license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
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mit | ['generated_from_keras_callback'] | false | ishaankul67/Human_Development_Index-clustered This model is a fine-tuned version of [nandysoham16/4-clustered_aug](https://huggingface.co/nandysoham16/4-clustered_aug) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1775 - Train End Logits Accuracy: 0.9722 - Train Start... | d24e372dfdd1da5f2f17d0af4901f7aa |
mit | ['generated_from_keras_callback'] | false | Training results | Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:----------... | 0dc29367505a45006d146de22602534c |
mit | ['generated_from_trainer'] | false | Vigec-V4 This model is a fine-tuned version of [VietAI/vit5-base](https://huggingface.co/VietAI/vit5-base) on the None dataset. It achieves the following results on the evaluation set: - eval_loss: 2.0959 - eval_bleu: 0.0 - eval_gen_len: 17.9268 - eval_runtime: 319.3265 - eval_samples_per_second: 15.658 - eval_steps_... | 4b47bb590682b90f0d40b062a6c77612 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 2000 - mixed_precisi... | 95dd5ac3db42337cd674cfe668ef9689 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-clinc This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset. It achieves the following results on the evaluation set: - Loss: 0.2454 - Accuracy: 0.9474 | c93d90d6bf93f692a8dc1f9c823666ee |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 3.496 | 1.0 | 954 | 1.8019 | 0.8306 | | 1.0663 | 2.0 | 1908 | 0.5690 | 0.9174 | | 0.3267 | 3.0 | 2862 | 0.3128 | 0.... | 1f443e21572e19506a63e7f0a5db8c46 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'diffusers'] | false | This is a Stable Diffusion model trained on Yoji Shinkawa's artworks from Metal Gear Solid and Death Stranding game series. This was made with dreambooth extension for Automatic 111 using 47 images and 4000 steps. The token is: yoshin style Exemple images:  for speech recognition using the train split of [Common Voice 7.0 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure th... | e9816626c7d31d2c380e89e82d4b8c40 |
mit | ['masked-lm', 'catalan', 'exbert'] | false | Introduction CALBERT is an open-source language model for Catalan pretrained on the ALBERT architecture. It is now available on Hugging Face in its `tiny-uncased` version and `base-uncased` (the one you're looking at) as well, and was pretrained on the [OSCAR dataset](https://traces1.inria.fr/oscar/). For further i... | 390c3af34cf7799df21024e6fa18bd58 |
mit | ['masked-lm', 'catalan', 'exbert'] | false | Pre-trained models | Model | Arch. | Training data | | ----------------------------------- | -------------- | ---------------------- | | `codegram` / `calbert-tiny-uncased` | Tiny (uncased) | OSCAR (4.3 GB of text) | | `codegram` / `calbert-base-uncased` | Base (uncased... | 6a250deed9dfe90c036ce9ce85f5e2b1 |
mit | ['masked-lm', 'catalan', 'exbert'] | false | Load Calbert and its tokenizer: ```python from transformers import AutoModel, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("codegram/calbert-base-uncased") model = AutoModel.from_pretrained("codegram/calbert-base-uncased") model.eval() | 72aaf73905a1a346a37195ec06103bc5 |
mit | ['masked-lm', 'catalan', 'exbert'] | false | Filling masks using pipeline ```python from transformers import pipeline calbert_fill_mask = pipeline("fill-mask", model="codegram/calbert-base-uncased", tokenizer="codegram/calbert-base-uncased") results = calbert_fill_mask("M'agrada [MASK] això") | 3b170b100f4b7eca44461a8c3b179bb1 |
mit | ['masked-lm', 'catalan', 'exbert'] | false | Authors CALBERT was trained and evaluated by [Txus Bach](https://twitter.com/txustice), as part of [Codegram](https://www.codegram.com)'s applied research. <a href="https://huggingface.co/exbert/?model=codegram/calbert-base-uncased&modelKind=bidirectional&sentence=M%27agradaria%20força%20saber-ne%20més"> <img width... | c308f1a7986018c73d548d8b5f4a47b4 |
mit | ['generated_from_trainer'] | false | roberta-base-finetuned-scrambled-squad-15-new This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.0283 | 839bfb41a39da255427b0aff5738f519 |
