license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'image-to-image', 'art', 'artistic', 'diffusers', 'filmation'] | false | Model Usage Use the token ```filmation motu style``` --- ☕ If you enjoy this model, buy us a coffee [](https://ko-fi.com/3eegames) --- | 7d1017b17dc804386b0a5d34b5a96b92 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'image-to-image', 'art', 'artistic', 'diffusers', 'filmation'] | false | 🧾 Prompt example: **The MOTU styled woman** ```award winning masterpiece illustration of a woman, filmation motu style, trending on artstation, highly detailed, rendered with Unreal Engine, 8k``` [Negative prompt]( | d846053a330f52708fd52bf0ca2e6f6d |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'image-to-image', 'art', 'artistic', 'diffusers', 'filmation'] | false | ❎-negative-prompt-template) _Steps: 40, Sampler: DPM++ 2S a Karras, CFG scale: 7, Seed: 311713104, Size: 512x512, Model hash: c0302f10_ --- **Refined He-Man** ```award winning masterpiece illustration of a man, filmation motu style, trending on artstation, highly detailed``` [Negative prompt]( | a6aaa6af6d361472aaa02ede50f9f884 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'image-to-image', 'art', 'artistic', 'diffusers', 'filmation'] | false | ❎ Negative Prompt Template All images were rendered with the negative prompt below: ```lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, artist name``` | cf6a3a8c8526e29e914d327167850c2d |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'image-to-image', 'art', 'artistic', 'diffusers', 'filmation'] | false | 🧨 Diffusers This model can be used just like any other Stable Diffusion model. For more information, please have a look at the [Stable Diffusion](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion). Export the model: - [ONNX](https://huggingface.co/docs/diffusers/optimization/onnx) - [MPS](https:/... | bba17cd600293dd27bc74eb9ba911f5b |
mit | ['generated_from_trainer'] | false | roberta-large_ner_wnut_17 This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the wnut_17 dataset. It achieves the following results on the evaluation set: - Loss: 0.2288 - Precision: 0.7346 - Recall: 0.6256 - F1: 0.6757 - Accuracy: 0.9650 | 43372a1ce3f23b622153e767037f95bf |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 213 | 0.1805 | 0.6403 | 0.6089 | 0.6242 | 0.9598 | | No log | 2.0 |... | f4b281bd8b87b3beeba839f95f1365e3 |
mit | ['pytorch', 'diffusers', 'unconditional-image-generation', 'diffusion-models-class'] | false | Example Fine-Tuned Model for Unit 2 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This is a diffusion model trained to generate butterflies using fine-tuning with only 2 epochs. | 927ed254569dbe414050ee8a49b82979 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | hyjtestmodel Dreambooth model trained by huang1995 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-stabl... | 97230b4507b5505187d304044af2094b |
apache-2.0 | ['automatic-speech-recognition', 'ar'] | false | exp_w2v2t_ar_vp-it_s204 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 (ar)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that you... | 5075aa2cf32627f55aa9cec3097c2a86 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-sst-2-english-finetuned-imdb This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.2165 - ... | 378cef9b27759238f3bb76b4f35dded8 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.2749 | 1.0 | 3125 | 0.2165 | 0.9303 | | 4aa2e42436df320d3a1b4d36137ee55f |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased_fold_5_ternary 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: 1.8096 - F1: 0.7352 | 972fd53e0297c4218a6cb59ffb3d53fb |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 291 | 0.6322 | 0.6742 | | 0.5533 | 2.0 | 582 | 0.5861 | 0.7285 | | 0.5533 | 3.0 | 873 | 0.6893 | 0.7117 | |... | a085765e063b75da95e1511ba17aa57f |
cc-by-4.0 | ['espnet', 'audio', 'text-to-speech'] | false | `kan-bayashi/jvs_tts_finetune_jvs010_jsut_vits_raw_phn_jaconv_pyopenjtalk_accent_with_pause_latest` ♻️ Imported from https://zenodo.org/record/5432566/ This model was trained by kan-bayashi using jvs/tts1 recipe in [espnet](https://github.com/espnet/espnet/). | d8e8ab3beabc15e835a9c5928da630ca |
