license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.869 | 1.0 | 250 | 0.3161 | 0.9075 | 0.9053 | | 0.2564 | 2.0 | 500 | 0.2182 | 0.9265 | 0.9266 | | 16ffe453d0d370d703dbfe583a9d5551 |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_8_0', 'robust-speech-event'] | false | This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - SV-SE dataset. It achieves the following results on the evaluation set: - Loss: 0.3549 - Wer: 0.3827 | 9f8734bea86b7e2c1023c3e04a39dde0 |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_8_0', 'robust-speech-event'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7.5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_... | c6090eef67dba77389da07a2603d4179 |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_8_0', 'robust-speech-event'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.4129 | 5.49 | 500 | 3.3224 | 1.0 | | 2.9323 | 10.98 | 1000 | 2.9128 | 1.0000 | | 1.6839 | 16.48 | 1500 | 0.7740 | 0.6854 | |... | a1287de02e491797bbc353e37b525057 |
apache-2.0 | ['stanza', 'token-classification'] | false | Stanza model for Icelandic (is) Stanza is a collection of accurate and efficient tools for the linguistic analysis of many human languages. Starting from raw text to syntactic analysis and entity recognition, Stanza brings state-of-the-art NLP models to languages of your choosing. Find more about it in [our website](h... | b82613301db3924f2c4033a799179892 |
apache-2.0 | ['generated_from_trainer'] | false | t5-end2end-questions-generation-cv-squadV2 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: 1.8541 | 0a4a1e7734f0dedfe9e37075e3dfeb7e |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_ep... | c3047389175eff6c31ce2b1e46ee10ab |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.6703 | 2.17 | 100 | 1.9685 | | 1.9718 | 4.34 | 200 | 1.8541 | | 60f4eb107a842bb73a169eaa0749374f |
mit | ['generated_from_trainer'] | false | blissful_leakey This model was trained from scratch on the tomekkorbak/pii-pile-chunk3-0-50000, the tomekkorbak/pii-pile-chunk3-50000-100000, the tomekkorbak/pii-pile-chunk3-100000-150000, the tomekkorbak/pii-pile-chunk3-150000-200000, the tomekkorbak/pii-pile-chunk3-200000-250000, the tomekkorbak/pii-pile-chunk3-250... | 2135c03c96063009f585fa6044b34413 |
mit | ['generated_from_trainer'] | false | Full config {'dataset': {'datasets': ['tomekkorbak/pii-pile-chunk3-0-50000', 'tomekkorbak/pii-pile-chunk3-50000-100000', 'tomekkorbak/pii-pile-chunk3-100000-150000', 'tomekkorbak/pii-pile-chunk3-150000-200000', 'tom... | 8e69bdd1bacd88ad4539009a02bd154c |
other | ['generated_from_trainer', 'opt', 'custom-license', 'non-commercial', 'email', 'auto-complete', '125m'] | false | > NOTE: there is currently a bug with huggingface API for OPT models. Please use the [colab notebook](https://colab.research.google.com/gist/pszemraj/033dc9a38da31ced7a0343091ba42e31/email-autocomplete-demo-125m.ipynb) to test :) | bc3763a30d0f60cff1b89a864e6e92e1 |
other | ['generated_from_trainer', 'opt', 'custom-license', 'non-commercial', 'email', 'auto-complete', '125m'] | false | opt for email generation - 125m Why write the rest of your email when you can generate it? ``` from transformers import pipeline model_tag = "pszemraj/opt-125m-email-generation" generator = pipeline( 'text-generation', model=model_tag, use_fast=False, do_samp... | cf8192586d293e085d4ee05596d7a302 |
other | ['generated_from_trainer', 'opt', 'custom-license', 'non-commercial', 'email', 'auto-complete', '125m'] | false | About This model is a fine-tuned version of [facebook/opt-125m](https://huggingface.co/facebook/opt-125m) on an `aeslc` dataset. - Emails, phone numbers, etc., were attempted to be excluded in a dataset preparation step using [clean-text](https://pypi.org/project/clean-text/) in Python. - Note that API is restrict... | f3d8e58309fc803fd71be7865e9528ce |
other | ['generated_from_trainer', 'opt', 'custom-license', 'non-commercial', 'email', 'auto-complete', '125m'] | false | Intended uses & limitations - OPT models cannot be used commercially - [here is a GitHub gist](https://gist.github.com/pszemraj/c1b0a76445418b6bbddd5f9633d1bb7f) for a script to generate emails in the console or to a text file. | 36b997af3716b02ed322ce01af02ada6 |
