license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
cc-by-sa-4.0 | [] | false | BERT base Japanese (character tokenization, whole word masking enabled) This is a [BERT](https://github.com/google-research/bert) model pretrained on texts in the Japanese language. This version of the model processes input texts with word-level tokenization based on the IPA dictionary, followed by character-level t... | 537906b13c40472db0e661b1cc5cc5f2 |
mit | [] | false | ambrose-arm-chair on Stable Diffusion This is the `<ambrose-arm-chair>` 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... | 0c786eb1f8d52bffee10b9b425c18526 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | `Shinji Watanabe/librispeech_asr_train_asr_transformer_e18_raw_bpe_sp_valid.acc.best` ♻️ Imported from https://zenodo.org/record/3966501 This model was trained by Shinji Watanabe using librispeech recipe in [espnet](https://github.com/espnet/espnet/). | b81198536292d38f25a3a4730e0e584b |
mit | [] | false | JoeMad on Stable Diffusion This is the `<joemad>` 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 y... | 4814d787651ad95a368f630aba2473d2 |
apache-2.0 | ['exbert', 'multiberts', 'multiberts-seed-2'] | false | MultiBERTs Seed 2 Checkpoint 1300k (uncased) Seed 2 intermediate checkpoint 1300k 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... | 8b80f3bf03258a1a6afffa7125865427 |
apache-2.0 | ['exbert', 'multiberts', 'multiberts-seed-2'] | 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-2-1300k') model = BertModel.from_pretrained("multiberts-seed-2-1300k") text = "Replace me by any text you'd lik... | 8ed42e60654c8cb18a78dbbd3ed259a9 |
cc-by-sa-4.0 | [] | false | How to use You can use this model for masked language modeling as follows: ```python from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("nlp-waseda/roberta-large-japanese") model = AutoModelForMaskedLM.from_pretrained("nlp-waseda/roberta-large-japanese") sentence =... | e0916c5a9841f1c0899f4dbd16b14df5 |
cc-by-sa-4.0 | [] | false | Tokenization The input text should be segmented into words by [Juman++](https://github.com/ku-nlp/jumanpp) in advance. Juman++ 2.0.0-rc3 was used for pretraining. Each word is tokenized into tokens by [sentencepiece](https://github.com/google/sentencepiece). `BertJapaneseTokenizer` now supports automatic `JumanppTok... | 2433f9a8924da985e76a8b327637c52b |
cc-by-sa-4.0 | [] | false | Training procedure This model was trained on Japanese Wikipedia (as of 20210920) and the Japanese portion of CC-100. It took two weeks using eight NVIDIA A100 GPUs. The following hyperparameters were used during pretraining: - learning_rate: 6e-5 - per_device_train_batch_size: 103 - distributed_type: multi-GPU - num... | d6aa723c94e8b0d7a18946f5b8fac533 |
apache-2.0 | ['generated_from_trainer'] | false | barthez-deft-archeologie This model is a fine-tuned version of [moussaKam/barthez](https://huggingface.co/moussaKam/barthez) on an unknown dataset. **Note**: this model is one of the preliminary experiments and it underperforms the models published in the paper (using [MBartHez](https://huggingface.co/moussaKam/mbar... | 6041be99a323db4d7af79d0870191f9b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 3.4832 | 1.0 | 108 | 2.4237 | 22.6662 | 10.009 | 19.8729 | 19.8814 | 15... | 6b9d834fef033059194a7469615a0f8c |
apache-2.0 | [] | false | OFA-Base-Caption This is the official checkpoint (adaptive to the official code instead of Huggingface Transformers) of OFA-Base finetuned on the MSCOCO Caption dataset for image captioning. Specifically, the model was first trained with cross-entropy loss and then with CIDEr optimization. For more information, plea... | fc31ca9d312f963bfd403e2cf7ad0dad |
apache-2.0 | ['vision', 'image-classification'] | false | Convolutional Vision Transformer (CvT) CvT-21 model pre-trained on ImageNet-1k at resolution 384x384. It was introduced in the paper [CvT: Introducing Convolutions to Vision Transformers](https://arxiv.org/abs/2103.15808) by Wu et al. and first released in [this repository](https://github.com/microsoft/CvT). Discla... | 8168fee575256c13a12826e7b2f06d5b |
