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 | ['automatic-speech-recognition', 'pl'] | false | exp_w2v2t_pl_vp-es_s438 Fine-tuned [facebook/wav2vec2-large-es-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-es-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (pl)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that you... | baa83b9cd8600b72a0343995a2d2dd01 |
apache-2.0 | ['generated_from_trainer'] | false | week5-eng-distilbert-base-multilingual-cased-finetuned This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0855 - Precision: 0.2813 - Recall: 0.2949... | a467c41ad52d08b1c22f9db2be8698b3 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.1071 | 1.0 | 924 | 0.0911 | 0.2472 | 0.0444 | 0.0753 | 0.9755 | | 0.07 | 2.0 |... | bc308f53de22ea1dc58cf91cc04b8a59 |
apache-2.0 | ['generated_from_trainer'] | false | all-roberta-large-v1-travel-1-16-5 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: 2.1384 - Accuracy: 0.4289 | c3c90a03744e426aae26a21fdb7dc66d |
gpl-3.0 | ['pytorch', 'token-classification', 'bert', 'zh'] | false | CKIP BERT Tiny Chinese This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition). 這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。 | 699920ba5e77f114d2735a29a85d5a7c |
gpl-3.0 | ['pytorch', 'token-classification', 'bert', 'zh'] | false | Usage Please use BertTokenizerFast as tokenizer instead of AutoTokenizer. 請使用 BertTokenizerFast 而非 AutoTokenizer。 ``` from transformers import ( BertTokenizerFast, AutoModel, ) tokenizer = BertTokenizerFast.from_pretrained('bert-base-chinese') model = AutoModel.from_pretrained('ckiplab/bert-tiny-chinese-pos') ... | 307d8e19c555176f7d59951d2a78abc7 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | TaylorSwift Dreambooth model trained by taytay4eva with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook using the StableDiffusionv1.5 model CREATOR NOTE 1: The keyword for this model is <b>taySwift</b> CREATOR NOTE 2: ... | ae66692a44ccb2c6b1ae1efaa52bcdeb |
apache-2.0 | ['icefall', 'k2', 'transducer', 'aishell', 'ASR', 'stateless transducer', 'PyTorch'] | false | Introduction This repo contains pre-trained model using <https://github.com/k2-fsa/icefall/pull/219>. It is trained on [AIShell](https://www.openslr.org/33/) dataset using modified transducer from [optimized_transducer](https://github.com/csukuangfj/optimized_transducer). | 2daaddb913cb1a866a136a6d3cf23d03 |
apache-2.0 | ['icefall', 'k2', 'transducer', 'aishell', 'ASR', 'stateless transducer', 'PyTorch'] | false | How to clone this repo ``` sudo apt-get install git-lfs git clone https://huggingface.co/csukuangfj/icefall-aishell-transducer-stateless-modified-2022-03-01 cd icefall-aishell-transducer-stateless-modified-2022-03-01 git lfs pull ``` **Catuion**: You have to run `git lfs pull`. Otherwise, you will be SAD later. The... | 6b4d55e675402fdb1d89d26abc2a23e9 |
apache-2.0 | ['icefall', 'k2', 'transducer', 'aishell', 'ASR', 'stateless transducer', 'PyTorch'] | false | L232>. In short, the encoder is a Conformer model with 8 heads, 12 encoder layers, 512-dim attention, 2048-dim feedforward; the decoder contains a 512-dim embedding layer and a Conv1d with kernel size 2. The decoder architecture is modified from [Rnn-Transducer with Stateless Prediction Network](https://ieeexplore.i... | eb57088ddfba409628987efcdd82f8aa |
apache-2.0 | ['icefall', 'k2', 'transducer', 'aishell', 'ASR', 'stateless transducer', 'PyTorch'] | false | Description This repo provides pre-trained transducer Conformer model for the AIShell dataset using [icefall][icefall]. There are no RNNs in the decoder. The decoder is stateless and contains only an embedding layer and a Conv1d. The commands for training are: ```bash cd egs/aishell/ASR ./prepare.sh --stop-stage 6 ... | 960de108b3f1ba856751d1edbbc61628 |