other | ['generated_from_trainer', 'opt', 'custom-license', 'non-commercial', 'email', 'auto-complete', '125m'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.8245 | 1.0 | 129 | 2.8030 | | 2.521 | 2.0 | 258 | 2.6343 | | 2.2074 | 3.0 | 387 | 2.5595 | | 2.0145 | 4.0 | 516 | 2.5552 ... | 3862f480c348b93c2e8d49366a72a546 |
creativeml-openrail-m | ['text-to-image'] | false | jessy-3500 Dreambooth model trained by eicu with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v1-5 base model You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blo... | 003aec03d8aee3ef01dddd4cf8d00e7c |
mit | [] | false | The model generated in the Enrich4All project.<br> Evaluated the perplexity of MLM Task fine-tuned for COVID-related corpus.<br> Baseline model: https://huggingface.co/racai/distilbert-base-romanian-cased <br> Scripts and corpus used for training: https://github.com/racai-ai/e4all-models Corpus --------------- The CO... | 9970436bb43e86cd892666c1d86322ec |
apache-2.0 | ['generated_from_trainer'] | false | all-roberta-large-v1-utility-4-16-5-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: 2.3728 - Accuracy: 0.3956 | 02bf750ac18e993c907276398c0e6e50 |
apache-2.0 | ['generated_from_keras_callback'] | false | nandysoham/3-clustered This model is a fine-tuned version of [Rocketknight1/distilbert-base-uncased-finetuned-squad](https://huggingface.co/Rocketknight1/distilbert-base-uncased-finetuned-squad) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.6964 - Train End Logits Acc... | acc6b0ae7cc26cf56bbd9a52187439f8 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 596, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_... | 500153b3673efeb9b8f677148ee9daa1 |
apache-2.0 | ['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 | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:----------... | 76c959bbe35afb20647b362760268f11 |
apache-2.0 | ['translation'] | false | opus-mt-gaa-de * source languages: gaa * target languages: de * OPUS readme: [gaa-de](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/gaa-de/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-20.zip](http... | 44583b62279dc1f5f64afddaf2df1a9e |
mit | [] | false | JoJo Bizzare Adventure manga lineart on Stable Diffusion This is the `<JoJo_lineart>` 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.i... | 17598958a9fcd814c3f359c275fc8f33 |
apache-2.0 | ['automatic-speech-recognition', 'nl'] | false | exp_w2v2t_nl_vp-it_s449 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 (nl)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that you... | 39040fc5dea0cfaceda5dc570ad34f63 |
apache-2.0 | ['generated_from_trainer'] | false | whisper-base.en This model is a fine-tuned version of [openai/whisper-base.en](https://huggingface.co/openai/whisper-base.en) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8125 - Wer: 50.1754 | bca8c7ded732a25479e4a5caa1483741 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 5 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - training_steps: 100 - mixed_precision... | e3dc23d0d4adc8cefcb2b7cb75b3db89 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.7532 | 1.12 | 100 | 0.8125 | 50.1754 | | 11de464bbfb3ecf7d2edf6ff0ff83a75 |
apache-2.0 | ['generated_from_trainer'] | false | finetuning-sentiment-model-3000-samples-pi 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: 0.3344 - Accuracy: 0.8633 - F1: 0.8664 | 6b53ce57918e575a27a3a37cad18338c |
mit | [] | false | model by ShadoWxShinigamI It can be used by adding **in the style of mdjrny-grfft** to the end of your prompt. (Token is mdjrny-grfft, but since the weight is too strong (over trained text encoder), using the full sentence can help in better style transfer (YMMV)). NO PROMPT ENGINEERING REQUIRED. Trained On TheLast... | bfd476eb0d60ac7d8fccb18c2c46dfbf |