apache-2.0 | ['vision', 'image-classification'] | false | Usage 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 AutoFeatureExtractor, CvtForImageClassification from PIL import Image import requests url = 'http://images.cocodataset.org/val2017/000000039769.jpg' image = Im... | 52cd7a8559ea37414dfaf4df127954cf |
apache-2.0 | ['multiberts', 'multiberts-seed_2', 'multiberts-seed_2-step_60k'] | false | MultiBERTs, Intermediate Checkpoint - Seed 2, Step 60k 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 r... | aa4a5aa0a285082d73cfb4b7cc90a8f0 |
apache-2.0 | ['multiberts', 'multiberts-seed_2', 'multiberts-seed_2-step_60k'] | 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_2-step_60k') model = TFBertModel.from_pretrained("google/multiber... | ec10b87d65a3d468a3375653352dcc5b |
apache-2.0 | ['exbert', 'multiberts'] | false | MultiBERTs Seed 20 (uncased) Seed 20 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/google-research/language/tree/master/language... | c3551cfba1f819bfb8f08e0e90559007 |
apache-2.0 | ['exbert', 'multiberts'] | 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-20') model = BertModel.from_pretrained("multiberts-seed-20") text = "Replace me by any text you'd like." enco... | f6783ea04a20bc9bb9f0f5d73ca425b2 |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'science'] | false | DreamBooth model for cms trained by carlosabadia. This is a Stable Diffusion model fine-tuned on the cms concept with DreamBooth. It can be used by modifying the `instance_prompt`: **cms cosmos** This model was created as part of the DreamBooth Hackathon 🔥. Visit the [organisation page](https://huggingface.co/dream... | 845c33d80f4af7ecb91fd9385251c1e0 |
mit | ['zero-shot-classification', 'nli', 'pytorch'] | false | bert-base-spanish-wwm-cased-xnli **UPDATE, 15.10.2021: Check out our new zero-shot classifiers, much more lightweight and even outperforming this one: [zero-shot SELECTRA small](https://huggingface.co/Recognai/zeroshot_selectra_small) and [zero-shot SELECTRA medium](https://huggingface.co/Recognai/zeroshot_selectra_m... | db50a61b753f483aac814bd47b8d2f44 |
mit | ['zero-shot-classification', 'nli', 'pytorch'] | false | Model description This model is a fine-tuned version of the [spanish BERT model](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased) with the Spanish portion of the XNLI dataset. You can have a look at the [training script](https://huggingface.co/Recognai/bert-base-spanish-wwm-cased-xnli/blob/main/zeroshot_... | 98c9e017c130584672c6a5bd6b1b61e5 |
mit | ['zero-shot-classification', 'nli', 'pytorch'] | false | How to use You can use this model with Hugging Face's [zero-shot-classification pipeline](https://discuss.huggingface.co/t/new-pipeline-for-zero-shot-text-classification/681): ```python from transformers import pipeline classifier = pipeline("zero-shot-classification", model="Recognai/bert-bas... | 75dfdc58cf9990b457a72fc0c81c5526 |
apache-2.0 | ['generated_from_trainer'] | false | small-mlm-imdb-target-conll2003 This model is a fine-tuned version of [muhtasham/small-mlm-wikitext](https://huggingface.co/muhtasham/small-mlm-wikitext) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1138 - Precision: 0.8869 - Recall: 0.9189 - F1: 0.9026 - Accuracy: 0.9777 | 1c3dbf02dc7ce064d1dc275aeef676c7 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.2197 | 1.14 | 500 | 0.0926 | 0.8440 | 0.8756 | 0.8595 | 0.9715 | | 0.0745 | 2.28 |... | fa4da916a2683d00ad81b9a8a7f3d2b6 |
apache-2.0 | ['generated_from_trainer'] | false | MTL-bert-base-uncased-ww 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: 2.5261 | 652c146999e59764f0abf4bee38bb774 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.2964 | 1.0 | 99 | 2.9560 | | 3.0419 | 2.0 | 198 | 2.8336 | | 2.8979 | 3.0 | 297 | 2.8009 | | 2.8815 | 4.0 | 396 | 2.7394 ... | 24616d2c77e2b617845e709fe53a0c2d |
apache-2.0 | ['automatic-speech-recognition', 'de'] | false | exp_w2v2t_de_vp-it_s946 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 (de)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that you... | e72158e23f5650ed5db7896b1725847b |