apache-2.0 | ['icefall', 'k2', 'transducer', 'aishell', 'ASR', 'stateless transducer', 'PyTorch'] | false | greedy search for epoch in 64; do for avg in 33; do ./transducer_stateless_modified-2/decode.py \ --epoch $epoch \ --avg $avg \ --exp-dir transducer_stateless_modified/exp-4 \ --max-duration 100 \ --context-size 2 \ --decoding-method greedy_search \ --max-sym-per-frame 1 done done | c5e044eb2980a54ea26cf5e4022bba74 |
apache-2.0 | ['icefall', 'k2', 'transducer', 'aishell', 'ASR', 'stateless transducer', 'PyTorch'] | false | modified beam search for epoch in 64; do for avg in 33; do ./transducer_stateless_modified/decode.py \ --epoch $epoch \ --avg $avg \ --exp-dir transducer_stateless_modified/exp-4 \ --max-duration 100 \ --context-size 2 \ --decoding-method modified_beam_search \ --beam-size 4 done do... | 537c91a75d428071bc29ea5d6f183db0 |
apache-2.0 | ['icefall', 'k2', 'transducer', 'aishell', 'ASR', 'stateless transducer', 'PyTorch'] | false | File description - [log][log], this directory contains the decoding log and decoding results - [test_wavs][test_wavs], this directory contains wave files for testing the pre-trained model - [data][data], this directory contains files generated by [prepare.sh][prepare] - [exp][exp], this directory contains only one fi... | 3a42c2ff8cbff2569100847ffe45130f |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-mrpc This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.4044 - Accuracy: 0.8480 - F1: 0.8942 | f29e7be77fd8127d8636195e1a44a598 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 230 | 0.3830 | 0.8162 | 0.8673 | | No log | 2.0 | 460 | 0.3957 | 0.8456 | 0.8952 | | 0.4307 |... | 41be18458636c24206d31ecb645642e9 |
apache-2.0 | ['generated_from_keras_callback'] | false | Rocketknight1/distilbert-base-uncased-finetuned-squad 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: 1.5124 - Train End Logits Accuracy: 0.6041 - Train Start L... | 9786546e656e96ffe7cf29397b26513e |
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 | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:----------... | 51942e443eadeac7704b8a8f35dbbe51 |
apache-2.0 | ['translation'] | false | opus-mt-om-en * source languages: om * target languages: en * OPUS readme: [om-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/om-en/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://... | 648f04d0fa294fbd7b7b5cca4abd7a32 |
apache-2.0 | ['translation'] | false | opus-mt-sg-sv * source languages: sg * target languages: sv * OPUS readme: [sg-sv](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sg-sv/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-21.zip](https://... | 331985e0aaf21bd4a12c9aedf3698a5a |
cc-by-4.0 | ['answer extraction'] | false | Model Card of `lmqg/t5-base-squad-ae` This model is fine-tuned version of [t5-base](https://huggingface.co/t5-base) for answer extraction on the [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation). | 4ac918997e81f99db31bd917254244e0 |
cc-by-4.0 | ['answer extraction'] | false | model prediction answers = model.generate_a("William Turner was an English painter who specialised in watercolour landscapes") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "lmqg/t5-base-squad-ae") output = pipe("extract answers: <hl> Beyonce further e... | e0338fd1503dda1d57036e4c3b3d550e |
cc-by-4.0 | ['answer extraction'] | false | Evaluation - ***Metric (Answer Extraction)***: [raw metric file](https://huggingface.co/lmqg/t5-base-squad-ae/raw/main/eval/metric.first.answer.paragraph_sentence.answer.lmqg_qg_squad.default.json) | | Score | Type | Dataset | |:---------... | 126f00995c04f301d9c582e269de6d82 |
cc-by-4.0 | ['answer extraction'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_squad - dataset_name: default - input_types: ['paragraph_sentence'] - output_types: ['answer'] - prefix_types: ['ae'] - model: t5-base - max_length: 512 - max_length_output: 32 - epoch: 8 - batch: 16... | 038896e1af009482deff85ae3a3a2c56 |