mit | ['token-classification', 'fill-mask'] | false | This model is the combined camembert-base model, with the pretrained lilt checkpoint from the paper "LiLT: A Simple yet Effective Language-Independent Layout Transformer for Structured Document Understanding". Original repository: https://github.com/jpWang/LiLT To use it, it is necessary to fork the modeling and ... | a29c8438face3e3e938cdb4f5119da66 |
mit | ['token-classification', 'fill-mask'] | false | patch_transformers() must have been executed beforehand tokenizer = AutoTokenizer.from_pretrained("camembert-base") model = AutoModel.from_pretrained("manu/lilt-camembert-base") model = AutoModelForTokenClassification.from_pretrained("manu/lilt-camembert-base") | b0039c5b3b1923d1d0897ceb10e12269 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-xlsr-53-espeak-cv-ft-evn3-ntsema-colab This model is a fine-tuned version of [facebook/wav2vec2-xlsr-53-espeak-cv-ft](https://huggingface.co/facebook/wav2vec2-xlsr-53-espeak-cv-ft) on the audiofolder dataset. It achieves the following results on the evaluation set: - Loss: 1.5004 - Wer: 0.97 | f957ce80bccfdea6350661e27270eca6 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 4.8078 | 7.14 | 400 | 1.3558 | 0.9933 | | 0.7854 | 14.28 | 800 | 1.2786 | 0.98 | | 0.3685 | 21.43 | 1200 | 1.4606 | 0.9733 | |... | 9d09331e1837d9759db31e957a555a92 |
mit | ['generated_from_trainer'] | false | run-1 This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.3480 - Accuracy: 0.73 - Precision: 0.6930 - Recall: 0.6829 - F1: 0.6871 | bb5dc33f530aaa8f973e520ff6ffd9e1 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | 1.0042 | 1.0 | 50 | 0.8281 | 0.665 | 0.6105 | 0.6240 | 0.6016 | | 0.8062 | 2.0 |... | 2b6efea6d742b2b7358560d5e4890614 |
apache-2.0 | [] | false | mk-gpt2 Test the whole generation capabilities here: https://transformer.huggingface.co/doc/gpt2-large Pretrained model on English language using a causal language modeling (CLM) objective. It was introduced in [this paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_mul... | 626a325c477b5356fc2f3e74cbad8d71 |
apache-2.0 | [] | false | Model description mk-gpt2 is a transformers model pretrained on a very large corpus of Macedonian data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to genera... | d0ca7cc4951f464b0f745a76c34f93e8 |
apache-2.0 | [] | false | How to use Here is how to use this model to get the features of a given text in PyTorch: import random from transformers import AutoTokenizer, AutoModelWithLMHead tokenizer = AutoTokenizer.from_pretrained('macedonizer/mk-gpt2') \ model = AutoModelWithLMHead.from_pretrained('macedonizer/mk-gpt2') input_text = 'Скопј... | 3bd5f09fc82cf177bda417052e2655b5 |
apache-2.0 | ['generated_from_trainer'] | false | finetuning-sentiment-model-24000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.3505 - Accuracy: 0.9267 - F1: 0.9274 | 587bdf241c7c2c198a62552d0881dcb6 |
creativeml-openrail-m | ['coreml', 'stable-diffusion', 'text-to-image'] | false | Anything V4 Welcome to Anything V4 - a latent diffusion model for weebs. The newest version of Anything. This model is intended to produce high-quality, highly detailed anime style with just a few prompts. Like other anime-style Stable Diffusion models, it also supports danbooru tags to generate images. e.g. **_1gir... | af13d83d9cdbd68a508efb238bb88f46 |
creativeml-openrail-m | ['coreml', 'stable-diffusion', 'text-to-image'] | false | Gradio We support a [Gradio](https://github.com/gradio-app/gradio) Web UI to run anything-v4.0: [. You can also export the model to [ONNX](https://huggingface.co/docs/diffusers/optimization/onnx), [... | 493bcf95df15ef360d908d342226ff08 |
creativeml-openrail-m | ['coreml', 'stable-diffusion', 'text-to-image'] | false | Examples Below are some examples of images generated using this model: **Anime Girl:**  ``` masterpiece, best quality, 1girl, white hair, medium hair, cat ears, closed eyes, looking at viewer, :3, cute, scarf, jacket, outdoors, stre... | ca54c1db8fec08e5c6d8bda925a42b11 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Large Es - Javier Alonso This model is a fine-tuned version of [openai/whisper-large](https://huggingface.co/openai/whisper-large) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.1571 - Wer: 5.5201 | f70db7e7603f1cf397d6692a13692745 |