cc-by-sa-4.0 | ['serbian', 'token-classification', 'pos', 'dependency-parsing'] | false | Model Description This is a RoBERTa model in Serbian (Cyrillic and Latin) for POS-tagging and dependency-parsing, derived from [roberta-base-serbian](https://huggingface.co/KoichiYasuoka/roberta-base-serbian). Every word is tagged by [UPOS](https://universaldependencies.org/u/pos/) (Universal Part-Of-Speech). | 3e5908a50fec87bcccc956d29458eddc |
cc-by-sa-4.0 | ['serbian', 'token-classification', 'pos', 'dependency-parsing'] | false | How to Use ```py from transformers import AutoTokenizer,AutoModelForTokenClassification tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/roberta-base-serbian-upos") model=AutoModelForTokenClassification.from_pretrained("KoichiYasuoka/roberta-base-serbian-upos") ``` or ``` import esupar nlp=esupar.load("Koichi... | 5a86557195e4b6392c225dbe95616f6f |
cc-by-4.0 | ['question generation', 'answer extraction'] | false | Model Card of `lmqg/mbart-large-cc25-itquad-qg-ae` This model is fine-tuned version of [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) for question generation and answer extraction jointly on the [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) (dataset_name: default) via ... | 12bdb22e5af377f091c2739db4e408e0 |
cc-by-4.0 | ['question generation', 'answer extraction'] | false | Overview - **Language model:** [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) - **Language:** it - **Training data:** [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) (default) - **Online Demo:** [https://autoqg.net/](https://autoqg.net/) - **Repository:** [https://g... | 18acf4c102a0e422377b96d0c9bf0dbd |
cc-by-4.0 | ['question generation', 'answer extraction'] | false | model prediction question_answer_pairs = model.generate_qa("Dopo il 1971 , l' OPEC ha tardato ad adeguare i prezzi per riflettere tale deprezzamento.") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "lmqg/mbart-large-cc25-itquad-qg-ae") | fd56adfb94e618d677eb6b417a74117e |
cc-by-4.0 | ['question generation', 'answer extraction'] | false | Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/lmqg/mbart-large-cc25-itquad-qg-ae/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_itquad.default.json) | | Score | Type | Dataset ... | c08d95906d1675d09af61c7c398c077d |
cc-by-4.0 | ['question generation', 'answer extraction'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_itquad - dataset_name: default - input_types: ['paragraph_answer', 'paragraph_sentence'] - output_types: ['question', 'answer'] - prefix_types: ['qg', 'ae'] - model: facebook/mbart-large-cc25 - max_leng... | 8fb17925da7ee18cf31cf6dc0823e8c7 |
apache-2.0 | ['translation'] | false | eng-pqe * source group: English * target group: Eastern Malayo-Polynesian languages * OPUS readme: [eng-pqe](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-pqe/README.md) * model: transformer * source language(s): eng * target language(s): fij gil haw lkt mah mri nau niu rap smo tah ton... | 12216ed11b0b8a451344219f9c724683 |
apache-2.0 | ['translation'] | false | Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | Tatoeba-test.eng-fij.eng.fij | 22.1 | 0.396 | | Tatoeba-test.eng-gil.eng.gil | 41.9 | 0.673 | | Tatoeba-test.eng-haw.eng.haw | 0.6 | 0.114 | | Tatoeba-test.eng-lkt.eng.lkt | 0.5 | 0.075 | | Tatoeba-test.eng-mah.en... | 5a3cd0d2c99b26ef6a2c16ca3ed78698 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: eng-pqe - source_languages: eng - target_languages: pqe - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-pqe/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['en', 'fj', 'mi', 'ty', 'to', 'na', 'sm', 'mh', 'pqe'... | 251e0ff4cc4b73c4a7883a19437a27d7 |
apache-2.0 | ['Quality Estimation', 'monotransquest', 'DA'] | false | Using Pre-trained Models ```python import torch from transquest.algo.sentence_level.monotransquest.run_model import MonoTransQuestModel model = MonoTransQuestModel("xlmroberta", "TransQuest/monotransquest-da-si_en-wiki", num_labels=1, use_cuda=torch.cuda.is_available()) predictions, raw_outputs = model.predict([["R... | 1279c347af4e8802924845a1bb98fe58 |