cc-by-4.0 | [] | false | AssameseBERT AssameseBERT is an Assamese BERT model trained on publicly available Assamese monolingual datasets. Preliminary details on the dataset, models, and baseline results can be found in our [<a href='https://arxiv.org/abs/2211.11418'> paper </a>] . Citing: ``` @article{joshi2022l3cubehind, title={L3Cube-H... | b0efd4566e718f54d2126e768c9975ac |
apache-2.0 | ['generated_from_trainer'] | false | opus-mt-de-en-finetuned-de-to-en-first This model is a fine-tuned version of [Helsinki-NLP/opus-mt-de-en](https://huggingface.co/Helsinki-NLP/opus-mt-de-en) on the wmt16 dataset. It achieves the following results on the evaluation set: - Loss: 1.1465 - Bleu: 39.8122 - Gen Len: 25.579 | ebdab188abac98a5e4665d9bee6599f4 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:| | No log | 1.0 | 63 | 1.1465 | 39.8122 | 25.579 | | 070846f814e879767f927963c53c440a |
apache-2.0 | ['generated_from_trainer'] | false | recipe-lr1e05-wd0.02-bs32 This model is a fine-tuned version of [paola-md/recipe-distilroberta-Is](https://huggingface.co/paola-md/recipe-distilroberta-Is) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2756 - Rmse: 0.5250 - Mse: 0.2756 - Mae: 0.4181 | a4ac5720c90f03e3b1d448c3125f6104 |
mit | ['text-classification', 'tensorflow', 'roberta'] | false | What is GoEmotions Dataset labelled 58000 Reddit comments with 28 emotions - admiration, amusement, anger, annoyance, approval, caring, confusion, curiosity, desire, disappointment, disapproval, disgust, embarrassment, excitement, fear, gratitude, grief, joy, love, nervousness, optimism, pride, realization, relief, ... | 8b98d2f0969e767cfb4fc7283f7e3005 |
mit | ['text-classification', 'tensorflow', 'roberta'] | false | What is RoBERTa RoBERTa builds on BERT’s language masking strategy and modifies key hyperparameters in BERT, including removing BERT’s next-sentence pretraining objective, and training with much larger mini-batches and learning rates. RoBERTa was also trained on an order of magnitude more data than BERT, for a longer... | 77c0ca12cc5377053c10e0b2b4b99900 |
mit | ['text-classification', 'tensorflow', 'roberta'] | false | Hyperparameters | Parameter | | | ----------------- | :---: | | Learning rate | 5e-5 | | Epochs | 10 | | Max Seq Length | 50 | | Batch size | 16 | | Warmup Proportion | 0.1 | | Epsilon | 1e-8 | | 4edf6a2b421eeb894995792618853227 |
mit | ['text-classification', 'tensorflow', 'roberta'] | false | Usage ```python from transformers import RobertaTokenizerFast, TFRobertaForSequenceClassification, pipeline tokenizer = RobertaTokenizerFast.from_pretrained("arpanghoshal/EmoRoBERTa") model = TFRobertaForSequenceClassification.from_pretrained("arpanghoshal/EmoRoBERTa") emotion = pipeline('sentiment-analysis', ... | 817893fee9b28ec772563fb97d6a39d7 |
apache-2.0 | ['chinese', 'classical chinese', 'literary chinese', 'ancient chinese', 'bert', 'pytorch', 'punctuation marker'] | false | Guwen Punc A Classical Chinese Punctuation Marker. See also: <a href="https://github.com/ethan-yt/guwen-models"> <img align="center" width="400" src="https://github-readme-stats.vercel.app/api/pin/?username=ethan-yt&repo=guwen-models&bg_color=30,e96443,904e95&title_color=fff&text_color=fff&icon_color=fff&show_ow... | 80fa219adbb4eaf407469f2e60d88c6d |
mit | [] | false | Iridescent Photo Style on Stable Diffusion This is the 'iridescent-photo-style' concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) ... | ebd2f50051b8162ad1f38e5c3d375870 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | dbluth I played a lot in my childhood at laser disc videogames so this model is my personal tribute to the great Disney animator Don Bluth.This is a fine-tuned Stable Diffusion model (based on v1.5), I've trained three different models from videogames laser disc **Dragon's Lair** , **Space Ace** and **Dragon's Lair II... | 89ee385f173a8c62c9f9df465bd8d8f8 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | Mike Dreambooth model trained by vtwoods with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusio... | 948adb3f270dfebbc9b26d821d30cb63 |