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: 8 - eval_batch_size: 2 - 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: 10000 - mixed_precisi... | bb13aa4ae669316f5ce3491892cd9dc6 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 0.211 | 0.1 | 1000 | 0.2293 | 8.3896 | | 0.2227 | 0.2 | 2000 | 0.2215 | 8.2552 | | 0.1496 | 0.3 | 3000 | 0.2121 | 8.036... | 134c1814a19ca96cae5120fba38d7ede |
apache-2.0 | ['generated_from_trainer'] | false | finetuned-mlm_small This model is a fine-tuned version of [muhtasham/bert-small-mlm-finetuned-emotion](https://huggingface.co/muhtasham/bert-small-mlm-finetuned-emotion) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.5097 - Accuracy: 0.9084 - F1: 0.9520 | 6e8015c7f4829564eb2a0897c6366adf |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.2852 | 2.55 | 500 | 0.1781 | 0.9334 | 0.9656 | | 0.1243 | 5.1 | 1000 | 0.3215 | 0.9078 | 0.9517 | | 0.0543 |... | 7a7d54585336fa8c014482ec3693647d |
mit | ['generated_from_trainer'] | false | deberta-finetuned-ner This model is a fine-tuned version of [microsoft/deberta-base](https://huggingface.co/microsoft/deberta-base) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0515 - Precision: 0.9577 - Recall: 0.9652 - F1: 0.9614 - Accuracy: 0.9907 | c6f572ca6a22774957ed3fa6cca4d269 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0742 | 1.0 | 1756 | 0.0526 | 0.9390 | 0.9510 | 0.9450 | 0.9868 | | 0.0374 | 2.0 |... | 748da919f9e53df596303bc6afe39be4 |
apache-2.0 | ['automatic-speech-recognition', 'en'] | false | exp_w2v2r_en_vp-100k_gender_male-10_female-0_s691 Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (en)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using ... | 459a443579766d17e2764b7ddb69ba1c |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-rotten-tomatoes This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the rotten_tomatoes dataset. It achieves the following results on the evaluation set: - Loss: 0.3616 - Accuracy: 0.8386 - F1: 0.8386 | 2bacc7110d21bda8af9fff162de80c2d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.4767 | 1.0 | 134 | 0.3825 | 0.8227 | 0.8221 | | 0.3106 | 2.0 | 268 | 0.3616 | 0.8386 | 0.8386 | | 85d9a10fe1595abcae0309cb19a37908 |
apache-2.0 | ['generated_from_trainer'] | false | canine-s-finetuned-stsb This model is a fine-tuned version of [google/canine-s](https://huggingface.co/google/canine-s) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.7223 - Pearson: 0.8397 - Spearmanr: 0.8397 | e0149c482356ffbf2e5e67b7a7262c24 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Pearson | Spearmanr | |:-------------:|:-----:|:----:|:---------------:|:-------:|:---------:| | No log | 1.0 | 360 | 0.7938 | 0.8083 | 0.8077 | | 1.278 | 2.0 | 720 | 0.7349 | 0.8322 | 0.8305 | | 0.6765 ... | 20a835988cf9be82501e1aae842baf04 |
cc-by-sa-4.0 | ['generated_from_trainer'] | false | t5-base-TEDxJP-0front-1body-3rear This model is a fine-tuned version of [sonoisa/t5-base-japanese](https://huggingface.co/sonoisa/t5-base-japanese) on the te_dx_jp dataset. It achieves the following results on the evaluation set: - Loss: 0.4700 - Wer: 0.1779 - Mer: 0.1718 - Wil: 0.2600 - Wip: 0.7400 - Hits: 55384 - S... | a3b248bf429510c1534ea20d24192ea3 |
cc-by-sa-4.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Mer | Wil | Wip | Hits | Substitutions | Deletions | Insertions | Cer | |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:------:|:-----:|:-------------:|:---------:|:----------:|:------:| | 0.6519 ... | b968686f5038434e50426992565d2a72 |
apache-2.0 | ['text-generation', 'dialogue-generation', 'pytorch', 'inference acceleration', 'gpt2', 'gpt3'] | false | YuYan-Dialogue YuYan is a series of Chinese language models with different size, developed by Fuxi AI lab, Netease.Inc. They are trained on a large Chinese novel dataset of high quality. YuYan is in the same family of decoder-only models like [GPT2 and GPT-3](https://arxiv.org/abs/2005.14165). As such, it was pretr... | 322ab6a94a9606a6622919edd5edb584 |