apache-2.0 | ['automatic-speech-recognition', 'gary109/AI_Light_Dance', 'generated_from_trainer'] | false | ai-light-dance_stepmania_ft_wav2vec2-large-xlsr-53-v1 This model is a fine-tuned version of [gary109/ai-light-dance_stepmania_ft_wav2vec2-large-xlsr-53](https://huggingface.co/gary109/ai-light-dance_stepmania_ft_wav2vec2-large-xlsr-53) on the GARY109/AI_LIGHT_DANCE - ONSET-STEPMANIA2 dataset. It achieves the followin... | d6f3b63a1df80fdda230c1228cad75db |
apache-2.0 | ['automatic-speech-recognition', 'gary109/AI_Light_Dance', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4e-05 - 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... | a414e5dbd352a4f3c8dc53ce29f22091 |
apache-2.0 | ['automatic-speech-recognition', 'gary109/AI_Light_Dance', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 1.1632 | 1.0 | 150 | 1.2007 | 0.9875 | | 1.1615 | 2.0 | 300 | 1.1912 | 0.9875 | | 1.1487 | 3.0 | 450 | 1.1942 | 0.9875 | |... | b3e8db277d7c917023961b5d833041d1 |
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: 0.2161 - Accuracy: 0.923 - F1: 0.9227 | dedcdaf5b127b762a12b8c36eb48e6e3 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8365 | 1.0 | 250 | 0.3102 | 0.9075 | 0.9051 | | 0.246 | 2.0 | 500 | 0.2161 | 0.923 | 0.9227 | | 1cb8efc6d2ad723a0e538ad18c4b5c59 |
mit | ['generated_from_trainer'] | false | roberta-large-mnli-misogyny-sexism-4tweets-3e-05-0.05-singledt This model is a fine-tuned version of [roberta-large-mnli](https://huggingface.co/roberta-large-mnli) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.4013 - Accuracy: 0.703 - F1: 0.7003 - Precision: 0.6535 - Recall:... | b0dc1d95444c8ff4d68ceb981694b25b |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Mae | Tn | Fp | Fn | Tp | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:-----:|:---:|:---:|:---:|:---:| | 0.4806 | 1.0 | 250 | 0.6819 | 0.... | d7e479a3417728b73c52bbc899bc0fb5 |
mit | ['generated_from_trainer'] | false | roberta-fine-sentiment-hineng-concat This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.1126 - Accuracy: 0.8669 - Precision: 0.8667 - Recall: 0.8669 - F1: 0.8668 | 0c5e613bdb6ee98c5b58d608d1cde5cb |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:| | 0.5814 | 1.0 | 4293 | 0.6920 | 0.8249 | 0.8304 | 0.8249 | 0.8257 | | 0.5169 | 2.0 ... | 5b42fd03c699a26f74cb583f1598ea7c |
mit | [] | false | BEE on Stable Diffusion This is the `<b-e-e>` 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 your ... | 4c0ecbcbe762545e4a75c0000aa0f61f |
apache-2.0 | ['stanza', 'token-classification'] | false | Stanza model for Sindhi (sd) 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](http... | f0961a8754913b8dc8c95b155298e452 |
apache-2.0 | ['summarization', 't5', 'seq2seq'] | false | T5 v1.1 Base finetuned for CNN news summarization in Dutch 🇳🇱 This model is [t5-v1.1-base-dutch-cased](https://huggingface.co/yhavinga/t5-v1.1-base-dutch-cased) finetuned on [CNN Dailymail NL](https://huggingface.co/datasets/ml6team/cnn_dailymail_nl) For a demo of the Dutch CNN summarization models, head over to t... | c942e1757a1db5579190a19a33374f28 |
apache-2.0 | ['summarization', 't5', 'seq2seq'] | false | Tokenizer * SentencePiece tokenizer trained from scratch for Dutch on mC4 nl cleaned with scripts from the Huggingface Transformers [Flax examples](https://github.com/huggingface/transformers/tree/master/examples/flax/language-modeling). | a51e329eb0e7bd76ecee0fe512dc45a6 |
apache-2.0 | ['summarization', 't5', 'seq2seq'] | false | Dataset All models listed below are trained on of the `full` configuration (39B tokens) of [cleaned Dutch mC4](https://huggingface.co/datasets/yhavinga/mc4_nl_cleaned), which is the original mC4, except * Documents that contained words from a selection of the Dutch and English [List of Dirty Naught Obscene and Oth... | fa8f0bd99cdffe4e76a7af3915148396 |
