLwNuR6_400x400.jpg&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </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">rogue⛓🐕|| BIRFDAY BOY</div> <div style="text-align: center; font-size: 14px;">@thed3linquent_</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. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) 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 rogue⛓🐕|| BIRFDAY BOY. | Data | rogue⛓🐕|| BIRFDAY BOY | | --- | --- | | Tweets downloaded | 3246 | | Retweets | 334 | | Short tweets | 710 | | Tweets kept | 2202 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1tal3g38/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 @thed3linquent_'s tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1aw76tml) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1aw76tml/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/thed3linquent_') 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* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
lilitket/wav2vec2-large-xls-r-300m-hyAM_batch2
1f08ccc5853ef5080f49f51a765bbd2cd8ec962f
2022-03-12T14:52:57.000Z
[ "pytorch", "wav2vec2", "automatic-speech-recognition", "transformers" ]
automatic-speech-recognition
false
lilitket
null
lilitket/wav2vec2-large-xls-r-300m-hyAM_batch2
0
null
transformers
36,471
Entry not found
lilitket/wav2vec2-large-xls-r-300m-hyAM_batch4_lr2
690aba7a14a0c95db306468cbd784d2bcc11fe03
2022-03-12T16:03:02.000Z
[ "pytorch", "wav2vec2", "automatic-speech-recognition", "transformers" ]
automatic-speech-recognition
false
lilitket
null
lilitket/wav2vec2-large-xls-r-300m-hyAM_batch4_lr2
0
null
transformers
36,472
Entry not found
lilitket/wav2vec2-large-xls-r-300m-hyAM_batch4_lr8
16beeb0aefdd2bcc3e9e5cb780a1e27c49e01634
2022-03-12T20:58:57.000Z
[ "pytorch", "wav2vec2", "automatic-speech-recognition", "transformers" ]
automatic-speech-recognition
false
lilitket
null
lilitket/wav2vec2-large-xls-r-300m-hyAM_batch4_lr8
0
null
transformers
36,473
Entry not found
lilitket/300m-hyAM_batch4_lr8_warmup4000
7c7525017d51f3e7476633a17ae1d06c440fc931
2022-03-17T18:50:33.000Z
[ "pytorch", "wav2vec2", "automatic-speech-recognition", "transformers" ]
automatic-speech-recognition
false
lilitket
null
lilitket/300m-hyAM_batch4_lr8_warmup4000
0
null
transformers
36,474
Entry not found
zdepablo/xlm-roberta-base-finetuned-panx-de
eb5298cbd737fbcf33cf9f7678affd139691e912
2022-03-12T18:25:42.000Z
[ "pytorch", "tensorboard", "xlm-roberta", "token-classification", "dataset:xtreme", "transformers", "generated_from_trainer", "license:mit", "model-index", "autotrain_compatible" ]
token-classification
false
zdepablo
null
zdepablo/xlm-roberta-base-finetuned-panx-de
0
null
transformers
36,475
--- 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: 0.8594910162670748 --- <!-- 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: 0.1348 - F1: 0.8595 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2556 | 1.0 | 525 | 0.1629 | 0.8218 | | 0.1309 | 2.0 | 1050 | 0.1378 | 0.8522 | | 0.0812 | 3.0 | 1575 | 0.1348 | 0.8595 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.9.1 - Datasets 1.16.1 - Tokenizers 0.10.3
zdepablo/xlm-roberta-base-finetuned-panx-de-fr
fbeb4772ce785f68908426f3b13ddd7df6b59191
2022-03-12T18:54:00.000Z
[ "pytorch", "xlm-roberta", "token-classification", "transformers", "generated_from_trainer", "license:mit", "model-index", "autotrain_compatible" ]
token-classification
false
zdepablo
null
zdepablo/xlm-roberta-base-finetuned-panx-de-fr
0
null
transformers
36,476
--- license: mit tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de-fr results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-finetuned-panx-de-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1664 - F1: 0.8556 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2846 | 1.0 | 715 | 0.1837 | 0.8247 | | 0.1446 | 2.0 | 1430 | 0.1617 | 0.8409 | | 0.0923 | 3.0 | 2145 | 0.1664 | 0.8556 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.9.1 - Datasets 1.16.1 - Tokenizers 0.10.3
lilitket/xls-r-300m-hyAM_batch1_lr2e-05_warmup400
db529f41916cf30ce2ceff9f1c9a6e1be7ccba74