other | [] | false | Rug Cleaning Fort Worth TX https://txfortworthcarpetcleaning.com/rug-cleaning.html (817) 523-1237 Carpet cleaning Fort Worth TX is nearby and able to provide you with professional cleaning services if you require an efficient and high-quality rug cleaning service.Simply contact our professionals, and your rug will rega... | cdfa5dd1c527abb213ae18d987544c6e |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'food'] | false | DreamBooth model for the food concept trained by Someman on the Someman/momo dataset. This is a Stable Diffusion model fine-tuned on the food concept with DreamBooth. It can be used by modifying the `instance_prompt`: **a photo of food momos** This model was created as part of the DreamBooth Hackathon 🔥. Visit the ... | 2645ba9f3edcbd228b32dcd83fbd073a |
apache-2.0 | ['part-of-speech', 'token-classification'] | false | XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: German 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. | 24fc51679965971094c1cc021172da33 |
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-de") model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-de") ``` | 2a8234a260405ace852a3af1f3d6ed9b |
mit | ['roberta-base', 'roberta-base-epoch_6'] | false | RoBERTa, Intermediate Checkpoint - Epoch 6 This model is part of our reimplementation of the [RoBERTa model](https://arxiv.org/abs/1907.11692), trained on Wikipedia and the Book Corpus only. We train this model for almost 100K steps, corresponding to 83 epochs. We provide the 84 checkpoints (including the randomly i... | 2cf29f754f3a6d853444e25ce1f4959b |
mit | ['generated_from_trainer'] | false | gpt2.CEBaB_confounding.food_service_positive.absa.5-class.seed_43 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: 0.8080 - Accuracy: 0.7689 - Macro-f1: 0.7651 - Weighted-macro-f1: 0.7692... | de5f6f772030adc208b611874f37719b |
apache-2.0 | ['generated_from_trainer'] | false | hf_distilbert_uncased_somm This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0481 - F1: 0.9077 | 02c59a661c2a5b442feca5d84aa57799 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 | f43de18b808681e225dcfb2cfb7daaf9 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.1032 | 1.0 | 565 | 0.0521 | 0.8929 | | 0.0432 | 2.0 | 1130 | 0.0481 | 0.9077 | | 162aa8d03c1e3ddd0e9e51a7c5e75696 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 | 7fc506f11d107a4b58a00c2d62679150 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | No log | 1.0 | 495 | 1.7971 | 26.6141 | 13.9957 | 22.3012 | 23.7509 | 20... | 0bb132bdcfeedd13c26d5ffe9c2cbbda |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-korean-v3 This model is a fine-tuned version of [Hyuk/wav2vec2-korean-v3](https://huggingface.co/Hyuk/wav2vec2-korean-v3) on the None dataset. It achieves the following results on the evaluation set: - eval_loss: 0.9860 - eval_wer: 0.3797 - eval_runtime: 56.5559 - eval_samples_per_second: 10.167 - eval_steps... | 48549860ef05ec7bc3d244d8f484164c |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sched... | 58bfddf7dd5ff97edd02feb9273f0057 |
apache-2.0 | ['generated_from_keras_callback'] | false | distilbert_oscarth_0020 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.4909 - Validation Loss: 1.4161 - Epoch: 19 | d4fe8975d04b9664181226378c083e28 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 4.1327 | 2.9983 | 0 | | 2.7813 | 2.4562 | 1 | | 2.4194 | 2.2066 | 2 | | 2.2231 | 2.0562 | 3 | | 2.0894 | 1.9450 | 4 | | 1.9905 |... | 6d5638f8de135c9c37b70a1c17f97644 |
apache-2.0 | ['automatic-speech-recognition', 'es'] | false | exp_w2v2r_es_xls-r_accent_surpeninsular-0_nortepeninsular-10_s157 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 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this... | f606974c5c26c558c7fc07bf01e9ecb2 |
apache-2.0 | [] | false | Mengzi-BERT base fin model (Chinese) Continue trained mengzi-bert-base with 20G financial news and research reports. Masked language modeling(MLM), part-of-speech(POS) tagging and sentence order prediction(SOP) are used as training task. [Mengzi: Towards Lightweight yet Ingenious Pre-trained Models for Chinese](https... | 8ab504b81b75a1cf76abb964f7d8873a |
