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 | ['endpoints-template'] | false | FORK of FLAN-T5 XXL > This is a fork of google/flan-t5-xxl implementing a custom `handler.py` as an example for how to use t5-11b with inference-endpoints on a single NVIDIA A10G. You can deploy the flan-t5-xxl with a [1-click](https://ui.endpoints.huggingface.co/new?repository=philschmid/flan-t5-xxl-sharded-fp16). ... | 57321a0ff443a72674ba3341ad75075d |
apache-2.0 | [] | false | Model Description A BERT-like model pretrained on Java software code. - **Developed by:** Christian-Albrechts-University of Kiel (CAUKiel) - **Shared by [Optional]:** Hugging Face - **Model type:** Fill-Mask - **Language(s) (NLP):** en - **License:** Apache-2.0 - **Related Models:** A version of this model using a... | 7bde6b669dc5537abfd282bebb00163f |
apache-2.0 | [] | false | Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. { see paper= word something) | f9ca0cb5985d680a9b41ebdb78d54a39 |
apache-2.0 | [] | false | compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** More information needed. - **Hours used:** More information needed. - **Cloud Provider:** More information needed. - **Compute Region:** More information needed. - **Carbon Emitted:** More information needed. | 17dc7d59615bc2a6e2e819c9a0dcaf0b |
apache-2.0 | [] | false | How to Get Started with the Model Use the code below to get started with the model. <details> <summary> Click to expand </summary> ```python from transformers import pipeline pipe = pipeline('fill-mask', model='CAUKiel/JavaBERT') output = pipe(CODE) | 778e622fb5c335b8c18c24dd76df9cdf |
apache-2.0 | ['generated_from_trainer'] | false | whisper-small-bem This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4251 - Wer: 39.6676 | 90d4dbf4bfb746202641a56d42838ea2 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.4394 | 1.34 | 1000 | 0.4104 | 45.0013 | | 0.2845 | 2.68 | 2000 | 0.3709 | 39.4217 | | 0.1073 | 4.03 | 3000 | 0.3963 | 38.472... | 69bcef14dbc5015d1326d4d00b95745a |
apache-2.0 | [] | false | ALBERT XLarge v2 Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in [this paper](https://arxiv.org/abs/1909.11942) and first released in [this repository](https://github.com/google-research/albert). This model, as all ALBERT models, is uncased: it does not make... | 4bf183dd6fe2b4a0154ea26a2147272f |
apache-2.0 | [] | false | Model description ALBERT is a transformers model pretrained on a large corpus of English 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 generate input... | 09cf51e56847593f16a9525771514513 |
apache-2.0 | [] | false | How to use You can use this model directly with a pipeline for masked language modeling: ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='albert-xlarge-v2') >>> unmasker("Hello I'm a [MASK] model.") [ { "sequence":"[CLS] hello i'm a modeling model.[SEP]", "sc... | 8f7e955718dfb91b911c0f157e963b5e |
apache-2.0 | [] | false | Limitations and bias Even if the training data used for this model could be characterized as fairly neutral, this model can have biased predictions: ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='albert-xlarge-v2') >>> unmasker("The man worked as a [MASK].") [ { ... | b75bdf7a9c8cd006b5c866661303e3f7 |
apache-2.0 | ['generated_from_trainer'] | false | bert-uncased-base This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an Reddit-dialogue dataset. This model can be used for Text Classification: Given two sentences, see if they are related. It achieves the following results on the evaluation set: - Loss: 0.2297 - A... | 4db555aa7fbb0229d5b4bfe0fc9ca112 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 320 - eval_batch_size: 80 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 | 7a5405878e7919a58157d5c1b5832290 |
apache-2.0 | ['generated_from_trainer'] | false | Set the input post = "don't make gravy with asbestos." response = "i'd expect someone with a culinary background to know that. since we're talking about school dinner ladies, they need to learn this pronto." | a623b6a55729601e4da2c29ad635ee79 |
