license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
apache-2.0 | ['generated_from_trainer'] | false | distilBERT_token_itr0_0.0001_editorials_01_03_2022-15_20_12 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: 0.1290 - Precision: 0.0637 - Recall: 0.0080 - F1: 0.0141 - Accuracy: 0.9... | fd0cd52945c0e55cdef97f8a0716d69e |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 15 | 0.0733 | 0.04 | 0.0055 | 0.0097 | 0.9861 | | No log | 2.0 |... | ca1d2c10a5897a1ecad10cb3e6806588 |
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: 0.1474 - F1: 0.8651 | a35aa5197aa9e9d9da9f92fc066ae55e |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2498 | 1.0 | 1049 | 0.1835 | 0.8213 | | 0.1293 | 2.0 | 2098 | 0.1448 | 0.8481 | | 0.0788 | 3.0 | 3147 | 0.1474 | 0.8651 | ... | 183cbf43e753997ec17e5459345966e5 |
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 617189d2d7e060bbcf670ab54b88776333b5137e pip install -e . cd egs2/librispeech/asr1 ./run.sh --skip_data_prep false --skip_train... | 813a9d3ab6ee6bddb612b8f7510c0fc7 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Environments - date: `Thu Dec 29 11:58:25 UTC 2022` - python version: `3.10.8 (main, Nov 24 2022, 14:13:03) [GCC 11.2.0]` - espnet version: `espnet 202211` - pytorch version: `pytorch 1.12.0` - Git hash: `617189d2d7e060bbcf670ab54b88776333b5137e` - Commit date: `Mon Dec 26 18:01:58 2022 +0900` | bc527912be7c3d283e414c325ac39b28 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | WER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |beam60_ctc0.3/dev_clean|2703|54402|98.2|1.6|0.2|0.2|2.0|25.9| |beam60_ctc0.3/dev_other|2864|50948|95.5|4.2|0.4|0.5|5.0|42.2| |beam60_ctc0.3/test_clean|2620|52576|98.0|1.8|0.2|0.3|2.3|27.2| |beam60_ctc0.3/test_other|2939|52343|95.6... | 5c0d9b8f5c472df31d22ed765e5a7754 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | CER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |beam60_ctc0.3/dev_clean|2703|288456|99.5|0.3|0.2|0.2|0.6|25.9| |beam60_ctc0.3/dev_other|2864|265951|98.4|1.0|0.6|0.5|2.1|42.2| |beam60_ctc0.3/test_clean|2620|281530|99.5|0.3|0.2|0.2|0.7|27.2| |beam60_ctc0.3/test_other|2939|272758|... | 4de5c197c1579f0e0b7547138c9ab2da |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | TER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |beam60_ctc0.3/dev_clean|2703|68010|97.8|1.6|0.6|0.4|2.5|25.9| |beam60_ctc0.3/dev_other|2864|63110|94.5|4.3|1.3|0.9|6.4|42.2| |beam60_ctc0.3/test_clean|2620|65818|97.5|1.7|0.7|0.4|2.8|27.2| |beam60_ctc0.3/test_other|2939|65101|94.6... | 7bd9dffd2468fdee434724ec96866149 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | ASR config <details><summary>expand</summary> ``` config: conf/tuning/train_asr_s4_decoder.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/asr_train_asr_s4_decoder_raw_en_bpe5000_sp ngpu: 1 seed: 0 num_workers: 4 num_att_plot: 3 dist_backend: nccl dist_init_method: env... | 4f20a7bd2b03ff33a03ac38d15a45679 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Citing ESPnet ```BibTex @inproceedings{watanabe2018espnet, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, title={{ESPnet}: End-to-En... | b59dcb7e71ffe5e53b7f8382f25833cf |
apache-2.0 | ['automatic-speech-recognition', 'ja'] | false | exp_w2v2t_ja_unispeech_s947 Fine-tuned [microsoft/unispeech-large-1500h-cv](https://huggingface.co/microsoft/unispeech-large-1500h-cv) for speech recognition using the train split of [Common Voice 7.0 (ja)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that you... | 1f466899bf120149648c3be1a1fd4e13 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 421 | 0.3940 | 0.8306 | | 529064230f305b56f5bf6dba7ac01354 |