mit | ['vision', 'image-segmentation'] | false | UperNet, Swin Transformer tiny-sized backbone UperNet framework for semantic segmentation, leveraging a Swin Transformer backbone. UperNet was introduced in the paper [Unified Perceptual Parsing for Scene Understanding](https://arxiv.org/abs/1807.10221) by Xiao et al. Combining UperNet with a Swin Transformer backbo... | cd1471cbc9d472ca0371f72bda33f57d |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | fi_core_news_sm Finnish pipeline optimized for CPU. Components: tok2vec, tagger, morphologizer, parser, lemmatizer (trainable_lemmatizer), senter, ner. | Feature | Description | | --- | --- | | **Name** | `fi_core_news_sm` | | **Version** | `3.5.0` | | **spaCy** | `>=3.5.0,<3.6.0` | | **Default Pipeline** | `tok2vec`... | a1f6e4bba3bcd177d7f03bc9aabf7358 |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | Label Scheme <details> <summary>View label scheme (2145 labels for 4 components)</summary> | Component | Labels | | --- | --- | | **`tagger`** | `A`, `Adj`, `Adp`, `Adv`, `Adv_V`, `C`, `C_V`, `Foreign`, `Interj`, `N`, `Num`, `Pron`, `Punct`, `Symb`, `V`, `V_Pron`, `_SP` | | **`morphologizer`** | `Case=Nom\|Number=S... | b54dd72e8aeaf450b8bdec2179702034 |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | Accuracy | Type | Score | | --- | --- | | `TOKEN_ACC` | 100.00 | | `TOKEN_P` | 99.79 | | `TOKEN_R` | 99.90 | | `TOKEN_F` | 99.85 | | `TAG_ACC` | 93.35 | | `POS_ACC` | 92.57 | | `MORPH_ACC` | 86.56 | | `MORPH_MICRO_P` | 91.98 | | `MORPH_MICRO_R` | 90.45 | | `MORPH_MICRO_F` | 91.21 | | `SENTS_P` | 90.19 | | `SENTS_R` |... | 390d20e141915fac0323da54a006431f |
gpl-3.0 | ['pytorch', 'token-classification', 'bert', 'zh'] | false | Usage Please use BertTokenizerFast as tokenizer instead of AutoTokenizer. 請使用 BertTokenizerFast 而非 AutoTokenizer。 ``` from transformers import ( BertTokenizerFast, AutoModel, ) tokenizer = BertTokenizerFast.from_pretrained('bert-base-chinese') model = AutoModel.from_pretrained('ckiplab/bert-base-chinese-pos') ... | d92ec578281f6a3a726a36eaad3c32ce |
apache-2.0 | ['generated_from_keras_callback'] | false | Xzt/bert-finetuned-ner This model is a fine-tuned version of [hfl/chinese-bert-wwm-ext](https://huggingface.co/hfl/chinese-bert-wwm-ext) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0622 - Epoch: 0 | afe7ab4f9ad0b8db50fd9edf01938a98 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 7039, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay':... | b57b53c451045370f12c835ccbd1f887 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.1683 | 3c619d0b7fa13f41f4ae81aa55cb67ee |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.2264 | 1.0 | 5533 | 1.1663 | | 0.9606 | 2.0 | 11066 | 1.1288 | | 0.7432 | 3.0 | 16599 | 1.1683 | | 17ee9d76afbb23bf5fa471475f58b5cf |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Small Welsh This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the mozilla-foundation/common_voice_11_0 cy dataset. It achieves the following results on the evaluation set: - Loss: 0.4594 - Wer: 29.7843 | 3ca812766e51e23444384fb21ee33e85 |
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: 32 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant_with_warmup - lr_scheduler_warmup_steps: 50 - training_steps: 1000 -... | a59a4a53db9909f9905fd6fa72677015 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.1288 | 3.02 | 1000 | 0.4594 | 29.7843 | | ad8251b690ba238964e1a6a12271c550 |
apache-2.0 | ['generated_from_trainer'] | false | tiny-mlm-glue-mnli-target-glue-sst2 This model is a fine-tuned version of [muhtasham/tiny-mlm-glue-mnli](https://huggingface.co/muhtasham/tiny-mlm-glue-mnli) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5014 - Accuracy: 0.7993 | 4bebaea83df19bafff7917abab2242c7 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.5929 | 0.24 | 500 | 0.4966 | 0.7752 | | 0.4445 | 0.48 | 1000 | 0.4688 | 0.7833 | | 0.3937 | 0.71 | 1500 | 0.4462 | 0.... | 42c79547d4f34b48f45c84f19d9deb83 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | openai/whisper-large-v2-breton This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.7162 - Wer: 39.9271 | 25e33bbe407332db2a8d0fef978d3948 |