apache-2.0 | ['automatic-speech-recognition', 'pl'] | false | exp_w2v2t_pl_vp-it_s157 Fine-tuned [facebook/wav2vec2-large-it-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-it-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (pl)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that you... | d9896e1b81c3e10f8523678ecce1210b |
apache-2.0 | ['generated_from_trainer'] | false | tiny-mlm-glue-stsb-target-glue-wnli This model is a fine-tuned version of [muhtasham/tiny-mlm-glue-stsb](https://huggingface.co/muhtasham/tiny-mlm-glue-stsb) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.2775 - Accuracy: 0.1408 | 946915abd3a320c772ae37d8f12a4d56 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6893 | 25.0 | 500 | 0.7717 | 0.2254 | | 0.6584 | 50.0 | 1000 | 1.1922 | 0.1408 | | 0.5966 | 75.0 | 1500 | 1.7754 | 0.... | a6f3a68ac75169018c9078404f49c541 |
apache-2.0 | ['generated_from_trainer'] | false | Article_100v8_NER_Model_3Epochs_UNAUGMENTED This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the article100v8_wikigold_split dataset. It achieves the following results on the evaluation set: - Loss: 0.6455 - Precision: 0.0 - Recall: 0.0 - F1: 0.0 - Accuracy: 0.7750 | 3e24240a891e9960b7acb08c32112114 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:---:|:--------:| | No log | 1.0 | 12 | 0.7691 | 0.0 | 0.0 | 0.0 | 0.7750 | | No log | 2.0 | 24 | 0... | 48831459ade6d238f5f95b2ef14be634 |
gpl-3.0 | ['pytorch', 'token-classification', 'bert', 'zh'] | false | Usage * Using our model in your script ```python from transformers import ( AutoTokenizer, AutoModel, ) tokenizer = AutoTokenizer.from_pretrained("ckiplab/bert-base-han-chinese-ws") model = AutoModel.from_pretrained("ckiplab/bert-base-han-chinese-ws") ``` * Using our model for in... | f261184ece413333ac8d602f3f26a8c9 |
gpl-3.0 | ['pytorch', 'token-classification', 'bert', 'zh'] | false | output [{'entity': 'B', 'score': 0.9999793, 'index': 1, 'word': '帝', 'start': 0, 'end': 1}, {'entity': 'I', 'score': 0.9915047, 'index': 2, 'word': '堯', 'start': 1, 'end': 2}, {'entity': 'B', 'score': 0.99992275, 'index': 3, 'word': '曰', 'start': 2, ... | 4791c4ba6ff9d4b952a4768efafde370 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | SMNBSLY Dreambooth model trained by Taboodada with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-dif... | 6831880ee06c74ac2447b0a028464a75 |
apache-2.0 | ['generated_from_keras_callback'] | false | Okyx/finetuned-imdb This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 2.8587 - Validation Loss: 2.6062 - Epoch: 0 | c1b97f9aeed2d3df4215504a1ac2e255 |
apache-2.0 | ['generated_from_trainer'] | false | distilroberta-base-etc This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3382 - Accuracy: 0.919 - F1: 0.9190 | 6128931576a07e43c8af3bcbc2d33144 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4.969790133269121e-05 - train_batch_size: 400 - eval_batch_size: 400 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 7 | 7b4d52bcd0e604cf9e72adf9c61529f0 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 84 | 0.2372 | 0.907 | 0.9070 | | No log | 2.0 | 168 | 0.2358 | 0.9083 | 0.9083 | | No log |... | e878f814ac74f9413c831c935668aabb |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Small hy This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the common_voice_11_0 dataset. It achieves the following results on the evaluation set: - Loss: 0.5960 - Wer: 125.5263 | 3250cf03de145a1e90b5d55adfd8a6fe |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - 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_sch... | 9516b6e59c1d0ed149d63026747abffb |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.9554 | 0.17 | 10 | 1.0085 | 174.1118 | | 0.7053 | 0.33 | 20 | 0.7362 | 197.4013 | | 0.681 | 0.5 | 30 | 0.6581 | 13... | 18cb4614b667fc216b24b3f5abdef9d7 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-large-xlsr-53_toy_train_data_augment_0.1 This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4658 - Wer: 0.5037 | d06cba179b7b51efeb570629496102f6 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.447 | 1.05 | 250 | 3.3799 | 1.0 | | 3.089 | 2.1 | 500 | 3.4868 | 1.0 | | 3.063 | 3.15 | 750 | 3.3155 | 1.0 | |... | 59622ff6cf3178f807812a0f9e449545 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:| | No log | 1.0 | 188 | 2.4324 | 1.2308 | 17.8904 | | d3e2292dbfb7acfd7d004bf022dfcb29 |