apache-2.0 | ['generated_from_trainer'] | false | Predict whether the two sentences are matched def predict(post, response, max_seq_length=128): with torch.no_grad(): args = (post, response) input = tokenizer(*args, padding="max_length", max_length=max_seq_length, truncation=True, return_tensors="pt") output = model(**input) logi... | ca4004dacb31b0fb789b8dec287851c2 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | sentence-transformers/paraphrase-MiniLM-L6-v2 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search. This is a clone of the original model, with `pipeline_tag` metadata change... | 01006eb16325b6986fd77cff69b58d5a |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sen... | e2b1b84b62b1847c1bd503b36b6bd477 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/paraphrase-MiniLM-L6-v2') model = AutoModel.from_pretrained('sentence-transformers/paraphrase-MiniLM-L6-v2') | b8e0e57dd15b974d735178637e434c3a |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Evaluation Results For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/paraphrase-MiniLM-L6-v2) | 6545b7c350778001a94cf1ae833a520f |
cc-by-4.0 | ['generated_from_trainer'] | false | deepset_deberta-v3-large-squad2_1.23e-04_9.40e-02_8_512_7 This model is a fine-tuned version of [deepset/deberta-v3-large-squad2](https://huggingface.co/deepset/deberta-v3-large-squad2) on an unknown dataset. | 3eed11c144bdba96a7cbbb12440bb893 |
cc-by-4.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.00012263579392223837 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 7 - total_train_batch_size: 14 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: ... | 02de8235211d3d66b412148ab504b2a5 |
mit | [] | false | GTA5 Artwork on Stable Diffusion This is the `<gta5-artwork>` 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 ... | 982427a6e675940c9290ca957ef5c68c |
apache-2.0 | ['summarization', 'arabic', 'am', 'es', 'amharic', 'mt5', 'Abstractive Summarization', 'generated_from_trainer'] | false | mt5-base-finetuned-ar-sp This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.2772 - Rouge-1: 23.01 - Rouge-2: 10.41 - Rouge-l: 20.94 - Gen Len: 19.0 - Bertscore: 71.56 | 5d04d005173cd7a622ca57ebf969b9c1 |
apache-2.0 | ['summarization', 'arabic', 'am', 'es', 'amharic', 'mt5', 'Abstractive Summarization', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge-1 | Rouge-2 | Rouge-l | Gen Len | Bertscore | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:-------:|:---------:| | 4.1968 | 1.0 | 1352 | 3.5142 | 18.69 | 6.73 | 16.97 | 19.0 | 70.3... | 1e3f8d55586fc7d4ba1e27d9566d964e |
mit | [] | false | hebrew_bible_ai Finetuned gpt2-xl model on the Hebrew bible. This will babble new potentially good looking Hebrew bible verses. You can run an example at Hugging Face: https://huggingface.co/tombenj/hebrew_bible_ai Based on the input from: https://raw.githubusercontent.com/BibleNLP/ebible/main/corpus/heb-heb.txt A... | 34101dccc8e3fcb32c5f15a56a7bcdf0 |
mit | [] | false | x2067; ``` ืืืื ืืืฆืจื ืืืฃ ืื ืื ืื ืืขืืื ืืืฆืืชื ืืฉืจ ืื ืืฉืืืขื ืืขื ืืฉืจ ืืขื ืืฉืืื ืืืฉืืื ืืฉืจ ืืขื ืื ื ื ืืื ืืฉืจ ืืฉืืชื ืืืืื ืืืื ืืืืื ืืืช ืืืื ืื ืืคืฅ ืื ืืืืจ ืืืื ืืืื ืืืืฉืจ ืืื ืืืจืฅ ืืงืืื ืืืงืืฆื ืืื ืืืืืจื ืื ืืืื ืืืืืื ืื ืืคืฅ ืื ืื ื ื ืืื ืืืืืื ืขื ืืขืจื ืืืื ืืืืืื ืืื ืืื ืืืืืื ืืจืืฉ ืืฉืจ ืืขืืื ืืืงืฆืฅ ืืืืืืข ืืคื ื ืืืื ืื... | c63f424fdb729df668a6ea135552c9e3 |
mit | [] | false | model by multimodalart This your the Stable Diffusion model fine-tuned the Cat toy concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: **a photo of sks toy** You can also train your own concepts and upload them to the library by using [this notebook](https://colab.re... | dbd6b1933a361a5cb5eca4f0356a151c |
mit | ['generated_from_trainer'] | false | aristo-roberta-finetuned-csqa This model is a fine-tuned version of [LIAMF-USP/aristo-roberta](https://huggingface.co/LIAMF-USP/aristo-roberta) on the commonsense_qa dataset. It achieves the following results on the evaluation set: - Loss: 1.2187 - Accuracy: 0.7305 | 5df3ad42f64d5efda3186213ddcfe262 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.131 | 1.0 | 609 | 0.7109 | 0.7232 | | 0.6957 | 2.0 | 1218 | 0.6912 | 0.7346 | | 0.459 | 3.0 | 1827 | 0.8364 | 0.... | f1fbdb143c02af59d1d911721d6268b9 |