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: 8 - seed: 42 - distributed_type: multi-GPU - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_sc... | 397c072b2da847e3238c70296defe811 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.7423 | 0.1 | 100 | 0.8363 | 57.1553 | | 0.4361 | 1.07 | 200 | 0.6833 | 46.7176 | | 0.2227 | 2.03 | 300 | 0.6483 | 42.592... | c1a0cec963703b3fa7dfd6a01d4f4cdb |
apache-2.0 | ['generated_from_trainer'] | false | superglue-boolq This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2098 - Accuracy: 76.7584 - Average Metrics: 76.7584 | 525b0e284a55827f1fb8613867d34bfc |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Average Metrics | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------------:| | No log | 0.34 | 100 | 0.2293 | 73.2722 | 73.2722 | | No log | 0.68 | 200 | 0.2098 | 76.7584 |... | 2f8c9778facaf86b3031c68e771d6ca2 |
other | ['vision'] | false | SegFormer (b2-sized) encoder pre-trained-only SegFormer encoder fine-tuned on Imagenet-1k. It was introduced in the paper [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://arxiv.org/abs/2105.15203) by Xie et al. and first released in [this repository](https://github.com/NVla... | 076fe3b1e76107b4accebf03818c0d9e |
other | ['vision'] | false | How to use Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes: ```python from transformers import SegformerFeatureExtractor, SegformerForImageClassification from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769... | 44c84facc467473532744547d3fb4307 |
other | ['vision'] | false | model predicts one of the 1000 ImageNet classes predicted_class_idx = logits.argmax(-1).item() print("Predicted class:", model.config.id2label[predicted_class_idx]) ``` For more code examples, we refer to the [documentation](https://huggingface.co/transformers/model_doc/segformer.html | f761b4e6b08766d8f9f10b7b82aee539 |
apache-2.0 | ['generated_from_trainer'] | false | bart-large-mrpc This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) on the GLUE MRPC dataset. It achieves the following results on the evaluation set: - Loss: 0.5684 - Accuracy: 0.8775 - F1: 0.9120 - Combined Score: 0.8947 | 8666259ba53021dc5c1e99289da0e42a |
mit | [] | false | oanarinaldi artist on Stable Diffusion This is the `<oanarinaldi>` 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) notebook. You... | 2f8f1922b2d464754bdee6a607649410 |
apache-2.0 | ['generated_from_trainer'] | false | small-mlm-glue-rte-custom-tokenizer-expand-vocab This model is a fine-tuned version of [google/bert_uncased_L-4_H-512_A-8](https://huggingface.co/google/bert_uncased_L-4_H-512_A-8) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.9781 | 79be7b6d7e1fb1873c14f56b571df232 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 5.366 | 1.6 | 500 | 4.5434 | | 4.5071 | 3.21 | 1000 | 4.1161 | | 4.0413 | 4.81 | 1500 | 3.7831 | | 3.6922 | 6.41 | 2000 | 3.4965 ... | 394bae1ea3749d73cb4cd1b485d04d22 |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | mt5-small-finetuned-amazon-en-es 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: 3.1185 - Rouge1: 17.2081 - Rouge2: 8.8374 - Rougel: 16.8033 - Rougelsum: 16.663 | 0537274b6023a49385f9e53c5367c12c |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:| | No log | 1.0 | 303 | 3.9821 | 8.3993 | 2.0894 | 8.1427 | 8.135 | | No log | 2.0 |... | d528a7318be52a8716365120d2676b92 |
other | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'zh', 'Chinese'] | false | Examples Firstly, install our package as follows. This package is modified [🤗's Diffusers library](https://github.com/huggingface/diffusers) to run Chinese Stable Diffusion. ```bash diffusers==0.6.0 transformers torch datasets accelerate sentencepiece ``` Run this command to log in with your HF Hub token if you h... | 3086a1aa28323fb81170ac29f5c1563f |
other | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'zh', 'Chinese'] | false | Generator Results comparison [https://github.com/svjack/Stable-Diffusion-Chinese-Extend](https://github.com/svjack/Stable-Diffusion-Chinese-Extend)  . We refer the reader to the [SantaCoder model page](https://huggingface.co/bigcode/santacoder) for full documentation about this model - **Repository:** [bigcode/Megatron-LM](https://github.com/bigcode-project/Mega... | f91bee41c0a47b4cdd001c99123e6069 |
mit | ['generated_from_trainer'] | false | 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: 0.1360 - F1: 0.8645 | 7d2710f822264362107100730da146ef |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2528 | 1.0 | 787 | 0.1657 | 0.8244 | | 0.1298 | 2.0 | 1574 | 0.1369 | 0.8555 | | 0.0787 | 3.0 | 2361 | 0.1360 | 0.8645 | ... | fb76f65c1f773b9b16579e30b62d5ba8 |
gpl-3.0 | ['question-answering', 'camembert'] | false | Usage ```python from transformers import pipeline nlp = pipeline('question-answering', model='illuin/camembert-base-fquad', tokenizer='illuin/camembert-base-fquad') nlp({ 'question': "Qui est Claude Monet?", 'context': "Claude Monet, né le 14 novembre 1840 à Paris et mort le 5 décembre 1926 à Giverny, est u... | 6628eeb255a5371f0d1492eed03a8a6a |
gpl-3.0 | ['question-answering', 'camembert'] | false | Citation If you use our work, please cite: ```bibtex @article{dHoffschmidt2020FQuADFQ, title={FQuAD: French Question Answering Dataset}, author={Martin d'Hoffschmidt and Maxime Vidal and Wacim Belblidia and Tom Brendl'e and Quentin Heinrich}, journal={ArXiv}, year={2020}, volume={abs/2002.06071} } ``` | 0c02e8bc5d3fb27124ba45d9e11e28b6 |
apache-2.0 | ['automatic-speech-recognition', 'en'] | false | exp_w2v2r_en_xls-r_gender_male-0_female-10_s727 Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (en)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure ... | 7472e44741ee99f049e9335d524f2866 |
cc-by-4.0 | ['question generation'] | false | Model Card of `research-backup/t5-large-squad-qg-no-paragraph` This model is fine-tuned version of [t5-large](https://huggingface.co/t5-large) for question generation task on the [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-... | 5e2e51e4acc15435ef4b72f3ba4fc3c4 |
cc-by-4.0 | ['question generation'] | false | Overview - **Language model:** [t5-large](https://huggingface.co/t5-large) - **Language:** en - **Training data:** [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (default) - **Online Demo:** [https://autoqg.net/](https://autoqg.net/) - **Repository:** [https://github.com/asahi417/lm-question-gener... | ae322ef52ac9cce139d3d53057cb10aa |
cc-by-4.0 | ['question generation'] | false | model prediction questions = model.generate_q(list_context="William Turner was an English painter who specialised in watercolour landscapes", list_answer="William Turner") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "research-backup/t5-large-squad-qg... | 294246d6cf2f979345fc0833e275dd53 |
cc-by-4.0 | ['question generation'] | false | Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/research-backup/t5-large-squad-qg-no-paragraph/raw/main/eval/metric.first.sentence.sentence_answer.question.lmqg_qg_squad.default.json) | | Score | Type | Dataset ... | 0d97382f8a179913abc899b5a75a3b02 |
cc-by-4.0 | ['question generation'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_squad - dataset_name: default - input_types: ['sentence_answer'] - output_types: ['question'] - prefix_types: ['qg'] - model: t5-large - max_length: 128 - max_length_output: 32 - epoch: 6 - batch: 16... | 4519c361e74bfe9fe2c8e6e4197b471e |
mit | ['generated_from_trainer'] | false | gpt2-finetuned-nft-shakes-seuss This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.8505 | c08253703fdadf21274078a8c9f27107 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 4.2178 | 1.0 | 1095 | 4.0073 | | 3.9522 | 2.0 | 2190 | 3.8824 | | 3.8393 | 3.0 | 3285 | 3.8505 | | a153dcfe28ed3634add1438997c792ca |