apache-2.0 | ['generated_from_trainer'] | false | mt5-small-tuto-mt5-small-1 This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.0740 - Rouge1: 0.3812 - Rouge2: 0.2565 - Rougel: 0.3583 - Rougelsum: 0.3582 | 169068f0a18daca4a6d466b2b080acf8 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:| | 2.5909 | 1.0 | 6034 | 2.0740 | 0.3812 | 0.2565 | 0.3583 | 0.3582 | | d50a1eb4b2439522027dbdc6cc0e6140 |
apache-2.0 | ['generated_from_trainer'] | false | t5-small-ret-conceptnet2 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1709 - Acc: {'accuracy': 0.8700980392156863} - Precision: {'precision': 0.811340206185567} - Recall: {'recall': 0.964460784... | 97570dd9ffbe4ea0f8dff0b3a7103a1d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Acc | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------------------------------:|:--------------------------------... | e7c511eab9e7f573fdf6ddc8fff513cf |
other | ['vision', 'image-segmentation'] | false | Mask2Former Mask2Former model trained on ADE20k semantic segmentation (large-sized version, Swin backbone). It was introduced in the paper [Masked-attention Mask Transformer for Universal Image Segmentation ](https://arxiv.org/abs/2112.01527) and first released in [this repository](https://github.com/facebookresearch... | 39a55fd9e4dc88d5916b60c7b6866b61 |
other | ['vision', 'image-segmentation'] | false | load Mask2Former fine-tuned on ADE20k semantic segmentation processor = AutoImageProcessor.from_pretrained("facebook/mask2former-swin-large-ade-semantic") model = Mask2FormerForUniversalSegmentation.from_pretrained("facebook/mask2former-swin-large-ade-semantic") url = "http://images.cocodataset.org/val2017/0000000397... | 7c8b4a8ffebcdd35c38b7a072a3d7e0e |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-uncased-issues-128 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.2312 | ab4bf96a187e2deb5db68b118f897d91 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.3399 | 1.0 | 73 | 1.7462 | | 1.799 | 2.0 | 146 | 1.4703 | | 1.6353 | 3.0 | 219 | 1.4796 | | 1.5464 | 4.0 | 292 | 1.3851 ... | 31328228e22e71b716f10dc18b5a991b |
apache-2.0 | ['image-classification', 'generated_from_trainer'] | false | vit-base-xray-pneumonia This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the [chest-xray-pneumonia](https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia) dataset. It achieves the following results on the evaluation set: - L... | 0276a5ac555669afacf2050b11d478a9 |
apache-2.0 | ['image-classification', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 | ff062d0806008b25bba4dc96a8874596 |
apache-2.0 | ['image-classification', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.1233 | 0.31 | 100 | 1.1662 | 0.6651 | | 0.0868 | 0.61 | 200 | 0.3387 | 0.9006 | | 0.1387 | 0.92 | 300 | 0.5297 | 0.... | 807fb5d1427c606403655f0bda74efa6 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-amazon-shoe-reviews This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on a [subset](https://huggingface.co/datasets/juliensimon/amazon-shoe-reviews) of the [Amazon US reviews](https://huggingface.co/datasets/amazon_us_reviews) dataset. It achie... | 77b280206a008503b0eb980c7fa80ba7 |
mit | ['text-generation', 'gpt2', 'gpt'] | false | ai-msgbot GPT2-XL-dialogue _NOTE: model card is WIP_ GPT2-XL (~1.5 B parameters) trained on [the Wizard of Wikipedia dataset](https://parl.ai/projects/wizard_of_wikipedia/) for 40k steps with **33**/36 layers frozen using `aitextgen`. The resulting model was then **further fine-tuned** on the [Daily Dialogues](http:... | e39dbd449000f049e16d3b087007b4e1 |
mit | ['text-generation', 'gpt2', 'gpt'] | false | conversation data The dataset was tokenized and fed to the model as a conversation between two speakers, whose names are below. This is relevant for writing prompts and filtering/extracting text from responses. `script_speaker_name` = `person alpha` `script_responder_name` = `person beta` | 56a9c1b110e6af6e46ad95bc5f8c510a |