mit | ['generated_from_trainer'] | false | bertimbau-large-lener_br This model is a fine-tuned version of [neuralmind/bert-large-portuguese-cased](https://huggingface.co/neuralmind/bert-large-portuguese-cased) on the lener_br dataset. It achieves the following results on the evaluation set: - Loss: 0.1271 - Precision: 0.8965 - Recall: 0.9198 - F1: 0.9080 - Ac... | ce547dfcb191f13a830107dfc07316b4 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0674 | 1.0 | 1957 | 0.1349 | 0.7617 | 0.8710 | 0.8127 | 0.9594 | | 0.0443 | 2.0 ... | 8c004deaa36d94057e007acd86fe589f |
apache-2.0 | ['generated_from_trainer'] | false | tiny-mlm-glue-mrpc-target-glue-qnli This model is a fine-tuned version of [muhtasham/tiny-mlm-glue-mrpc](https://huggingface.co/muhtasham/tiny-mlm-glue-mrpc) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4717 - Accuracy: 0.7798 | d9c88c6fc163b3de594514f1e83ba9e0 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6112 | 0.15 | 500 | 0.5408 | 0.7346 | | 0.5426 | 0.31 | 1000 | 0.5351 | 0.7366 | | 0.522 | 0.46 | 1500 | 0.5029 | 0.... | 54e700eb6a6b466dd72de3d918845268 |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | t5-v1_1-small-finetuned-samsum This model is a fine-tuned version of [google/t5-v1_1-small](https://huggingface.co/google/t5-v1_1-small) on the samsum dataset. It achieves the following results on the evaluation set: - Loss: 2.0053 - Rouge1: 0.4061 - Rouge2: 0.1804 - Rougel: 0.3478 - Rougelsum: 0.3774 | 8b4b4eee32d85dbb20ecc50a0d14d9d7 |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:| | 3.9788 | 1.0 | 1842 | 2.2499 | 0.3743 | 0.1569 | 0.3191 | 0.3486 | | 2.9091 | 2.0 | 3684 ... | 8d954b511d9c79a6586d83ec831d132f |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_7_0', 'robust-speech-event'] | false | wav2vec2-large-xls-r-300m-bashkir-cv7_opt 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_7_0 - BA dataset. It achieves the following results on the evaluation set: - Training Loss: 0.268400 - Validation L... | 649f1a9de68c0ab8d87e7c566ae92fe7 |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_7_0', 'robust-speech-event'] | false | Intended uses & limitations In order to reduce the number of characters, the following letters have been replaced or removed: - 'ั' -> 'ะนะฐ' - 'ั' -> 'ะนั' - 'ั' -> 'ะนะพ' - 'ะต' -> 'ะนั' for first letter - 'ะต' -> 'ั' for other cases - 'ั' -> deleted - 'ั' -> deleted Therefore, in order to get the correct text, you need ... | bd84b232e2ca1914c8da8a342d7aa6e2 |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_7_0', 'robust-speech-event'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sc... | 27de64c889ad477aef849a8f2b329460 |
apache-2.0 | ['automatic-speech-recognition', 'es'] | false | exp_w2v2r_es_vp-100k_gender_male-5_female-5_s244 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 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using t... | 8f869b95eb2a8a2807a0d81ca4fa4503 |
apache-2.0 | ['generated_from_trainer'] | false | bert-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0599 - Precision: 0.9330 - Recall: 0.9492 - F1: 0.9410 - Accuracy: 0.9862 | e61cbe2c0393fcf1334c4262950cdf89 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0852 | 1.0 | 1756 | 0.0647 | 0.9147 | 0.9345 | 0.9245 | 0.9826 | | 0.0305 | 2.0 |... | 26dd56658eb455a099e34624b4a0173d |