apache-2.0 | ['generated_from_trainer'] | false | finetuning-sentiment-model-3000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.2830 - Accuracy: 0.8733 - F1: 0.8742 | 4dcfdb7175d585b238b97f78d5518c70 |
apache-2.0 | ['generated_from_trainer'] | false | 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_v2 dataset. It achieves the following results on the evaluation set: - Loss: 1.4088 | c6655307ae58dab1fc8b6009a7d7f186 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.2545 | 1.0 | 8235 | 1.2770 | | 0.9861 | 2.0 | 16470 | 1.3071 | | 0.8098 | 3.0 | 24705 | 1.4088 | | 016246522fabc9e63914fe58a79f0046 |
mit | ['sklearn', 'skops', 'tabular-classification'] | false | Hyperparameters The model is trained with below hyperparameters. <details> <summary> Click to expand </summary> | Hyperparameter | Value | |------------------|---------| | alpha | 1 | | class_prior | | | fit_prior | 1 | | norm | 0 | </details> | 980868ce5922098423b6a1127d09f4be |
mit | ['sklearn', 'skops', 'tabular-classification'] | false | sk-949a9edd-cac5-469d-9c40-9f504bdd0b78 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;} | a0c26d040094323f28ae154c2c4a01da |
mit | ['sklearn', 'skops', 'tabular-classification'] | false | sk-949a9edd-cac5-469d-9c40-9f504bdd0b78 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;position: relative;} | d0ec1f599ade2f43c2f45d346489ea8f |
mit | ['sklearn', 'skops', 'tabular-classification'] | false | sk-949a9edd-cac5-469d-9c40-9f504bdd0b78 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. ... | a583ca0c14dff18c854d88d34aaa61cc |
mit | ['sklearn', 'skops', 'tabular-classification'] | false | sk-949a9edd-cac5-469d-9c40-9f504bdd0b78 div.sk-text-repr-fallback {display: none;}</style><div id="sk-949a9edd-cac5-469d-9c40-9f504bdd0b78" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>ComplementNB()</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.<... | b5306683ab510ba2c76cbde5fb3d02da |
apache-2.0 | ['generated_from_keras_callback'] | false | whisper_werbest This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.3071 - Train Accuracy: 0.0324 - Train Wermet: 1.7931 - Validation Loss: 0.5766 - Validation Accuracy: ... | 490518159a83e899d4532f59fe544925 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Train Accuracy | Train Wermet | Validation Loss | Validation Accuracy | Validation Wermet | Epoch | |:----------:|:--------------:|:------------:|:---------------:|:-------------------:|:-----------------:|:-----:| | 5.0795 | 0.0116 | 43.8776 | 4.4395 | 0.0122... | 0d182ed2a364c3c247ff5fa1971cd783 |
apache-2.0 | ['generated_from_trainer'] | false | sentiment-model-sample-group-emotion 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: 2.4604 - Accuracy: 0.7004 | 3ab5990aef06cdd87a4c85bdd2c7f236 |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Whisper Small Hi - Sanchit Gandhi 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: - eval_loss: 0.2226 - eval_wer: 10.0023 - eval_runtime: 65.2041 - eval_samples_per_seco... | ff9f5c2bf675e9ead498358207d00c12 |
apache-2.0 | ['hf-asr-leaderboard', '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: 200 - training_steps: 1000 - mixed_precisi... | 754ee8f5f9f3533f3b80d00ff50dbce0 |
mit | ['generated_from_trainer'] | false | 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: 0.1379 - F1: 0.8653 | 3b329494f6d448dabd39f153faa7891a |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2557 | 1.0 | 525 | 0.1583 | 0.8231 | | 0.1269 | 2.0 | 1050 | 0.1393 | 0.8524 | | 0.0826 | 3.0 | 1575 | 0.1379 | 0.8653 | ... | ad53d0748f8b6822bf3a2e3d8a0a6421 |
apache-2.0 | [] | false | This model is used detecting **hatespeech** in **English language**. The mono in the name refers to the monolingual setting, where the model is trained using only English language data. It is finetuned on multilingual bert model. The model is trained with different learning rates and the best validation score achieved ... | ceb0163a9d135b8db6aef3465bc96e4b |