apache-2.0 | [] | false | Usage ```python from transformers import BertTokenizer, BertModel tokenizer = BertTokenizer.from_pretrained("Langboat/mengzi-bert-base-fin") model = BertModel.from_pretrained("Langboat/mengzi-bert-base-fin") ``` | 232a5091a8dc763208594b15bea8b99e |
apache-2.0 | [] | false | Citation If you find the technical report or resource is useful, please cite the following technical report in your paper. ``` @misc{zhang2021mengzi, title={Mengzi: Towards Lightweight yet Ingenious Pre-trained Models for Chinese}, author={Zhuosheng Zhang and Hanqing Zhang and Keming Chen and Yuhang Guo a... | eaf813d317e7472ae2bed00c2d9807a9 |
creativeml-openrail-m | ['coreml', 'stable-diffusion', 'text-to-image'] | false | Openjourney + Robo-Diffusion Merge: Source(s): [CivitAI](https://civitai.com/models/1214/openjourney-robo-diffusion-merge) It's just a merge of Robo-Diffusion, Openjourney (Midjourney-V4), and SD v1.5. | 558fa30343a587b065628deb8f0f96b4 |
apache-2.0 | ['vision'] | false | ImageGPT (large-sized model) ImageGPT (iGPT) model pre-trained on ImageNet ILSVRC 2012 (14 million images, 21,843 classes) at resolution 32x32. It was introduced in the paper [Generative Pretraining from Pixels](https://cdn.openai.com/papers/Generative_Pretraining_from_Pixels_V2.pdf) by Chen et al. and first release... | f21f7470c76d357b71c2158b0c70af2c |
apache-2.0 | ['vision'] | false | How to use Here is how to use this model in PyTorch to perform unconditional image generation: ```python from transformers import ImageGPTFeatureExtractor, ImageGPTForCausalImageModeling import torch import matplotlib.pyplot as plt import numpy as np feature_extractor = ImageGPTFeatureExtractor.from_pretrained('ope... | b0eda6e695b0d4e0825ff76735b9ee6a |
apache-2.0 | ['generated_from_keras_callback'] | false | kunxiaogao/my_awesome_wnut_model This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1293 - Validation Loss: 0.2770 - Train Precision: 0.5634 - Train Recall: 0.38... | f954828325100a3553c10f3ca267bbdd |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Train Precision | Train Recall | Train F1 | Train Accuracy | Epoch | |:----------:|:---------------:|:---------------:|:------------:|:--------:|:--------------:|:-----:| | 0.3690 | 0.3318 | 0.4888 | 0.1304 | 0.2059 | 0.9299 | 0 ... | 0cd074e2a48f1715f9fbd17ebe69ff92 |
apache-2.0 | ['generated_from_trainer'] | false | tiny-mlm-glue-cola-target-glue-mrpc This model is a fine-tuned version of [muhtasham/tiny-mlm-glue-cola](https://huggingface.co/muhtasham/tiny-mlm-glue-cola) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.1611 - Accuracy: 0.7377 - F1: 0.8231 | c00ea6ab859bd23a1645d2e9b8845e9e |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.5876 | 4.35 | 500 | 0.5489 | 0.7132 | 0.8116 | | 0.4468 | 8.7 | 1000 | 0.5577 | 0.7426 | 0.8298 | | 0.2984 |... | 31c760acc9aba9a8e256b63ab5c0bf21 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-large-xls-r-300m-finetuned-hindi-common-voice-9-0 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: 0.7392 - Wer: 1.0141 | efd20229dc2d52693563ce8be150eb9b |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4.42184e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - l... | 725be5fd8070618911f29b769048ac29 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 9.2217 | 3.03 | 400 | 4.0314 | 1.0 | | 3.2902 | 6.06 | 800 | 2.1356 | 1.0001 | | 0.9858 | 9.09 | 1200 | 0.8566 | 1.0037 | |... | ec310cea1733bd2c0bc067307bdb2090 |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-cased-wikitext2 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: 6.8567 | e00aca29d961d207619bffba09bf0601 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 7.0923 | 1.0 | 2346 | 7.0511 | | 6.9047 | 2.0 | 4692 | 6.8751 | | 6.8831 | 3.0 | 7038 | 6.8942 | | cc44592b02c861f23989bd41610dfb8f |