apache-2.0 | ['translation'] | false | opus-mt-fr-ro * source languages: fr * target languages: ro * OPUS readme: [fr-ro](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fr-ro/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://... | 382e9b3cd8bf0defff9751768794bf0c |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-jm-finetuned-panx-de_hub 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: 0.1401 - F1: 0.8619 | ca1626916a8df8c39bcc0a06c9e76f2a |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2566 | 1.0 | 525 | 0.1638 | 0.8263 | | 0.1309 | 2.0 | 1050 | 0.1414 | 0.8535 | | 0.0831 | 3.0 | 1575 | 0.1401 | 0.8619 | ... | f0fd5a019c7b58cc4b38d94988fe79e1 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | Brunoo Dreambooth model trained by BryanBizzAI 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-di... | edb15ea8466c674d6f41fa088a7c2302 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | Recipe | Interpolation Method | Primary Model | Secondary Model | Tertiary Model | Output | | ---- | ---- | ---- | ---- | ---- | | Weighted sum @ 0.5 | [Anything v4.5](https://huggingface.co/andite/anything-v4.0) | [Pastel Mix](https://huggingface.co/andite/pastel-mix) | \- | AnyPastel | | Add difference @ 0.3 | Any... | 029d9dd6b3fd58ed5f84e8d104b74927 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | Examples <img src="https://huggingface.co/m4gnett/any-pastel/resolve/main/images/2023-01-31_16.20.31_259005813.png" width="512px"> ``` solo, 1girl, portrait, looking at viewer, masterpiece, best quality, 4k, 8k, Negative prompt: (worst quality, low quality:1.4), (bad-image-v2-39000:0.75), (bad_prompt_v2:0.85), (censo... | 13f97a025444de9448f5a26feb53c0d9 |
apache-2.0 | ['pretraining', 'pixel'] | false | PIXEL (Pixel-based Encoder of Language) PIXEL is a language model trained to reconstruct masked image patches that contain rendered text. PIXEL was pretrained on the *English* Wikipedia and Bookcorpus (in total around 3.2B words) but can theoretically be finetuned on data in any written language that can be typeset o... | 6a235b81a3dc7f3feefd33b45a3f6a16 |
apache-2.0 | ['pretraining', 'pixel'] | false | Model description PIXEL consists of three major components: a text renderer, which draws text as an image; an encoder, which encodes the unmasked regions of the rendered image; and a decoder, which reconstructs the masked regions at the pixel level. It is built on [ViT-MAE](https://arxiv.org/abs/2111.06377). During ... | 9c24c5960abb6d3010318ecd58adebda |
apache-2.0 | ['pretraining', 'pixel'] | false | Intended uses PIXEL is primarily intended to be finetuned to downstream NLP tasks. See the [model hub](https://huggingface.co/models?search=Team-PIXEL/pixel-base) to look for finetuned versions on a task that interests you. Otherwise, check out the PIXEL codebase on Github [here](https://github.com/xplip/pixel) to fi... | 080d4714600a9b287fac4ca365808865 |
apache-2.0 | ['pretraining', 'pixel'] | false | How to use Here is how to load PIXEL: ```python from pixel import PIXELConfig, PIXELForPreTraining config = PIXELConfig.from_pretrained("Team-PIXEL/pixel-base") model = PIXELForPreTraining.from_pretrained("Team-PIXEL/pixel-base", config=config) ``` | f0e4e3496efd85265a5ff3c0689ac6b7 |
apache-2.0 | ['pretraining', 'pixel'] | false | Citing and Contact Author ```bibtex @article{rust-etal-2022-pixel, title={Language Modelling with Pixels}, author={Phillip Rust and Jonas F. Lotz and Emanuele Bugliarello and Elizabeth Salesky and Miryam de Lhoneux and Desmond Elliott}, journal={arXiv preprint}, year={2022}, url={https://arxiv.org/abs/2207.... | 8012b0554b8c0dac4c920ceac5101acd |
apache-2.0 | ['part-of-speech', 'token-classification'] | false | XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Old Church Slavonic This model is part of our paper called: - Make the Best of Cross-lingual Transfer: Evidence from POS Tagging with over 100 Languages Check the [Space](https://huggingface.co/spaces/wietsedv/xpos) for more details. | c4d546ea45247677ede57a540cf77186 |
apache-2.0 | ['part-of-speech', 'token-classification'] | false | Usage ```python from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-cu") model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-cu") ``` | e9672dec1283ff3e38ead55e3e08ebe6 |
apache-2.0 | ['tapas', 'sequence-classification'] | false | TAPAS tiny model fine-tuned on Tabular Fact Checking (TabFact) This model has 2 versions which can be used. The latest version, which is the default one, corresponds to the `tapas_tabfact_inter_masklm_tiny_reset` checkpoint of the [original Github repository](https://github.com/google-research/tapas). This model was... | ad34a2d9bb29a0fa8fc98bd2fd5d79bc |