apache-2.0 | [] | false | **How do I pronounce the name of the model?** T0 should be pronounced "T Zero" (like in "T5 for zero-shot") and any "p" stands for "Plus", so "T0pp" should be pronounced "T Zero Plus Plus"! **Official repository**: [bigscience-workshop/t-zero](https://github.com/bigscience-workshop/t-zero) | 07379af227e6144873c6a6394d978b84 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-test2 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the wnut_17 dataset. It achieves the following results on the evaluation set: - Loss: 0.2937 - Precision: 0.5410 - Recall: 0.3976 - F1: 0.4583 - Accuracy: 0.9469 | 2b43bf4b7fd5da595e2a3d102d5a0ecb |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 213 | 0.2700 | 0.5102 | 0.3698 | 0.4288 | 0.9447 | | No log | 2.0 |... | b132116324236976f8d79e1d34495c81 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-timit-demo-colab11 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: 0.4922 - Wer: 0.4348 | 6e624726eb14ea1b015748caee04dcad |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 15 - mixed_precision_tr... | ad1760c31776a804f45e92160a8eca27 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 4.2269 | 3.52 | 500 | 1.1191 | 0.7121 | | 0.8297 | 7.04 | 1000 | 0.6064 | 0.5228 | | 0.4988 | 10.56 | 1500 | 0.5057 | 0.4627 | |... | 7039a7caa9d086cfdd7d93908cceda7c |
apache-2.0 | ['fill-mask'] | false | Model description This model is an adaptation of DistilBERT (Victor Sanh et al., 2019) for Marathi language. This version of Marathi-DistilBERT is trained from scratch on approximately 11.2 million sentences. ``` DISCLAIMER This model has not been thoroughly tested and may contain biased opinions or inappropriate ... | 17709046d7331dddd9e74b4dc9382497 |
apache-2.0 | ['fill-mask'] | false | Training data The training data has been extracted from a variety of sources, mainly including: 1. Oscar Corpus 2. Marathi Newspapers 3. Marathi storybooks and articles The data is cleaned by removing all languages other than Marathi, while preserving common punctuations | c472f4dea9922d66215169aca3f1e184 |
apache-2.0 | ['fill-mask'] | false | Training procedure The model is trained from scratch using an Adam optimizer with a learning rate of 1e-4 and default β1 and β2 values of 0.9 and 0.999 respectively with a total batch size of 256 on a v3-8 TPU and mask probability of 15%. | 002eb759e5ea60e10cef0fc67100449e |
apache-2.0 | ['fill-mask'] | false | Example ```python from transformers import pipeline fill_mask = pipeline( "fill-mask", model="DarshanDeshpande/marathi-distilbert", tokenizer="DarshanDeshpande/marathi-distilbert", ) fill_mask("हा खरोखर चांगला [MASK] आहे.") ``` | e0edda02e42f0da05da554e1cb4e6407 |
apache-2.0 | ['fill-mask'] | false | BibTeX entry and citation info ```bibtex @misc{sanh2020distilbert, title={DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter}, author={Victor Sanh and Lysandre Debut and Julien Chaumond and Thomas Wolf}, year={2020}, eprint={1910.01108}, archivePrefix={arXiv},... | 0660b92c1b42a0db0aacce598f83924e |
apache-2.0 | ['tapas'] | false | TAPAS large model fine-tuned on Sequential Question Answering (SQA) This model has 2 versions which can be used. The default version corresponds to the `tapas_sqa_inter_masklm_large_reset` checkpoint of the [original Github repository](https://github.com/google-research/tapas). This model was pre-trained on MLM and a... | 59927a85653d7a30da56068d17629beb |
apache-2.0 | ['tapas'] | false | Results on SQA - Dev Accuracy Size | Reset | Dev Accuracy | Link -------- | --------| -------- | ---- **LARGE** | **noreset** | **0.7223** | [tapas-large-finetuned-sqa (absolute pos embeddings)](https://huggingface.co/google/tapas-large-finetuned-sqa/tree/no_reset) **LARGE** | **reset** | **0.7289** | [tapas-la... | 83969566574f6af62e72a36b147c991d |