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 an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0531 - Rouge1: 97.0969 - Rouge2: 95.8095 - Rougel: 96.7452 - Rougelsum: 96.7363 - Gen Len: 15.3151 | eee25774376e5365192a9f3027c43aba |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 1.033 | 1.0 | 519 | 0.2212 | 95.3952 | 91.6915 | 94.8024 | 94.7963 | 14... | e87b677550d3cbaa2d11040baee245ad |
mit | ['vision', 'image-to-text', 'image-captioning', 'visual-question-answering'] | false | BLIP-2, OPT-6.7b, pre-trained only BLIP-2 model, leveraging [OPT-6.7b](https://huggingface.co/facebook/opt-6.7b) (a large language model with 6.7 billion parameters). It was introduced in the paper [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://arxiv.o... | 26e16e5d79c162e09815a42896895b33 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-timit-demo-colab This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4769 - Wer: 0.4305 | 35c0a2ce3153707e479db879d77779a0 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 5.2022 | 13.89 | 500 | 2.9267 | 0.9995 | | 0.834 | 27.78 | 1000 | 0.4769 | 0.4305 | | 2208e3c7af83db22289affc03452a148 |
gpl-3.0 | ['fastai', 'image-classification'] | false | Some next steps 1. Fill out this model card with more information (template below and [documentation here](https://huggingface.co/docs/hub/model-repos))! 2. Create a demo in Gradio or Streamlit using the 🤗Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)). 3. Join our fastai community on the Hugg... | 739dc3be09f6d1e3f183bf43b497d7ed |
apache-2.0 | ['automatic-speech-recognition', '/workspace/asante/ai-light-dance_datasets/AI_Light_Dance.py', 'generated_from_trainer'] | false | ai-light-dance_singing2_ft_wav2vec2-large-xlsr-53 This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the /WORKSPACE/ASANTE/AI-LIGHT-DANCE_DATASETS/AI_LIGHT_DANCE.PY - ONSET-SINGING2 dataset. It achieves the following results on the evalua... | bf5bc7268e8983236b00289b8a495ce9 |
apache-2.0 | ['automatic-speech-recognition', '/workspace/asante/ai-light-dance_datasets/AI_Light_Dance.py', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4e-06 - train_batch_size: 10 - eval_batch_size: 10 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 160 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_s... | ede26b8ccfec3a32536c17d597aa5aef |
apache-2.0 | ['automatic-speech-recognition', '/workspace/asante/ai-light-dance_datasets/AI_Light_Dance.py', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 27.4755 | 1.0 | 112 | 23.2618 | 1.0 | | 5.5145 | 2.0 | 224 | 5.2213 | 1.0 | | 4.2211 | 3.0 | 336 | 4.1673 | 1.0 | |... | feb003119cd23f5ac621729a14afbf89 |
apache-2.0 | ['generated_from_trainer'] | false | distilgpt2-finetuned-wikitext2 This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.6432 | e4cd83a82e164a0d6825944407eaaf97 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.7607 | 1.0 | 2334 | 3.6664 | | 3.6323 | 2.0 | 4668 | 3.6461 | | 3.6075 | 3.0 | 7002 | 3.6432 | | da3e3dd1a15b41679d780d8daccb114c |
apache-2.0 | ['automatic-speech-recognition', 'es'] | false | exp_w2v2r_es_xls-r_gender_male-8_female-2_s471 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 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure t... | 3012d83d122e9ac2ff9289390f550bda |
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: 0.1630 - Accuracy: 0.9315 - F1: 0.9318 | f99ca111bd3a6d1457359add74354a23 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.2115 | 1.0 | 250 | 0.1696 | 0.93 | 0.9295 | | 0.1376 | 2.0 | 500 | 0.1630 | 0.9315 | 0.9318 | | d367c27b9f4bd34ea9fa33e53074148b |
apache-2.0 | [] | false | Overview Model included in a paper for modeling fine grained similarity between documents: **Title**: "Multi-Vector Models with Textual Guidance for Fine-Grained Scientific Document Similarity" **Authors**: Sheshera Mysore, Arman Cohan, Tom Hope **Paper**: https://arxiv.org/abs/2111.08366 **Github**: https://gith... | 940c521a018aa0cb4a64e5db3c395511 |
apache-2.0 | [] | false | Model description This model is a BERT based multi-vector model trained for fine-grained similarity of computer science papers. This model inputs the title and abstract of a paper and represents a paper with a contextual sentence vectors obtained by averaging the token representations of individual sentences - the wh... | aff3a578bbccb06eee0099cd51959bec |