apache-2.0 | [] | false | For more details about our paper Sai Saketh Aluru, Binny Mathew, Punyajoy Saha and Animesh Mukherjee. "[Deep Learning Models for Multilingual Hate Speech Detection](https://arxiv.org/abs/2004.06465)". Accepted at ECML-PKDD 2020. ***Please cite our paper in any published work that uses any of these resources.*** ~~~... | 7c529e72750779c0c5e6ca989fd7c73b |
apache-2.0 | ['generated_from_trainer'] | false | summarise_v6 This model is a fine-tuned version of [allenai/led-base-16384](https://huggingface.co/allenai/led-base-16384) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0497 - Rouge2 Precision: 0.3109 - Rouge2 Recall: 0.406 - Rouge2 Fmeasure: 0.3375 | a132809b4573b0a21f47b02eea50c69f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure | |:-------------:|:-----:|:----:|:---------------:|:----------------:|:-------------:|:---------------:| | 1.7163 | 0.22 | 10 | 1.2307 | 0.1428 | 0.5118 | 0.2089 ... | 61b27e299ce79e9cb93956905e4f290b |
apache-2.0 | ['automatic-speech-recognition', 'sv-SE'] | false | exp_w2v2t_sv-se_vp-sv_s331 Fine-tuned [facebook/wav2vec2-large-sv-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-sv-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (sv-SE)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure th... | a187472a0a610449ec2ec840869de271 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - 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 | 51728d03a00708d88a62920c0ee4d1a4 |
cc-by-4.0 | ['espnet', 'audio', 'self-supervised-learning'] | false | `simpleoier/simpleoier_librispeech_hubert_iter0_train_ssl_torchaudiohubert_base_960h_pretrain_it0_raw` This model was trained by simpleoier using librispeech recipe in [espnet](https://github.com/espnet/espnet/). | 1f670e929cd20530f2b4ce1e1cc83019 |
cc-by-4.0 | ['espnet', 'audio', 'self-supervised-learning'] | false | Demo: How to use in ESPnet2 Follow the [ESPnet installation instructions](https://espnet.github.io/espnet/installation.html) if you haven't done that already. ```bash cd espnet git checkout 753f40d61813436d4e76660904d02eaed7a6649e pip install -e . cd egs2/librispeech/ssl1 ./run.sh --skip_data_prep false --skip_train... | de2513cf17c0c85b1b71ad6a5c260c6b |
cc-by-4.0 | ['espnet', 'audio', 'self-supervised-learning'] | false | SSL config <details><summary>expand</summary> ``` config: conf/tuning/train_ssl_torchaudiohubert_base_960h_pretrain_it0.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/hubert_iter0_train_ssl_torchaudiohubert_base_960h_pretrain_it0_raw ngpu: 1 seed: 0 num_workers: 64 nu... | d44369ac45dfed645d0eab444cc1a3fb |
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 the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2263 - Accuracy: 0.918 - F1: 0.9179 | 88ef1339b128e755a94b3bb259f6a943 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8566 | 1.0 | 250 | 0.3283 | 0.903 | 0.9002 | | 0.2607 | 2.0 | 500 | 0.2263 | 0.918 | 0.9179 | | 9f3aa88ac21f7af45036d38379ac4fe7 |
mit | ['generated_from_trainer'] | false | lilt-en-funsd This model is a fine-tuned version of [SCUT-DLVCLab/lilt-roberta-en-base](https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base) on the funsd-layoutlmv3 dataset. It achieves the following results on the evaluation set: - Loss: 1.7699 - Answer: {'precision': 0.8906439854191981, 'recall': 0.89718482252... | daa9084b6b4cc596c63c7c93354d5af6 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Answer | Header | Question ... | 4630c648ff1aed6cbe6366f9ad5c04d4 |
afl-3.0 | ['t5'] | false | chunked T5 - base (cT5-base) Github: https://github.com/mtreviso/chunked-t5 A T5 model that uses a new loss where a special end-of-chunk token `</c>` is appended after sentinel tokens. The decoder has to predict the full input with masked tokens followed by `</c>`. This allows a much faster auto-regressive generat... | 0c21d74eec33d517755cf5028e559cba |
afl-3.0 | ['t5'] | false | Training details cT5 models used T5's weights as a starting point, and then it was finetuned on the English [wikipedia](https://huggingface.co/datasets/wikipedia) for 3 epochs, achieving ~74% validation accuracy (ct5-base). The training script is in JAX + Flax and can be found in `pretrain_ct5.py`. Flax checkpoint... | e118e00d3f1f191a2e759f6a1de6d3d6 |