apache-2.0 | ['summarization', 't5', 'seq2seq'] | false | Models TL;DR: [yhavinga/t5-v1.1-base-dutch-cased](https://huggingface.co/yhavinga/t5-v1.1-base-dutch-cased) is the best model. * `yhavinga/t5-base-dutch` is a re-training of the Dutch T5 base v1.0 model trained during the summer 2021 Flax/Jax community week. Accuracy was improved from 0.64 to 0.70. * The two T5 v1... | c744a082e10a19d0d77f3fa2015bf679 |
apache-2.0 | ['summarization', 't5', 'seq2seq'] | false | Acknowledgements This project would not have been possible without compute generously provided by Google through the [TPU Research Cloud](https://sites.research.google/trc/). The HuggingFace 🤗 ecosystem was also instrumental in many, if not all parts of the training. The following repositories where helpful in setti... | 2220c2a86a6a721334f7d80193e88634 |
apache-2.0 | ['whisper-event'] | false | Whisper Telugu Medium This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the Telugu data available from multiple publicly available ASR corpuses. It has been fine-tuned as a part of the Whisper fine-tuning sprint. | 84f0a084dc456e92b29b2d3bfb7356fa |
apache-2.0 | ['whisper-event'] | false | Training and evaluation data at Speech Lab, IITM Training Data: CSTD IIIT-H ASR Corpus, ULCA ASR Corpus, Shrutilipi ASR Corpus, Microsoft Research Telugu Corpus (Train+Dev), Babel ASR Corpus, Google/Fleurs (Train+Dev) set. Evaluation Data: Babel Test, Microsoft Research Telugu Corpus Test, Google/Fleurs Test set, Ope... | 190d39dce6500e5c092cf0489e6c635a |
apache-2.0 | ['whisper-event'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 24 - eval_batch_size: 48 - seed: 22 - optimizer: adamw_bnb_8bit - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 15000 - training_steps: 35808 (terminated upon convergence. Initially se... | 8dd11e32570b66e0ce5ae7447340eddb |
mit | ['qmsum-summarization', 'generated_from_trainer'] | false | bart-large-cnn-finetuned-qmsum-2-4 This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on the None dataset. It achieves the following results on the evaluation set: - Loss: 4.0277 - Rouge1: 0.3053 - Rouge2: 0.0660 - Rougel: 0.1903 - Rougelsum: 0.2598 | 3bb5ce4454926f7174c24d535ee271d5 |
mit | ['qmsum-summarization', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5.6e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 | 3005288a5ee626c08f1995a42eccf64a |
mit | ['qmsum-summarization', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:| | 3.3773 | 1.0 | 629 | 3.2522 | 0.2964 | 0.0713 | 0.1958 | 0.2593 | | 2.3656 | 2.0 | 1258 ... | fb8543e000c6efde8fd83e7e0f6f415a |
apache-2.0 | ['translation'] | false | opus-mt-en-gaa * source languages: en * target languages: gaa * OPUS readme: [en-gaa](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-gaa/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-08.zip](http... | 66b8d52fb3b7582474984b2f40c52002 |
creativeml-openrail-m | [] | false | here is a dreambooth from a zp92 by margret stalizburg. prompt keyword: "margret_stalizburg or margretstalizburg" (ie, no text on here "margret stalizburg" only what is written on it in the text, you can try the prompt if you like but normally only "margret_stalizburg" prompt works.) I trained it on about 82 images ... | 569df45fd2e23fd334a4617e363f23c3 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-de This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.1348 - F1: 0.8599 | 6a8ffb7efd5f52ecc50e55b7fd1710c8 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2533 | 1.0 | 525 | 0.1725 | 0.8205 | | 0.1293 | 2.0 | 1050 | 0.1429 | 0.8424 | | 0.0831 | 3.0 | 1575 | 0.1348 | 0.8599 | ... | 2e29aa20e381f5696e315507dcaa429a |
apache-2.0 | ['translation'] | false | opus-mt-fr-mh * source languages: fr * target languages: mh * OPUS readme: [fr-mh](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fr-mh/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-20.zip](https://... | 55ffd4b286ba19d0c2a2601ae3e1bc75 |
apache-2.0 | ['generated_from_trainer'] | false | all-roberta-large-v1-banking-12-16-5 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: 2.7470 - Accuracy: 0.0756 | 8fa5961a8fc9d9f70cb82ba7e3cf9e2e |