2022-03-13T07:14:22.000Z
[ "pytorch", "wav2vec2", "automatic-speech-recognition", "transformers" ]
automatic-speech-recognition
false
lilitket
null
lilitket/xls-r-300m-hyAM_batch1_lr2e-05_warmup400
0
null
transformers
36,477
Entry not found
lilitket/xls-r-300m-hyAM_batch1_lr1e-05_warmup400
e685207d23c9448938072f973c5b467e896d9f39
2022-03-13T07:41:07.000Z
[ "pytorch", "wav2vec2", "automatic-speech-recognition", "transformers" ]
automatic-speech-recognition
false
lilitket
null
lilitket/xls-r-300m-hyAM_batch1_lr1e-05_warmup400
0
null
transformers
36,478
Entry not found
holtin/distilbert-base-uncased-finetuned-squad
f0919e96377969142d6c032af9fa355ebb1496bd
2022-04-07T06:18:52.000Z
[ "pytorch", "distilbert", "question-answering", "transformers", "autotrain_compatible" ]
question-answering
false
holtin
null
holtin/distilbert-base-uncased-finetuned-squad
0
null
transformers
36,479
Entry not found
lilitket/xls-r-300m-hyAM_batch1_lr6e-06_warmup400
50c1e94bfd9a4222e7d26ebe4ab59a80f6194f8a
2022-03-20T20:17:52.000Z
[ "pytorch", "wav2vec2", "automatic-speech-recognition", "transformers" ]
automatic-speech-recognition
false
lilitket
null
lilitket/xls-r-300m-hyAM_batch1_lr6e-06_warmup400
0
null
transformers
36,480
Entry not found
sanchit-gandhi/wav2vec2-2-roberta-no-adapter-long-run
d126f4a7fdf2bde7ba506959857bf654f02eb442
2022-03-14T11:01:26.000Z
[ "pytorch", "tensorboard", "speech-encoder-decoder", "automatic-speech-recognition", "transformers" ]
automatic-speech-recognition
false
sanchit-gandhi
null
sanchit-gandhi/wav2vec2-2-roberta-no-adapter-long-run
0
null
transformers
36,481
Entry not found
huggingtweets/mikepompeo
39ec8a5587a6779f92817b10fd3ef6b9ef84d119
2022-03-13T14:28:20.000Z
[ "pytorch", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]
text-generation
false
huggingtweets
null
huggingtweets/mikepompeo
0
null
transformers
36,482
--- language: en thumbnail: http://www.huggingtweets.com/mikepompeo/1647181695747/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(&#39;https://pbs.twimg.com/profile_images/1498704685875744769/r3jThh-E_400x400.jpg&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </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">Mike Pompeo</div> <div style="text-align: center; font-size: 14px;">@mikepompeo</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. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) 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 Mike Pompeo. | Data | Mike Pompeo | | --- | --- | | Tweets downloaded | 1899 | | Retweets | 68 | | Short tweets | 60 | | Tweets kept | 1771 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2ll5re58/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 @mikepompeo's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/zi1wgzl5) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/zi1wgzl5/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/mikepompeo') 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* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
newtonkwan/gpt2-ft-with-non-challenging
6c1222d90d860aaeb135cce6b000dddd23348efa
2022-03-13T21:31:34.000Z
[ "pytorch", "gpt2", "text-generation", "transformers", "generated_from_trainer", "license:mit", "model-index" ]
text-generation
false
newtonkwan
null
newtonkwan/gpt2-ft-with-non-challenging
0
null
transformers
36,483
--- license: mit tags: - generated_from_trainer model-index: - name: gpt2-ft-with-non-challenging 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-ft-with-non-challenging This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.9906 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 32 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100.0 - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 1 | 4.0984 | | No log | 2.0 | 2 | 4.0802 | | No log | 3.0 | 3 | 4.0443 | | No log | 4.0 | 4 | 3.9906 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.11.0 - Datasets 1.18.4 - Tokenizers 0.11.6
lilitket/20220313-221906
4e8edea25bf164e0a8ed1f0b5ec22ee51d88be19
2022-03-14T04:27:43.000Z
[ "pytorch", "wav2vec2", "automatic-speech-recognition", "transformers" ]