afl-3.0 | ['t5'] | false | Usage ```python from transformers import AutoTokenizer from modeling_ct5 import CT5ForConditionalGeneration tokenizer = AutoTokenizer.from_pretrained("mtreviso/ct5-base-en-wiki") model = CT5ForConditionalGeneration.from_pretrained("mtreviso/ct5-base-en-wiki") ``` For training: ```python input_ids = tokenizer("The ... | fa236317dae16dc1aa25718a2675d916 |
afl-3.0 | ['t5'] | false | the default is 9999999, which is a large number ) ``` This will produce the following tokens: ```python >> ['<pad>', '<extra_id_0>', '▁Walking', '▁Trail', '</c>', '<extra_id_1>', '▁the', '</c>', '<extra_id_2>', '</s>'] >> ['<pad>', '<extra_id_0>', '▁treat', '▁Syria', '</c>', '<extra_id_1>', '</s>', '<pad>', '<pad>', ... | b2ccbeb987c51a7ac6276258ae5bdfc6 |
afl-3.0 | ['t5'] | false | Evaluation See the notebook `evaluate_ct5.ipynb` for an example of how to evaluate cT5 in terms of accuracy and perplexity. The notebook `profile.ipynb` shows how to profile the model to get runtimes. Here is a comparison between cT5-small and T5-small on a subset of the WikiText-103 dataset using deterministic gree... | b671855008aba807ed185305a85547f9 |
apache-2.0 | ['tapas'] | false | TAPAS small model fine-tuned on WikiSQL (in a supervised fashion) his model has 2 versions which can be used. The default version corresponds to the `tapas_wikisql_sqa_inter_masklm_small_reset` checkpoint of the [original Github repository](https://github.com/google-research/tapas). This model was pre-trained on MLM ... | bbe8ca0acb366bbf857a515379b4aa62 |
mit | [] | false | bart-large-mnli This is the checkpoint for [bart-large](https://huggingface.co/facebook/bart-large) after being trained on the [MultiNLI (MNLI)](https://huggingface.co/datasets/multi_nli) dataset. Additional information about this model: - The [bart-large](https://huggingface.co/facebook/bart-large) model page - [BA... | 824ff78db929250282a5ddf6c974f4e7 |
mit | [] | false | NLI-based Zero Shot Text Classification [Yin et al.](https://arxiv.org/abs/1909.00161) proposed a method for using pre-trained NLI models as a ready-made zero-shot sequence classifiers. The method works by posing the sequence to be classified as the NLI premise and to construct a hypothesis from each candidate label.... | e902db3746eb436b32bc437b60fa6a43 |
mit | [] | false | With the zero-shot classification pipeline The model can be loaded with the `zero-shot-classification` pipeline like so: ```python from transformers import pipeline classifier = pipeline("zero-shot-classification", model="facebook/bart-large-mnli") ``` You can then use this pipeline to classif... | a9584004128144ba8d38556e7d4079ce |
mit | [] | false | 'sequence': 'one day I will see the world'} ``` If more than one candidate label can be correct, pass `multi_class=True` to calculate each class independently: ```python candidate_labels = ['travel', 'cooking', 'dancing', 'exploration'] classifier(sequence_to_classify, candidate_labels, multi_class=True) | 3d8752b4a94b7127c6f7b6ef86ca5446 |
mit | [] | false | pose sequence as a NLI premise and label as a hypothesis from transformers import AutoModelForSequenceClassification, AutoTokenizer nli_model = AutoModelForSequenceClassification.from_pretrained('facebook/bart-large-mnli') tokenizer = AutoTokenizer.from_pretrained('facebook/bart-large-mnli') premise = sequence hypoth... | cc66bb40f1f3e63d3e13a0fdc5b79e80 |
apache-2.0 | ['generated_from_trainer'] | false | finetuning-sentiment-model-4000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.2706 - Accuracy: 0.9 - F1: 0.9038 | 8828e1ab37888bf6ab2074d1af3e33c2 |
apache-2.0 | ['generated_from_trainer'] | false | 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_v2 dataset. It achieves the following results on the evaluation set: - Loss: 1.4104 | 8ee5232b00fbb38150336c7ba2ad97a6 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.2182 | 1.0 | 8235 | 1.2318 | | 0.9451 | 2.0 | 16470 | 1.2693 | | 0.7554 | 3.0 | 24705 | 1.4104 | | cf073cdb932c90f2af8f27178134fc4e |
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