apache-2.0 | ['text-generation', 'dialogue-generation', 'pytorch', 'inference acceleration', 'gpt2', 'gpt3'] | false | make a folder, move the dictionary file and model file into it. mkdir transformer_lm_gpt2_xxl_dialogue mv dict.txt transformer_lm_gpt2_xxl_dialogue/ mv checkpoint_best_part_*.pt transformer_lm_gpt2_xxl_dialogue/ ``` `inference.py` is a script to provide a interface to initialize the EET object and sequence_generator.... | d504f1c0c11abfe9acc016be0a2031ee |
apache-2.0 | ['text-generation', 'dialogue-generation', 'pytorch', 'inference acceleration', 'gpt2', 'gpt3'] | false | max inference batch size, adjust according to cuda memory, 40GB memory is necessary inference = Inference(model_path, data_path, eet_batch_size) dialogue_model = Dialogue(inference) dialogue_model.get_repsonse("你好啊") ``` | 317c159ea23f1db5395181826cd0f269 |
apache-2.0 | ['generated_from_trainer'] | false | whisper-small-en This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the librispeech_asr dataset. It achieves the following results on the evaluation set: - Loss: 6.7832 - Wer: 124.5115 | e48556b84ebe56e6555331598a145af2 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-06 - lr_scheduler_type: linear - lr_sche... | e46b5c6378da13d88f4f6d20ac52799c |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:---------:| | 9.6259 | 1.57 | 5 | 10.7408 | 1127.3535 | | 11.5288 | 3.29 | 10 | 9.2534 | 100.0 | | 10.9249 | 4.86 | 15 | 7.8357 ... | 0d23d8f3efffcf1ef4defb1ce8892ced |
apache-2.0 | ['exbert', 'multiberts', 'multiberts-seed-4'] | false | MultiBERTs Seed 4 Checkpoint 1700k (uncased) Seed 4 intermediate checkpoint 1700k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in [this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in [this repository](https://github.com/g... | 2ad741adf5400fa6f12caf461fdae72e |
apache-2.0 | ['exbert', 'multiberts', 'multiberts-seed-4'] | false | How to use Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import BertTokenizer, BertModel tokenizer = BertTokenizer.from_pretrained('multiberts-seed-4-1700k') model = BertModel.from_pretrained("multiberts-seed-4-1700k") text = "Replace me by any text you'd lik... | 963ac2776ce64abc110d2cdcdad99ca5 |
apache-2.0 | ['translation'] | false | opus-mt-iso-en * source languages: iso * target languages: en * OPUS readme: [iso-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/iso-en/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-09.zip](http... | 7ad87bad4b5f2293cf138188d5af3e00 |
apache-2.0 | ['automatic-speech-recognition', 'de'] | false | exp_w2v2r_de_xls-r_gender_male-0_female-10_s922 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 (de)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure ... | c88f10bbb1a92d80a953e7c7cf75df77 |
apache-2.0 | ['generated_from_trainer'] | false | all-roberta-large-v1-meta-8-16-5 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 | 8e2b087f6c9c3bb1e7fff4f16e617bfc |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'diffusers', 'safetensors'] | false | Gradio We support a [Gradio](https://github.com/gradio-app/gradio) Web UI to run gigafractal2-diffusion: [ on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1623 - F1: 0.8596 | 3ddd82eb3af8658023bb950a84a2e8a0 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2865 | 1.0 | 715 | 0.1981 | 0.8167 | | 0.1484 | 2.0 | 1430 | 0.1595 | 0.8486 | | 0.0949 | 3.0 | 2145 | 0.1623 | 0.8596 | ... | c5eafe3f1405daaf14ce94222e7f38ad |
apache-2.0 | ['translation'] | false | opus-mt-is-fi * source languages: is * target languages: fi * OPUS readme: [is-fi](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/is-fi/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-09.zip](https://... | 385497a5c03be6b7fc7492fb94f50c40 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-large-xls-r-300m-hi 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: 2.4156 - Wer: 0.7181 | 9098e423e940cfa587604ea2f0639de2 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 5.7703 | 2.72 | 400 | 2.2274 | 0.9259 | | 0.6515 | 5.44 | 800 | 1.5812 | 0.7581 | | 0.339 | 8.16 | 1200 | 2.0590 | 0.7825 | |... | 68728c99f3604ce39ceff94082ae7e1c |