automatic-speech-recognition
false
lilitket
null
lilitket/20220313-221906
0
null
transformers
36,484
Entry not found
huggingtweets/ayurastro
b91d7fa463d6aacdf3de36d014a4fd562a6b630e
2022-03-13T23:27:16.000Z
[ "pytorch", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]
text-generation
false
huggingtweets
null
huggingtweets/ayurastro
0
null
transformers
36,485
--- language: en thumbnail: http://www.huggingtweets.com/ayurastro/1647214031676/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(&#39;https://pbs.twimg.com/profile_images/493786234221641730/OFQm2K8M_400x400.jpeg&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </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">AyurAstro®</div> <div style="text-align: center; font-size: 14px;">@ayurastro</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. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) 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 AyurAstro®. | Data | AyurAstro® | | --- | --- | | Tweets downloaded | 1437 | | Retweets | 112 | | Short tweets | 65 | | Tweets kept | 1260 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/36zw53cv/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 @ayurastro's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/nhbmyyli) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/nhbmyyli/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/ayurastro') 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* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
tau/fewsion_1024_0.3_2100
c82a58ef2aeb9b3372631dd1040feaae35f9bb05
2022-03-14T08:36:20.000Z
[ "pytorch", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
tau
null
tau/fewsion_1024_0.3_2100
0
null
transformers
36,486
Entry not found
tau/t5_1024_0.3_2400
4b3fb9e72af44a3c1f99415ec4949ddf28707576
2022-03-14T08:46:55.000Z
[ "pytorch", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
tau
null
tau/t5_1024_0.3_2400
0
null
transformers
36,487
Entry not found
lilitket/20220314-084929
76c5be10e2c9b620885461e93f6de52ea1c15da8
2022-03-14T13:26:14.000Z
[ "pytorch", "wav2vec2", "automatic-speech-recognition", "transformers" ]
automatic-speech-recognition
false
lilitket
null
lilitket/20220314-084929
0
null
transformers
36,488
Entry not found
sanchit-gandhi/wav2vec2-2-bert-large-no-adapter
b11802c5a1eadd0abd0c3b9e3027a7caa819c225
2022-03-15T17:22:33.000Z
[ "pytorch", "tensorboard", "speech-encoder-decoder", "automatic-speech-recognition", "transformers" ]
automatic-speech-recognition
false
sanchit-gandhi
null
sanchit-gandhi/wav2vec2-2-bert-large-no-adapter
0
null
transformers
36,489
Entry not found
peterhsu/codeparrot-ds
ea65cf18f515ffe2eda0a72ea58ed0d7f9f526ad
2022-03-14T23:00:48.000Z
[ "pytorch", "tensorboard", "gpt2", "text-generation", "transformers", "generated_from_trainer", "license:mit", "model-index" ]
text-generation
false
peterhsu
null
peterhsu/codeparrot-ds
0
null
transformers
36,490
--- license: mit tags: - generated_from_trainer model-index: - name: codeparrot-ds 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. --> # codeparrot-ds This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.9729 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.4939 | 0.93 | 5000 | 1.9729 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 1.18.4 - Tokenizers 0.11.6
newtonkwan/gpt2-xl-ft-with-non-challenging-25k
3d10551c6ecab21243f47a46f2e41545e616a560
2022-03-15T00:06:36.000Z
[ "pytorch", "gpt2", "text-generation", "transformers" ]
text-generation
false
newtonkwan
null
newtonkwan/gpt2-xl-ft-with-non-challenging-25k
0
null
transformers
36,491
Entry not found
tau/t5_1024_0.3_7950
619e06eb26ab187968ed87b3dfde7d024465ea8f
2022-03-15T07:29:37.000Z
[ "pytorch", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
tau
null
tau/t5_1024_0.3_7950
0
null
transformers
36,492
Entry not found
Norod78/ml-generated-muppets-rudalle
43559f240be193f32836a24406d6e6736a42cad0
2022-03-15T10:02:58.000Z
[ "pytorch", "license:mit" ]
null
false
Norod78
null
Norod78/ml-generated-muppets-rudalle
0
null
null
36,493