mit | ['text-generation', 'gpt2', 'gpt'] | false | examples - the default inference API examples should work _okay_ - an ideal test would be explicitly adding `person beta` into the prompt text the model is forced to respond to instead of adding onto the entered prompt. | 680b80c5be754b31aa4de1db65b5c924 |
mit | ['text-generation', 'gpt2', 'gpt'] | false | citations ``` @inproceedings{dinan2019wizard, author={Emily Dinan and Stephen Roller and Kurt Shuster and Angela Fan and Michael Auli and Jason Weston}, title={{W}izard of {W}ikipedia: Knowledge-powered Conversational Agents}, booktitle = {Proceedings of the International Conference on Learning Representations ... | 1c373f2d73c7b0b3f49fa37b0d7a8ae5 |
apache-2.0 | ['generated_from_trainer'] | false | tiny-mlm-snli-target-glue-rte This model is a fine-tuned version of [muhtasham/tiny-mlm-snli](https://huggingface.co/muhtasham/tiny-mlm-snli) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.6539 - Accuracy: 0.5957 | 0a47bbe7d6b75d49992e2748ea3e5546 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6405 | 6.41 | 500 | 0.6868 | 0.6137 | | 0.4338 | 12.82 | 1000 | 0.8381 | 0.6209 | | 0.268 | 19.23 | 1500 | 1.0333 | 0.... | 7bdc99d264885be617d603decd3da828 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.2861 - Accuracy: 0.4731 - F1: 0.4643 | b0fdda55147b26513298cc618373b6a7 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 1.5548 | 1.0 | 63 | 1.4000 | 0.3880 | 0.3166 | | 1.3084 | 2.0 | 126 | 1.2861 | 0.4731 | 0.4643 | | e23e41cbdd8276bf91fb8a012c5fe354 |
mit | ['vision', 'image-classification'] | false | NAT (tiny variant) NAT-Tiny trained on ImageNet-1K at 224x224 resolution. It was introduced in the paper [Neighborhood Attention Transformer](https://arxiv.org/abs/2204.07143) by Hassani et al. and first released in [this repository](https://github.com/SHI-Labs/Neighborhood-Attention-Transformer). | d7f6a88f30e6becd825377f23ffdf97d |
mit | ['vision', 'image-classification'] | false | Example Here is how to use this model to classify an image from the COCO 2017 dataset into one of the 1,000 ImageNet classes: ```python from transformers import AutoImageProcessor, NatForImageClassification from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = ... | dbf0b39837c73423b0fb3faf8504fcc3 |
apache-2.0 | ['translation'] | false | opus-mt-war-sv * source languages: war * target languages: sv * OPUS readme: [war-sv](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/war-sv/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](http... | cbf257f596686164758b383b0df1ad3b |
apache-2.0 | ['generated_from_trainer'] | false | all-roberta-large-v1-meta-8-16-5-oos This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.4797 - Accuracy: 0.28 | 6870e469281437bdb3f5920da0747d7c |
mit | [] | false | gpt-fluentui-flat-svg A custom GPT model which was trained upon svg files. Specifically the flat emoji variants from [Microsoft's FluentUI repo](https://github.com/microsoft/fluentui-emoji). These svn files only consist of "stand-alone" path elements which should make it simpler to train upon and sample from. | 9611bcecb16d8870f44c4c3f6f586f28 |
mit | [] | false | training and dataset Both Tokenizer and Model were trained using [aitextgen](https://docs.aitextgen.io/) The python file which was used for training, the .txt file dataset and a few generated samples can be found [here](https://huggingface.co/Norod78/gpt-fluentui-flat-svg/tree/main/train) | 7280895919c4a9a85fbb07e5789c3dc2 |
mit | [] | false | generated samples The generated samples below were also created with [this script](https://huggingface.co/Norod78/gpt-fluentui-flat-svg/blob/main/train/atg_train.py)   on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.7907 - Epoch: 1 | aeee5d0b3de139aa4522b947027c5274 |