mit | ['audio', 'text-to-speech'] | false | SpeechT5 (TTS task) SpeechT5 model fine-tuned for speech synthesis (text-to-speech) on LibriTTS. This model was introduced in [SpeechT5: Unified-Modal Encoder-Decoder Pre-Training for Spoken Language Processing](https://arxiv.org/abs/2110.07205) by Junyi Ao, Rui Wang, Long Zhou, Chengyi Wang, Shuo Ren, Yu Wu, Shujie... | 686231e902514568141c71d15ce739ba |
mit | ['audio', 'text-to-speech'] | false | How to Get Started With the Model Use the code below to convert text into a mono 16 kHz speech waveform. ```python from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech, SpeechT5HifiGan import torch import soundfile as sf processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts") model = ... | f7230b5a4eb11b88209a05fccb332f08 |
mit | ['audio', 'text-to-speech'] | false | load xvector containing speaker's voice characteristics from a dataset embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation") speaker_embeddings = torch.tensor(embeddings_dataset[7306]["xvector"]).unsqueeze(0) speech = model.generate_speech(inputs["input_ids"], speaker_embeddings, vocod... | 473eb771ec2407691ee0e11041a53da4 |
mit | ['audio', 'text-to-speech'] | false | Intended Uses & Limitations You can use this model for speech synthesis. See the [model hub](https://huggingface.co/models?search=speecht5) to look for fine-tuned versions on a task that interests you. Currently, both the feature extractor and model support PyTorch. | 29aec5335f5d475939c234de9ac0d326 |
creativeml-openrail-m | [] | false | Stable Diffusion v1-5 with the fine-tuned VAE `sd-vae-ft-mse` and files with config modifications for making it better to fine-tune made by [fast-stable-diffusion by TheLastBen](https://github.com/TheLastBen/fast-stable-diffusion) to be used on [fastDreambooth Colab Notebook](https://colab.research.google.com/github/Th... | 917172e8b38519b222726711016c7609 |
mit | [] | false | princess_knight_art on Stable Diffusion This is the `<princess-knight>` 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... | e9a9e6b1bca6dff1ac2a3bd555589745 |
apache-2.0 | ['multiberts', 'multiberts-seed_3', 'multiberts-seed_3-step_140k'] | false | MultiBERTs, Intermediate Checkpoint - Seed 3, Step 140k MultiBERTs is a collection of checkpoints and a statistical library to support robust research on BERT. We provide 25 BERT-base models trained with similar hyper-parameters as [the original BERT model](https://github.com/google-research/bert) but with different ... | e320aa837eaac2da35ac6dfefd524aa9 |
apache-2.0 | ['multiberts', 'multiberts-seed_3', 'multiberts-seed_3-step_140k'] | false | How to use Using code from [BERT-base uncased](https://huggingface.co/bert-base-uncased), here is an example based on Tensorflow: ``` from transformers import BertTokenizer, TFBertModel tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_3-step_140k') model = TFBertModel.from_pretrained("google/multibe... | 41737a2d5e12dcdbb4610e5e69ffeb25 |
apache-2.0 | ['generated_from_trainer'] | false | t5-small-finetuned-en-to-it-lrs-back 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: 1.7887 - Bleu: 15.4528 - Gen Len: 52.516 | 1bf3ea35bb018f71800f4d6913a6c7f0 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:| | 2.8637 | 1.0 | 1125 | 2.7212 | 3.496 | 82.846 | | 2.6665 | 2.0 | 2250 | 2.5507 | 5.4897 | 65.4087 | | 2.5307 ... | 194c9f14f16c67e33ddf8b7579179aba |
mit | ['vision', 'image-to-text', 'image-captioning', 'visual-question-answering'] | false | BLIP-2, Flan T5-xl, pre-trained only BLIP-2 model, leveraging [Flan T5-xl](https://huggingface.co/google/flan-t5-xl) (a large language model). It was introduced in the paper [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://arxiv.org/abs/2301.12597) by Li... | d4b065aaa3bc858a5fe9ec69fe7528ef |