apache-2.0 | [] | false | Training data The model is trained on pairs of co-cited papers with their sentences aligned by the co-citation context in a contrastive learning setup. The model is trained on 1.2 million computer science paper pairs. In training the model, negative examples for the contrastive loss are obtained as random in-batch n... | f6006b5159f95ea0e7adabe85b6d49dd |
apache-2.0 | [] | false | Intended uses & limitations This model is trained for fine-grained document similarity tasks in **computer science** scientific text using multiple vectors per document. The model allows fine grained similarity by establishing sentence-to-sentence similarity between documents. The model is most well suited to an aspe... | c32a29aa0707761b868ef0ed5a1bacb7 |
apache-2.0 | [] | false | How to use This model can be used via the `transformers` library and some additional code to compute contextual sentence vectors. View example usage in the model github repo: https://github.com/allenai/aspire | 84c44b1a0193d1670210dff7135fc75f |
apache-2.0 | [] | false | Variable and metrics This model is evaluated on information retrieval datasets with document level queries. Performance here is reported on CSFCube (computer science/English). This is detailed on [github](https://github.com/allenai/aspire) and in our [paper](https://arxiv.org/abs/2111.08366). CSFCube presents a finer-... | 1b5af73455b592128166eb49e42264db |
apache-2.0 | [] | false | Evaluation results The released model `aspire-contextualsentence-singlem-compsci` is compared against `allenai/specter`, a bi-encoder baseline and `all-mpnet-base-v2` a strong non-contextual sentence-bert baseline model trained on ~1 billion training examples. `aspire-contextualsentence-singlem-compsci`<sup>*</sup> i... | 885207cf007622994004013fc02c954f |
apache-2.0 | ['stanza', 'token-classification'] | false | Stanza model for Norwegian_Nynorsk (nn) 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 we... | 6c3859e74617ae2542cea6f1920f8cb0 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-large-xls-r-300m-ar-2 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: 0.4764 - Wer: 0.3073 | 3ec738cc14d1f2d84e219e42b8bf420f |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 32 - eval_batch_size: 16 - 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_sch... | 0e65b70043003f8f6fddce45ba230ab3 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 1.0851 | 1.18 | 400 | 0.5614 | 0.4888 | | 0.691 | 2.35 | 800 | 0.6557 | 0.5558 | | 0.6128 | 3.53 | 1200 | 0.5852 | 0.507... | 03662464e64de3eb4a4b8773e75dbaef |
apache-2.0 | ['generated_from_trainer'] | false | sagemaker-distilbert-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: 0.2421 - Accuracy: 0.912 | ddf0222045b5d8238bd176207b319388 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.9278 | 1.0 | 500 | 0.2421 | 0.912 | | 5243967dd8c952000a577320cfbfb68c |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 | ec11fae477b9e2732aa4c9fdd033a89c |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_sa_GLUE_Experiment_data_aug_qqp_192 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE QQP dataset. It achieves the following results on the evaluation set: - Loss: 0.4878 - Accuracy: 0.7887 - F1: 0.7232 - Combined Score: 0.7560 | eb39203bf32d66eb2828392c0cada6f7 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score | |:-------------:|:-----:|:------:|:---------------:|:--------:|:------:|:--------------:| | 0.4161 | 1.0 | 29671 | 0.4878 | 0.7887 | 0.7232 | 0.7560 | | 0.2684 | 2.0 | 59342... | 74fbab039ff23296adf87e370d23bad6 |
apache-2.0 | ['generated_from_trainer'] | false | t5-small-finetuned-text2log-finetuned-nl-to-fol This model is a fine-tuned version of [mrm8488/t5-small-finetuned-text2log](https://huggingface.co/mrm8488/t5-small-finetuned-text2log) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0267 - Bleu: 36.0754 - Gen Len: 18.6964 | e06d2eda39065827a712113523a0c59f |
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