apache-2.0 | ['generated_from_trainer'] | false | demo-transfer-learning This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.6183 - Accuracy: 0.8554 - F1: 0.8991 | a747fd5de34ce99275b3903b585886e5 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 459 | 0.3771 | 0.8358 | 0.8784 | | 0.5168 | 2.0 | 918 | 0.4530 | 0.8578 | 0.9033 | | 0.3018 |... | 8b276adc4ac7c8e663b41697a50f7616 |
mit | [] | false | model by no3 This your the **waifu diffusion** model fine-tuned the azura from [vibrant venture](https://store.steampowered.com/app/1264520) taught to **waifu diffusion** with Dreambooth. It can be used by modifying the `instance_prompt`: **sks_azura** You can also train your own concepts and upload them to the librar... | 169e97e865dbc058a6b3cbc1fac67c9f |
mit | [] | false | Note This model is based on **waifu diffusion** if you want to convert it to **.ckpt** with [this script](https://raw.githubusercontent.com/ratwithacompiler/diffusers_stablediff_conversion/main/convert_diffusers_to_sd.py) file make shore you choose waifu-diffusion v1.3 checkpoint not Stable Diffusion checkpoint becaus... | fedec3b168ad25660405becbade92183 |
mit | [] | false | German GPT-2 fine-tuned on Faust I and II We fine-tuned our German GPT-2 model on "Faust I and II" from Johann Wolfgang Goethe. These texts can be obtained from [Deutsches Textarchiv (DTA)](http://www.deutschestextarchiv.de/book/show/goethe_faust01_1808). We use the "normalized" version of both texts (to avoid out-of... | d19403ba69d3bbbc89f2c1e89a9a9738 |
apache-2.0 | ['generated_from_trainer'] | false | recipe-distilbert-is 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: 4.0558 | ec6f837ffb7ffcfd712e372cd9071192 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - 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_sche... | cdb5292b3201849159b86992c02446de |
apache-2.0 | [] | false | SELECTRA: A Spanish ELECTRA SELECTRA is a Spanish pre-trained language model based on [ELECTRA](https://github.com/google-research/electra). We release a `small` and `medium` version with the following configuration: | Model | Layers | Embedding/Hidden Size | Params | Vocab Size | Max Sequence Length | Cased | | ---... | 91b88d2fbfc2fa18e14b542502cbfaa4 |
apache-2.0 | [] | false | Usage From the original [ELECTRA model card](https://huggingface.co/google/electra-small-discriminator): "ELECTRA models are trained to distinguish "real" input tokens vs "fake" input tokens generated by another neural network, similar to the discriminator of a GAN." The discriminator should therefore activate the lo... | 6df8645b19efe030822354ef8aff0468 |
apache-2.0 | [] | false | ando pan rosa con tomate y aceite de oliva . -3.1 -3.6 -6.9 -3.0 0.19 -4.5 -3.3 -5.1 -5.7 -7.7 -4.4 -4.2 """ ``` However, you probably want to use this model to fine-tune it on a downstream task. We provide models fine-tuned on the [XNLI dataset](https://hugg... | 8f2615542ed43df83a5f2f627ba00b6a |
apache-2.0 | [] | false | Metrics We fine-tune our models on 3 different down-stream tasks: - [XNLI](https://huggingface.co/datasets/xnli) - [PAWS-X](https://huggingface.co/datasets/paws-x) - [CoNLL2002 - NER](https://huggingface.co/datasets/conll2002) For each task, we conduct 5 trials and state the mean and standard deviation of the m... | 2445026c4bf666979089c9b4ef5e76e9 |
apache-2.0 | [] | false | evaluation-) of the [Spanish Language Model](https://github.com/PlanTL-SANIDAD/lm-spanish) repo. | Model | CoNLL2002 - NER (f1) | PAWS-X (acc) | XNLI (acc) | Params | | --- | --- | --- | --- | --- | | SELECTRA small | 0.865 +- 0.004 | 0.896 +- 0.002 | 0.784 +- 0.002 | 22M | | SELECTRA medium | 0.873 +- 0.003 | 0.896 +... | 4e41706b3bbbf3e48944be60d3988c41 |
apache-2.0 | [] | false | Training We pre-trained our SELECTRA models on the Spanish portion of the [Oscar](https://huggingface.co/datasets/oscar) dataset, which is about 150GB in size. Each model version is trained for 300k steps, with a warm restart of the learning rate after the first 150k steps. Some details of the training: - steps: 30... | 1f67f052cf7f83cb1471eee4cae5ae55 |
apache-2.0 | [] | false | Motivation Despite the abundance of excellent Spanish language models (BETO, BSC-BNE, Bertin, ELECTRICIDAD, etc.), we felt there was still a lack of distilled or compact Spanish language models and a lack of comparing those to their bigger siblings. | 0c7ac8c7fc79018dec04e6c8b5a65a3f |