apache-2.0 | ['pytorch', 'diffusers', 'unconditional-image-generation'] | false | Samples 1.  2.  3. ... | fbed4c698d68ff3c6fbe322862ebc0bf |
mit | ['generated_from_trainer'] | false | big-balanced-combined-bert This model is a fine-tuned version of [dbmdz/bert-base-turkish-128k-uncased](https://huggingface.co/dbmdz/bert-base-turkish-128k-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2872 - Accuracy: 0.9055 - F1: 0.9061 | 1dde8690f0b9237e4fd82e0bfad4572e |
cc-by-4.0 | ['espnet', 'audio', 'text-to-speech'] | false | Example ESPnet2 TTS model โป๏ธ Imported from https://zenodo.org/record/3963886/ This model was trained by kan-bayashi using jsut/tts1 recipe in [espnet](https://github.com/espnet/espnet/). Model id: `kan-bayashi/jsut_tts_train_tacotron2_raw_phn_jaconv_pyopenjtalk_train.loss.best` | 3d317dc28eb1e32b83c662aa280c3d9b |
apache-2.0 | ['generated_from_trainer'] | false | swin-tiny-patch4-window7-224-finetuned-eurosat This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the image_folder dataset. It achieves the following results on the evaluation set: - Loss: 0.0654 - Accuracy: 0.9763 | ee48971801d94ca91c770a2afa473089 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.2431 | 1.0 | 190 | 0.1119 | 0.9607 | | 0.1682 | 2.0 | 380 | 0.0921 | 0.9693 | | 0.1644 | 3.0 | 570 | 0.0654 | 0.... | 0a6d70bd316cd0485121073f6d5de846 |
apache-2.0 | ['exbert'] | false | BatterySciBERT-uncased model Pretrained model on a large corpus of battery research papers using a masked language modeling (MLM) objective, starting with the [SciBERT-uncased](https://huggingface.co/allenai/scibert_scivocab_uncased) weights. It was introduced in [this paper](paper_link) and first released in [this r... | 7c09134720bf6bbf5bcb346bd8e1d4e6 |
apache-2.0 | ['exbert'] | false | Model description BatterySciBERT is a transformers model pretrained on a large corpus of battery research papers in a self-supervised fashion, starting with the [SciBERT-uncased](https://huggingface.co/allenai/scibert_scivocab_uncased) weights. This means it was pretrained on the raw texts only, with no humans labell... | 4386f4650f4ee52fa1842728360c8031 |
apache-2.0 | ['exbert'] | false | Training data The BatterySciBERT model was pretrained on the full text of battery papers only, after initialized from the [SciBERT-uncased](https://huggingface.co/allenai/scibert_scivocab_uncased) weights. The paper corpus contains a total of 400,366 battery research papers that are published from 2000 to June 2021, ... | 2173ca8def69cfd69f82a5786ce0202b |
apache-2.0 | ['exbert'] | false | Preprocessing The texts are lowercased and tokenized using WordPiece and a vocabulary size of 31,090. The inputs of the model are then of the form: ``` [CLS] Sentence A [SEP] Sentence B [SEP] ``` The details of the masking procedure for each sentence are the following: - 15% of the tokens are masked. - In 80% of th... | d97ec0a29f8907b8db9bf87986b2e124 |
apache-2.0 | ['exbert'] | false | Pretraining The model was trained on 8 NVIDIA DGX A100 GPUs for 1,000,000 steps with a batch size of 256. The sequence length was limited to 512 tokens. The optimizer used is Adam with a learning rate of 2e-5, \\(\beta_{1} = 0.9\\) and \\(\beta_{2} = 0.999\\), a weight decay of 0.01, learning rate warmup for 10,000 ... | 1abf7da61d989c747df843ea61a638ac |
apache-2.0 | ['exbert'] | false | How to use You can use this model directly with a pipeline for masked language modeling: ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='batterydata/batteryscibert-uncased') >>> unmasker("Hello I'm a <mask> model.") ``` Here is how to use this model to get the features of... | 19ed2fa6840a043412d26931fb60c5cd |