apache-2.0 | ['generated_from_trainer'] | false | xlsr-53-bemba-5hrs This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3414 - Wer: 0.4867 | 97b49ec35b4d562ef2792e36d99e88ca |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.2701 | 2.16 | 400 | 0.4047 | 0.6230 | | 0.488 | 4.32 | 800 | 0.3002 | 0.4917 | | 0.2807 | 6.49 | 1200 | 0.3342 | 0.4802 | |... | 3c994cf80da4ecf9fc873ef4162a310d |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'Avatar', 'Avatar The Way of Water', 'film', 'James Cameron'] | false | <center><img src="https://huggingface.co/riccardogiorato/avatar-diffusion/resolve/main/assets/avatartwow.png" width="512" height="512"/></center>  | bbda68f3760405896d1de081fbc2778b |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'Avatar', 'Avatar The Way of Water', 'film', 'James Cameron'] | false | Avatar Diffusion An AI model that generates artwork with Avatar style! Based of a finetuned Stable Diffusion V1.5, trained in Dreambooth with more than 50 images from the latest trailer Avatar: The Way of Water. By [riccardogiorato](https://twitter.com/riccardogiorato) > **Note**: To get the Avatar styles, use the... | fa9cfbdc493eae591b7986ad90f162e8 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'Avatar', 'Avatar The Way of Water', 'film', 'James Cameron'] | false | 🧨 Diffusers This model can be used just like any other Stable Diffusion model. For more information, please have a look at the [Stable Diffusion](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion). You can also export the model to [ONNX](https://huggingface.co/docs/diffusers/optimization/onnx), [... | a2a2a9653c7acd69230cfec0232164fc |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'Avatar', 'Avatar The Way of Water', 'film', 'James Cameron'] | false | License This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies: 1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content 2. The authors claim no rights on the outpu... | 7a4baffd71f22b5e830df11a9f8d0099 |
apache-2.0 | ['translation'] | false | opus-mt-en-ber * source languages: en * target languages: ber * OPUS readme: [en-ber](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-ber/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2019-12-18.zip](http... | c617f25bff6def9120db65a740e0587c |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | sentence-transformers/paraphrase-multilingual-mpnet-base-v2 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. | d9285eff546edace3715e847f346d504 |
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... | 77555ddb401d6210b663cf03446a2664 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/paraphrase-multilingual-mpnet-base-v2') model = AutoModel.from_pretrained('sentence-transformers/paraphrase-multilingual-mpnet-base-v2') | 8b476bbe4a5c70c4770001a17b08be00 |
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-multilingual-mpnet-base-v2) | 737bcaab8cb52f5a1982d93725ea62b5 |
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: 0.2239 - Accuracy: 0.923 - F1: 0.9233 | 0aa35925a81428209e79cfa328cd5293 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8359 | 1.0 | 250 | 0.3198 | 0.9085 | 0.9057 | | 0.2491 | 2.0 | 500 | 0.2239 | 0.923 | 0.9233 | | 7f73c26e42b1b39c88f89e6729f50b38 |
bsd-3-clause | ['image-text-matching'] | false | BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation Model card for BLIP trained on image-text matching - base architecture (with ViT base backbone) trained on COCO dataset. | ![BLIP.gif](https://s3.amazonaws.com/moonup/production/uploads/1670928184033-62441d1d9fde... | 01c23a5dee6fb4367fc61392fd986f0f |
bsd-3-clause | ['image-text-matching'] | false | Running the model on CPU <details> <summary> Click to expand </summary> ```python import requests from PIL import Image from transformers import BlipProcessor, BlipForImageTextRetrieval processor = BlipProcessor.from_pretrained("Salesforce/blip-itm-base-coco") model = BlipForImageTextRetrieval.from_pretrained("Sale... | a96b370b1a64c207ae7632a48cc0824c |