apache-2.0 | [] | false | Authors - David Fidalgo ([GitHub](https://github.com/dcfidalgo)) - Javier Lopez ([GitHub](https://github.com/javispp)) - Daniel Vila ([GitHub](https://github.com/dvsrepo)) - Francisco Aranda ([GitHub](https://github.com/frascuchon)) | a8c3262499350cfc68f269878b9d191b |
mit | ['generated_from_trainer'] | false | xlnet-base-rte-finetuned This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 2.6688 - Accuracy: 0.7040 | 2c4c5076a96473c6ff4d8b3fafb484a8 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epoch... | d2d9b4899f960c874ccf36138286d5f1 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 311 | 0.9695 | 0.6859 | | 0.315 | 2.0 | 622 | 2.2516 | 0.6498 | | 0.315 | 3.0 | 933 | 2.0439 | 0.... | 13244c75ef93ec0d26c71edd000889d6 |
openrail | [] | false | Basic Introduction * A chatbot you can chat with by sending messages whenever you want. We finetune the pre-trained **microsoft dialoGPT** dialogue systems on the movie dialogue corpus. And finally deploy the chatbots using the API provided by **google voice**. | 6e40a95fdc154cbdaed5cb2f13b88f49 |
openrail | [] | false | Movie Dialogue Corpus: https://www.kaggle.com/datasets/Cornell-University/movie-dialog-corpus The corpus contains a metadata-rich collection of fictional conversations extracted from raw movie scripts: * 220,579 conversational exchanges between 10,292 pairs of movie characters * involves 9,035 characters from 617 mo... | bac5586b7002353a6e84b0876e4ee651 |
agpl-3.0 | ['pytorch', 'causal-lm'] | false | 🪷 Lotus-12B Lotus-12B is a GPT-NeoX 12B model fine-tuned on 2.5GB of a diverse range of light novels, erotica, annotated literature, and public-domain conversations for the purpose of generating novel-like fictional text and conversations. | eb1276bd22a52ba8973d97ce0f37d3fc |
agpl-3.0 | ['pytorch', 'causal-lm'] | false | Model Description The model used for fine-tuning is [Pythia 12B Deduped](https://github.com/EleutherAI/pythia), which is a 12 billion parameter auto-regressive language model trained on [The Pile](https://pile.eleuther.ai/). | b49477293294f02064d072e650d7a83f |
agpl-3.0 | ['pytorch', 'causal-lm'] | false | Training Data & Annotative Prompting The data used in fine-tuning has been gathered from various sources such as the [Gutenberg Project](https://www.gutenberg.org/). The annotated fiction dataset has prepended tags to assist in generating towards a particular style. Here is an example prompt that shows how to use the... | e288936ae61e69e825a872a475a1d1dd |
agpl-3.0 | ['pytorch', 'causal-lm'] | false | Example Code ``` from transformers import AutoTokenizer, AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained('hakurei/lotus-12B') tokenizer = AutoTokenizer.from_pretrained('hakurei/lotus-12B') prompt = '''[ Title: The Dunwich Horror; Author: H. P. Lovecraft; Genre: Horror ] *** When a traveler''' inp... | 76cc891db3fb943fe9d7ce78d5d0afea |
agpl-3.0 | ['pytorch', 'causal-lm'] | false | Team members and Acknowledgements This project would not have been possible without the work done by EleutherAI. Thank you! - [Anthony Mercurio](https://github.com/harubaru) - Imperishable_NEET In order to reach us, you can join our [Discord server](https://discord.gg/touhouai). [ on the glue dataset. It achieves the following results on the evaluation set: - Loss: 2.5545 - Accuracy: 0.8280 | 7813d5735be7d73908228fb8e8ce4b9a |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.6309 | 1.0 | 4210 | 2.5545 | 0.8280 | | 0.6148 | 2.0 | 8420 | 2.4299 | 0.8245 | | 0.595 | 3.0 | 12630 | 2.3525 ... | 694bfddacd17cd6f97872bffa259992a |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | `Hoon_Chung/jsut_asr_train_asr_conformer8_raw_char_sp_valid.acc.ave` ♻️ Imported from https://zenodo.org/record/4292742/ This model was trained by Hoon Chung using jsut/asr1 recipe in [espnet](https://github.com/espnet/espnet/). | dd9e7feaa31b56d89daad06135d25dce |
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