--- license: mit --- Muppet image generator, based on ruDALL-E. You can perform inference using this [Colab notebook](https://github.com/Norod/my-colab-experiments/blob/master/ruDALLE_muppets_norod78.ipynb) ![Лягушонок](frog.jpg)
zuppif/resnetd-18
0d36c4fbc31431b03072141da0e4ba0a55a7af0f
2022-03-17T09:08:23.000Z
[ "pytorch", "resnetd", "transformers" ]
null
false
zuppif
null
zuppif/resnetd-18
0
null
transformers
36,494
Entry not found
zuppif/resnetd-101
232531b093321fe8f34fd4a28d5c7fc9564a8907
2022-03-17T09:13:10.000Z
[ "pytorch", "resnetd", "transformers" ]
null
false
zuppif
null
zuppif/resnetd-101
0
null
transformers
36,495
Entry not found
zuppif/resnetd-200
41253945cdbde0dce274d7413e99e97f64c4d424
2022-03-17T09:18:51.000Z
[ "pytorch", "resnetd", "transformers" ]
null
false
zuppif
null
zuppif/resnetd-200
0
null
transformers
36,496
Entry not found
spasis/marian-finetuned-kde4-en-to-fr
40cbbd3582645298cb26de24efd54ae12e7ae605
2022-03-15T17:39:40.000Z
[ "pytorch", "marian", "text2text-generation", "dataset:kde4", "transformers", "tanslation", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]
text2text-generation
false
spasis
null
spasis/marian-finetuned-kde4-en-to-fr
0
null
transformers
36,497
--- license: apache-2.0 tags: - tanslation - generated_from_trainer datasets: - kde4 model-index: - name: marian-finetuned-kde4-en-to-fr results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # marian-finetuned-kde4-en-to-fr This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) on the kde4 dataset. ## 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: 128 - eval_batch_size: 256 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1 - Datasets 1.17.0 - Tokenizers 0.10.3
moralstories/gpt2_action_context-consequence
284a29966aaa68ab47729808b3b22cbac493f06f
2022-03-15T18:13:31.000Z
[ "pytorch", "gpt2", "text-generation", "transformers", "license:afl-3.0" ]
text-generation
false
moralstories
null
moralstories/gpt2_action_context-consequence
0
null
transformers
36,498
--- license: afl-3.0 ---
facebook/regnet-x-016
5f7992cd8a33f3be2417b0a7b91f349ca6ad2932
2022-06-30T10:14:50.000Z
[ "pytorch", "tf", "regnet", "image-classification", "dataset:imagenet-1k", "arxiv:2003.13678", "transformers", "vision", "license:apache-2.0" ]
image-classification
false
facebook
null
facebook/regnet-x-016
0
null
transformers
36,499
--- license: apache-2.0 tags: - vision - image-classification datasets: - imagenet-1k 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 --- # RegNet RegNet model trained on imagenet-1k. It was introduced in the paper [Designing Network Design Spaces](https://arxiv.org/abs/2003.13678) and first released in [this repository](https://github.com/facebookresearch/pycls). Disclaimer: The team releasing RegNet did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description The authors design search spaces to perform Neural Architecture Search (NAS). They first start from a high dimensional search space and iteratively reduce the search space by empirically applying constraints based on the best-performing models sampled by the current search space. ![model image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/regnet_architecture.png) ## Intended uses & limitations You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=regnet) to look for fine-tuned versions on a task that interests you. ### How to use Here is how to use this model: ```python >>> from transformers import AutoFeatureExtractor, RegNetForImageClassification >>> import torch >>> from datasets import load_dataset >>> dataset = load_dataset("huggingface/cats-image") >>> image = dataset["test"]["image"][0] >>> feature_extractor = AutoFeatureExtractor.from_pretrained("zuppif/regnet-y-040") >>> model = RegNetForImageClassification.from_pretrained("zuppif/regnet-y-040") >>> inputs = feature_extractor(image, return_tensors="pt") >>> with torch.no_grad(): ... logits = model(**inputs).logits >>> # model predicts one of the 1000 ImageNet classes >>> predicted_label = logits.argmax(-1).item() >>> print(model.config.id2label[predicted_label]) 'tabby, tabby cat' ``` For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/regnet).