bsd-3-clause | ['image-text-matching'] | false | In full precision <details> <summary> Click to expand </summary> ```python import requests from PIL import Image from transformers import BlipProcessor, BlipForImageTextRetrieval processor = BlipProcessor.from_pretrained("Salesforce/blip-itm-base-coco") model = BlipForImageTextRetrieval.from_pretrained("Salesforce... | 505c01d32e8fbf136c9b205c4f7e821c |
bsd-3-clause | ['image-text-matching'] | false | In half precision (`float16`) <details> <summary> Click to expand </summary> ```python import torch import requests from PIL import Image from transformers import BlipProcessor, BlipForImageTextRetrieval processor = BlipProcessor.from_pretrained("Salesforce/blip-itm-base-coco") model = BlipForImageTextRetrieval.fro... | 99fbfe234f8e2f1aae369fafacb1149d |
apache-2.0 | [] | false | Model description Macaw (<b>M</b>ulti-<b>a</b>ngle <b>c</b>(q)uestion <b>a</b>ns<b>w</b>ering) is a ready-to-use model capable of general question answering, showing robustness outside the domains it was trained on. It has been trained in "multi-angle" fashion, which means it can handle a flexible set of input and... | 4d047823f65f6f4813e2c442ae729fdf |
mit | ['text-classification', 'generated_from_trainer'] | false | deberta-v3-xsmall-with-biblio-context-frozenlm-finetuned-review_classifier This model is a fine-tuned version of [microsoft/deberta-v3-xsmall](https://huggingface.co/microsoft/deberta-v3-xsmall) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3109 - Accuracy: 0.9066 - F1: 0.0... | 8e17254036fe1f99ccb554928fcab22c |
mit | ['text-classification', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Recall | Precision | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:------:|:---------:| | 0.2938 | 1.0 | 6667 | 0.3103 | 0.9070 | 0.0221 | 0.0112 | 0.7636 | | 0.2851 | 2.0 ... | 471255877d4923bf6eeff195c4d44df1 |
cc-by-sa-4.0 | ['text-generation', 'transformers', 'pytorch', 'gpt2'] | false | Introduction GPT2-small-czech-cs is a first experimental model for Czech language based on the GPT-2 small model. It was trained on Czech Wikipedia using **Transfer Learning and Fine-tuning techniques** in about over a weekend on one GPU NVIDIA GTX 1080ti and with about 1GB of training data (cswiki). A training serve... | 60619d64205077b01875a577277823be |
cc-by-sa-4.0 | ['text-generation', 'transformers', 'pytorch', 'gpt2'] | false | model output outputs = model(**inp_tokens, labels=inp_tokens["input_ids"]) loss, logits = outputs[:2] predicted_index = torch.argmax(logits[0, -1, :]).item() predicted_text = tokenizer.decode([predicted_index]) | f70facf760b60b0c68d189a965668e29 |
cc-by-sa-4.0 | ['text-generation', 'transformers', 'pytorch', 'gpt2'] | false | if you need reproducibility sample_outputs = model.generate(encoded, do_sample=True, max_length=encoded.size()[1]+20, no_repeat_ngram_size=2, top_p=0.95, top_k=50, temperature=0.65, num_return_sequences=3) for i, sample_output in enumerate(sample_outputs): print("{}: {}\n".format... | a8b3c8f03a9c13e4a87008a1fb210c6b |
cc-by-sa-4.0 | ['text-generation', 'transformers', 'pytorch', 'gpt2'] | false | Limitations and bias The training data used for this model come from Czech Wikipedia dump. We know it contains a lot of unfiltered content from the internet, which is far from neutral. As the openAI team themselves point out in their model card: > Because large-scale language models like GPT-2 do not distinguish fac... | f4857db3d7aea8808f1e277c3e008aee |
cc-by-sa-4.0 | ['text-generation', 'transformers', 'pytorch', 'gpt2'] | false | Author Czech GPT-2 small was trained and evaluated by [Jiri Spitalsky](https://www.linkedin.com/in/jiri-spitalsky-09400a2) thanks to the computing power of the GPUs and other hardware generously provided by [ONYX engineering, spol. s r.o.](http://www.onyx.cz/). | 34fb9c92b1ef424630c99020c1230e19 |