apache-2.0 | ['generated_from_trainer'] | false | bert-finetuned-sla This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3274 - F1: 0.6555 - Roc Auc: 0.7660 - Accuracy: 0.5294 | 360b0fb1d481702ee55f31545246a75a |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 | 69b7d3b09bf5ecca6fd93c464b914af4 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:| | No log | 1.0 | 30 | 0.4994 | 0.0 | 0.5 | 0.0 | | No log | 2.0 | 60 | 0.4408 | 0.0 ... | 0b0b788828687949435f58c680bcb68f |
apache-2.0 | ['generated_from_trainer'] | false | recipe-distilbert-s 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: 1.0321 | fe725c497d382986ab42db8bea088318 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.8594 | 1.0 | 844 | 1.4751 | | 1.4763 | 2.0 | 1688 | 1.3282 | | 1.3664 | 3.0 | 2532 | 1.2553 | | 1.2975 | 4.0 | 3376 | 1.2093 ... | 1c73ddd175e3f044d85085000b67b441 |
apache-2.0 | ['generated_from_trainer'] | false | t5-small-finetuned-xsum This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xsum dataset. It achieves the following results on the evaluation set: - Loss: 2.7967 - Rouge1: 23.0533 - Rouge2: 3.912 - Rougel: 17.8534 - Rougelsum: 17.8581 - Gen Len: 18.6878 | a0184ad223bb7f4f64243802121e481a |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:| | 3.0574 | 1.0 | 1276 | 2.7967 | 23.0533 | 3.912 | 17.8534 | 17.8581 | 18.68... | a25cf05d92dd801337964f866f20360a |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.2992 - F1: 0.8494 | 0519ccda351890c914b577791569a180 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.537 | 1.0 | 382 | 0.3279 | 0.8002 | | 0.2603 | 2.0 | 764 | 0.2987 | 0.8356 | | 0.1589 | 3.0 | 1146 | 0.2992 | 0.8494 | ... | bd3885e9918ff8a8b4a4b233944c5d9f |
mit | ['generated_from_trainer'] | false | gpt2.CEBaB_confounding.observational.absa.5-class.seed_42 This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the OpenTable OPENTABLE-ABSA dataset. It achieves the following results on the evaluation set: - Loss: 0.5238 - Accuracy: 0.8223 - Macro-f1: 0.8182 - Weighted-macro-f1: 0.8228 | c9791c161a0bbd2aeecc297d81ef4f60 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-xls-r-tf-left-right-shuru This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0921 - Wer: 1.2628 | 71ba22cb239acd7385fa6f015bede5d2 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 100 - mixed_precision_... | 1790cbc9ba540e4db9e53ec3da6a445a |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 6.5528 | 23.81 | 500 | 0.5509 | 1.9487 | | 0.2926 | 47.62 | 1000 | 0.1306 | 1.2756 | | 0.1171 | 71.43 | 1500 | 0.1189 | 1.2628 | |... | 4bfce3fc9419133468b568e24c4e66aa |
mit | [] | false | model by Giordyman This your the Stable Diffusion model fine-tuned the Tempa2 concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: **a photo of sks Tempa** You can also train your own concepts and upload them to the library by using [this notebook](https://colab.resea... | c07dc2789c5cadd4c17fdb19f18237c8 |
apache-2.0 | ['generated_from_trainer'] | false | finetuned-marktextepoch-n600 This model is a fine-tuned version of [leokai/finetuned-marktextepoch-n500](https://huggingface.co/leokai/finetuned-marktextepoch-n500) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.6814 | bfae8ce3263fdcacdb9ee88c56ca73ed |
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: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 182 | 4b326498b9522b270a87a9e72a227c64 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:------:|:---------------:| | 0.5332 | 1.0 | 1606 | 2.5256 | | 0.5315 | 2.0 | 3212 | 2.4835 | | 0.5181 | 3.0 | 4818 | 2.5471 | | 0.5318 | 4.0 | 6424 | 2... | f84cd5dbcd1a5af87f8a3730cc4ede5b |
apache-2.0 | ['generated_from_trainer'] | false | new_classifer_epoch7 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: 0.1305 - Accuracy: 0.9861 | 6af6bed14ecab7e1e2c7370c51822a79 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.0526 | 1.0 | 4248 | 0.0587 | 0.9797 | | 0.0259 | 2.0 | 8496 | 0.0502 | 0.9855 | | 0.0121 | 3.0 | 12744 | 0.1170 ... | 76e5a1ec2e3cb2be0e6d6ed53b7e21be |
apache-2.0 | [] | false | Introduction Recently, pre-trained models have achieved state-of-the-art results in various language understanding tasks, which indicates that pre-training on large-scale corpora may play a crucial role in natural language processing. Current pre-training procedures usually focus on training the model with several s... | 106db25b17e7b2ed7ff1e1ee79248059 |