apache-2.0 | ['generated_from_trainer'] | false | Full config {'dataset': {'conditional_training_config': {'aligned_prefix': '<|aligned|>', 'drop_token_fraction': 0.05, 'misaligned_prefix': '<|misaligned|>', 'threshold': 0.000475}, ... | e8fdf2af7dca9b11139ee4be564c2853 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Wav2Vec2-Large-XLSR-53-Japanese Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Japanese using the [Common Voice](https://huggingface.co/datasets/common_voice), and JSUT dataset{s}. When using this model, make sure that your speech input is sampled at 16kHz. | 9cf7a982a65feb4dc4ab28ec7660cbe7 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Usage The model can be used directly (without a language model) as follows: ```python import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor test_dataset = load_dataset("common_voice", "ja", split="test[:2%]") processor = Wav2Vec2Processor.from_p... | 07d012c813be188d29d844b8e7d4aa82 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Evaluation The model can be evaluated as follows on the Japanese test data of Common Voice. ```python !pip install torchaudio !pip install datasets transformers !pip install jiwer !pip install mecab-python3 !pip install unidic-lite !python -m unidic download !pip install jaconv import torch import torchaudio from d... | b62ede46d1e011ca417c74cd2143c5d8 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Japanese preprocessing tagger = MeCab.Tagger("-Owakati") chars_to_ignore_regex = '[\。\、\「\」\,\?\.\!\-\;\:\"\“\%\‘\”\�]' def text2kata(text): node = tagger.parseToNode(text) word_class = [] while node: word = node.surface wclass = node.feature.split(',') if wclass[0] != u'BOS/EOS': ... | a72bd8be508481d8664f5083afe79000 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | We need to read the aduio files as arrays def speech_file_to_array_fn(batch): batch["sentence"] = hiragana(batch["sentence"]).strip() batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler... | d406572bf9b16e8fbdade161ec097f76 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | We need to read the aduio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch... | 0ef6dc7349b387aee5124a48f9b0311b |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | TODO: adapt to state all the datasets that were used for training. --> The privately collected JSUT Japanese dataset was used for training. <!-- The script used for training can be found [here](...) | 10ebad3c891d0816c60bb3ea2a077808 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | TODO: fill in a link to your training script here. If you trained your model in a colab, simply fill in the link here. If you trained the model locally, it would be great if you could upload the training script on github and paste the link here. --> | 4584ec69dad0c1a4788cbaa1677b4fc7 |
mit | ['generated_from_trainer'] | false | gpt2.CEBaB_confounding.uniform.sa.5-class.seed_43 This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the OpenTable OPENTABLE dataset. It achieves the following results on the evaluation set: - Loss: 0.9552 - Accuracy: 0.5672 - Macro-f1: 0.4441 - Weighted-macro-f1: 0.5100 | 5ae3c983491ab2ac9edb976c79fa5de4 |
apache-2.0 | ['generated_from_trainer'] | false | bert-emotion This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the tweet_eval dataset. It achieves the following results on the evaluation set: - Loss: 1.1658 - Precision: 0.7311 - Recall: 0.7299 - Fscore: 0.7299 | 920e51d5464e61f96544832b7e82bcb8 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | Fscore | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:| | 0.8562 | 1.0 | 815 | 0.7859 | 0.7527 | 0.6006 | 0.6173 | | 0.5352 | 2.0 | 1630 | 0.9248 | 0.7545 ... | e876dd218d45fdf624b8ce4c74823370 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | teamcomo-chf Dreambooth model trained by DFrostKilla 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-sta... | 49175e75a0451d0745efac4b69632c72 |
mit | [] | false | <design> on Stable Diffusion This is the `<design>` 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 can also train... | afea698e00ab350544da5e1f0ea59f0d |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-cased-ner_cv-med-ft This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5926 - Precision: 0.2559 - Recall: 0.3460 - F1: 0.2942 - Accuracy: 0.8368 | 0d6eb33be2d8d8f16482f08b18ad755b |
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