cc-by-sa-4.0 | ['text-generation', 'transformers', 'pytorch', 'gpt2'] | false | Citation My special thanks go to Pierre Guillou for his work **GPorTuguese-2 (Portuguese GPT-2 small): a Language Model for Portuguese text generation (and more NLP tasks...)**, my work would not be possible without it. | 332c347e504fa09cb1df510cc9e6328f |
['mit'] | ['paraphrase-generation', 'multilingual', 'nlp', 'indicnlp'] | false | MultiIndicParaphraseGeneration
This repository contains the [IndicBART](https://huggingface.co/ai4bharat/IndicBART) checkpoint finetuned on the 11 languages of [IndicParaphrase](https://huggingface.co/datasets/ai4bharat/IndicParaphrase) dataset. For finetuning details,
see the [paper](https://arxiv.org/abs/2203.05... | 951863012bc50e029b76648992360cbe |
['mit'] | ['paraphrase-generation', 'multilingual', 'nlp', 'indicnlp'] | false | Using this model in `transformers`
```
from transformers import MBartForConditionalGeneration, AutoModelForSeq2SeqLM
from transformers import AlbertTokenizer, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("ai4bharat/MultiIndicParaphraseGeneration", do_lower_case=False, use_fast=False, keep_accents=True... | 876196ae0d6a41c58751a5ababa731d1 |
['mit'] | ['paraphrase-generation', 'multilingual', 'nlp', 'indicnlp'] | false | Or use tokenizer = AlbertTokenizer.from_pretrained("ai4bharat/MultiIndicParaphraseGeneration", do_lower_case=False, use_fast=False, keep_accents=True)
model = AutoModelForSeq2SeqLM.from_pretrained("ai4bharat/MultiIndicParaphraseGeneration")
| 97c41c26f16a44802f13fc2b547a1946 |
['mit'] | ['paraphrase-generation', 'multilingual', 'nlp', 'indicnlp'] | false | Note:
If you wish to use any language written in a non-Devanagari script, then you should first convert it to Devanagari using the <a href="https://github.com/anoopkunchukuttan/indic_nlp_library">Indic NLP Library</a>. After you get the output, you should convert it back into the original script.
| d0bc6d89d6659ec5d0f75848c7cc28d0 |
['mit'] | ['paraphrase-generation', 'multilingual', 'nlp', 'indicnlp'] | false | Benchmarks
Scores on the `IndicParaphrase` test sets are as follows:
Language | BLEU / Self-BLEU / iBLEU
---------|----------------------------
as | 1.66 / 2.06 / 0.54
bn | 11.57 / 1.69 / 7.59
gu | 22.10 / 2.76 / 14.64
hi | 27.29 / 2.87 / 18.24
kn | 15.40 / 2.98 / 9.89
ml | 10.57 / 1.70 / 6.89
mr | 20.38... | 4de21878f1157e2b6bf724b133c2013d |
apache-2.0 | ['translation'] | false | zls-zls * source group: South Slavic languages * target group: South Slavic languages * OPUS readme: [zls-zls](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/zls-zls/README.md) * model: transformer * source language(s): bul mkd srp_Cyrl * target language(s): bul mkd srp_Cyrl * model: transf... | 7ed2339b360cd78d27095dce4064f956 |
apache-2.0 | ['translation'] | false | Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | Tatoeba-test.bul-hbs.bul.hbs | 19.3 | 0.514 | | Tatoeba-test.bul-mkd.bul.mkd | 31.9 | 0.669 | | Tatoeba-test.hbs-bul.hbs.bul | 18.0 | 0.636 | | Tatoeba-test.hbs-mkd.hbs.mkd | 19.4 | 0.322 | | Tatoeba-test.mkd-bul.... | 22da1e1f27cb08db03baa2796b2b666e |
apache-2.0 | ['translation'] | false | System Info: - hf_name: zls-zls - source_languages: zls - target_languages: zls - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/zls-zls/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['hr', 'mk', 'bg', 'sl', 'zls'] - src_constituents: {... | 615e5ad4173c99a82aaded11c96fa2b2 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | `pyf98/aidatatang_200zh_e_branchformer` This model was trained by Yifan Peng using aidatatang_200zh recipe in [espnet](https://github.com/espnet/espnet/). References: - [E-Branchformer: Branchformer with Enhanced merging for speech recognition (SLT 2022)](https://arxiv.org/abs/2210.00077) - [Branchformer: Parallel M... | da5a259c995527ae69e2173b91890081 