apache-2.0 | [] | false | Citation Info ```text @article{ernie2.0, title = {ERNIE 2.0: A Continual Pre-training Framework for Language Understanding}, author = {Sun, Yu and Wang, Shuohuan and Li, Yukun and Feng, Shikun and Tian, Hao and Wu, Hua and Wang, Haifeng}, journal={arXiv preprint arXiv:1907.12412}, year = {2019}, } ``` | bbba2457ae70f9e5cb5752460fd9ba1b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.2688 | 1.0 | 190 | 0.1419 | 0.9526 | | 0.1721 | 2.0 | 380 | 0.0858 | 0.97 | | 0.1079 | 3.0 | 570 | 0.0599 | 0.... | 0d3183d4dedfb88a4d7d3afd8deead92 |
apache-2.0 | ['translation'] | false | es-he * source group: Spanish * target group: Hebrew * OPUS readme: [spa-heb](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/spa-heb/README.md) * model: transformer * source language(s): spa * target language(s): heb * model: transformer * pre-processing: normalization + SentencePiece (spm3... | c528cccba3aa2700cf0f8382998bbe77 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: es-he - source_languages: spa - target_languages: heb - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/spa-heb/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['es', 'he'] - src_constituents: ('Spanish', {'spa'}) ... | 55f38cf01facdcc0c946fe7337f6d455 |
mit | [] | false | test-epson on Stable Diffusion This is the `<epson-branch>` 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 al... | 246eaa81e594dc6cf238d6e975429e61 |
mit | ['generated_from_keras_callback'] | false | huynhdoo/distilcamembert-base-finetuned-jva-missions-report This model is a fine-tuned version of [cmarkea/distilcamembert-base](https://huggingface.co/cmarkea/distilcamembert-base) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0336 - Validation Loss: 1.1880 - Train F... | 36494433542e9dc2d1ce2543b60910f2 |
mit | ['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': 3000, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta... | 656176120e7d73cd915ba61e77bf415a |
mit | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Train F1 | Epoch | |:----------:|:---------------:|:--------:|:-----:| | 0.5225 | 0.4756 | 0.3575 | 0 | | 0.4079 | 0.4294 | 0.2961 | 1 | | 0.3439 | 0.5053 | 0.2961 | 2 | | 0.2765 | 0.5106 | 0.2346 ... | 98f68b41b0f47a42a8b94150287f1630 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epoch... | d49a759b8dacce1eb46a1c56ccd7b72d |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input. The **Stable-Diffusion-v-1-4** checkpoint was initialized with the weights of the [Stable-Diffusion-v-1-2](https://steps/huggingface.co/CompVis/stable-diffusion-v-1-2-original) checkpoint and... | c8bbe86d4201605ea466211e18329eb9 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | Download the weights - [sd-v1-4.ckpt](https://huggingface.co/CompVis/stable-diffusion-v-1-4-original/resolve/main/sd-v1-4.ckpt) - [sd-v1-4-full-ema.ckpt](https://huggingface.co/CompVis/stable-diffusion-v-1-4-original/resolve/main/sd-v1-4-full-ema.ckpt) These weights are intended to be used with the original [CompVis ... | d67e6832b2f29d9116de77284648c03f |
apache-2.0 | ['translation'] | false | opus-mt-sg-es * source languages: sg * target languages: es * OPUS readme: [sg-es](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sg-es/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://... | 434950b80d96e2f41c7fc2ae4f1ae171 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 1 | c2a33585cfc778fb676ecacfb9c4f59d |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Medium Portuguese ๐ง๐ท๐ต๐น Bem-vindo ao whisper medium para transcriรงรฃo em portuguรชs ๐๐ป If you are looking to **quickly**, and **reliably**, transcribe Portuguese audio to text, you are in the right place! With a state-of-the-art [Word Error Rate](https://huggingface.co/spaces/evaluate-metric/wer) (WER) o... | c4c5d454fb272780909b7bb635370f98 |
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