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Demo: How to use in ESPnet2 Follow the [ESPnet installation instructions](https://espnet.github.io/espnet/installation.html) if you haven't done that already. ```bash cd espnet git checkout 7a203d55543df02f0369d5608cd6f3033119a135 pip install -e . cd egs2/aidatatang_200zh/asr1 ./run.sh --skip_data_prep false --skip_... | f972136782c2392dadb9f8b6a9f947e4 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Environments - date: `Mon Dec 26 19:46:01 EST 2022` - python version: `3.9.15 (main, Nov 24 2022, 14:31:59) [GCC 11.2.0]` - espnet version: `espnet 202211` - pytorch version: `pytorch 1.12.1` - Git hash: `7a203d55543df02f0369d5608cd6f3033119a135` - Commit date: `Fri Dec 23 00:58:49 2022 +0000` | 6d3b0047e204aaa91caaed9bd6d0595c |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | WER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_lm_lm_train_lm_transformer_zh_char_valid.loss.ave_asr_model_valid.acc.ave/dev|24216|24216|81.6|18.4|0.0|0.0|18.4|18.4| |decode_asr_lm_lm_train_lm_transformer_zh_char_valid.loss.ave_asr_model_valid.acc.ave/test|48144|481... | 875bed65aba59cb51980d944c2c88c3e |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | CER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_lm_lm_train_lm_transformer_zh_char_valid.loss.ave_asr_model_valid.acc.ave/dev|24216|234524|96.6|3.0|0.4|0.1|3.6|18.4| |decode_asr_lm_lm_train_lm_transformer_zh_char_valid.loss.ave_asr_model_valid.acc.ave/test|48144|4689... | 88e78bf61037d3192383078cd8b56fdc |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | ASR config <details><summary>expand</summary> ``` config: conf/train_asr_e_branchformer_linear1024.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/asr_train_asr_e_branchformer_linear1024_raw_zh_char_sp ngpu: 1 seed: 0 num_workers: 4 num_att_plot: 3 dist_backend: nccl d... | 7c0467880f7bf0b4dab7f17b9cafa54e |
cc-by-sa-4.0 | ['japanese', 'wikipedia', 'cc100', 'oscar', 'pos', 'dependency-parsing'] | false | Model Description This is a DeBERTa(V2) model pretrained on Japanese Wikipedia, CC-100, and OSCAR texts for POS-tagging and dependency-parsing (using `goeswith` for subwords), derived from [deberta-v2-base-japanese](https://huggingface.co/ku-nlp/deberta-v2-base-japanese). | 990d1c3cb8da14ec2809a3e504566209 |
cc-by-sa-4.0 | ['japanese', 'wikipedia', 'cc100', 'oscar', 'pos', 'dependency-parsing'] | false | How to Use ``` from transformers import pipeline nlp=pipeline("universal-dependencies","KoichiYasuoka/deberta-base-japanese-juman-ud-goeswith",trust_remote_code=True,aggregation_strategy="simple") print(nlp("全学年にわたって小学校の国語の教科書に挿し絵が用いられている")) ``` [fugashi](https://pypi.org/project/fugashi) is required. | affa94acf3972b8bd07e548f383a9089 |
mit | ['ts', 'fill-mask', 'pytorch', 'roberta', 'masked-lm'] | false | How to use ```python from transformers import AutoTokenizer, AutoModelWithLMHead tokenizer = AutoTokenizer.from_pretrained("jannesg/takalane_tso_roberta") model = AutoModelWithLMHead.from_pretrained("jannesg/takalane_tso_roberta") ``` | be87cf6ba63cbc578fd3018c7b53b966 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:| | No log | 1.0 | 136 | 1.1454 | 14.2319 | 17.8329 | | 9edd92e4a839eb799e50387ecab3f50d |
apache-2.0 | [] | false | !/usr/bin/env bash set -x K2_ROOT=/path/to/k2 ICEFALL=/path/to/icefall export PYTHONPATH=$K2_ROOT/k2/python:$PYTHONPATH export PYTHONPATH=$K2_ROOT/build/lib:$PYTHONPATH export PYTHONPATH=$ICEFALL:$PYTHONPATH export CUDA_VISIBLE_DEVICES="0,1,2,3" ./pruned_transducer_stateless4/train.py \ --exp-dir pruned_transduc... | 524e56d9c58b25f7d4cd836dbb6759ae |
apache-2.0 | [] | false | !/usr/bin/env bash set... | 7bb6d488c15b04d312443ddfb7dff6ca |
apache-2.0 | [] | false | !/usr/bin/env bash set... | b0bac60d402cc2d3b37ac7225ee9cb9f |
apache-2.0 | [] | false | chunk_size=8 left_context=64 ... | e790b15fa00aa25e41a1b7c94154bcbc |
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