apache-2.0 | ['tapas'] | false | BibTeX entry and citation info ```bibtex @misc{herzig2020tapas, title={TAPAS: Weakly Supervised Table Parsing via Pre-training}, author={Jonathan Herzig and Paweł Krzysztof Nowak and Thomas Müller and Francesco Piccinno and Julian Martin Eisenschlos}, year={2020}, eprint={2004.02349}, a... | 5ad01bfada491ad3563d2d2e2184bdc1 |
apache-2.0 | ['exbert', 'multiberts', 'multiberts-seed-4'] | false | MultiBERTs Seed 4 Checkpoint 1800k (uncased) Seed 4 intermediate checkpoint 1800k 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... | 941a58926e64a413709fbbdc354a9662 |
apache-2.0 | ['exbert', 'multiberts', 'multiberts-seed-4'] | false | How to use Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import BertTokenizer, BertModel tokenizer = BertTokenizer.from_pretrained('multiberts-seed-4-1800k') model = BertModel.from_pretrained("multiberts-seed-4-1800k") text = "Replace me by any text you'd lik... | 94788dac345192d955f0735e3c16f032 |
apache-2.0 | ['automatic-speech-recognition', 'zh-CN'] | false | exp_w2v2t_zh-cn_r-wav2vec2_s237 Fine-tuned [facebook/wav2vec2-large-robust](https://huggingface.co/facebook/wav2vec2-large-robust) for speech recognition using the train split of [Common Voice 7.0 (zh-CN)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your... | 8f517dc61a42b4e2e15b9f19ab32bd55 |
apache-2.0 | ['speech', 'audio', 'automatic-speech-recognition', 'hf-asr-leaderboard'] | false | Wav2Vec2-Conformer-Large-960h with Relative Position Embeddings + 4-gram This model is identical to [Facebook's wav2vec2-conformer-rel-pos-large-960h-ft](https://huggingface.co/facebook/wav2vec2-conformer-rel-pos-large-960h-ft), but is augmented with an English 4-gram. The `4-gram.arpa.gz` of [Librispeech's official... | 1433829cc3d8f635fc60fa024aeb0101 |
apache-2.0 | ['speech', 'audio', 'automatic-speech-recognition', 'hf-asr-leaderboard'] | false | Evaluation This code snippet shows how to evaluate **patrickvonplaten/wav2vec2-conformer-rel-pos-large-960h-ft-4-gram** on LibriSpeech's "clean" and "other" test data. ```python from datasets import load_dataset from transformers import AutoModelForCTC, AutoProcessor import torch from jiwer import wer model_id =... | 8115cda35c579c43eb1f8dda78f1c43b |
cc-by-sa-4.0 | ['generated_from_trainer'] | false | t5-base-TEDxJP-1front-1body-1rear This model is a fine-tuned version of [sonoisa/t5-base-japanese](https://huggingface.co/sonoisa/t5-base-japanese) on the te_dx_jp dataset. It achieves the following results on the evaluation set: - Loss: 0.4600 - Wer: 0.1742 - Mer: 0.1683 - Wil: 0.2562 - Wip: 0.7438 - Hits: 55625 - S... | 5e82196afb8bc8e5ba38f1bd566ce672 |
cc-by-sa-4.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Mer | Wil | Wip | Hits | Substitutions | Deletions | Insertions | Cer | |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:------:|:-----:|:-------------:|:---------:|:----------:|:------:| | 0.6478 ... | ec26d7802b9f4070212c5232690639f4 |
mit | [] | false | teferi on Stable Diffusion This is the `<teferi>` 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... | b669e3208c7fb1b39e38a1f7f875e361 |
cc-by-4.0 | ['generated_from_trainer'] | false | hing-mbert-ours-run-3 This model is a fine-tuned version of [l3cube-pune/hing-mbert](https://huggingface.co/l3cube-pune/hing-mbert) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.9769 - Accuracy: 0.675 - Precision: 0.6433 - Recall: 0.6344 - F1: 0.6344 | 0902e4474d9fb765bc952bed46638a87 |
cc-by-4.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | 0.9089 | 1.0 | 100 | 1.0993 | 0.635 | 0.6487 | 0.5304 | 0.5060 | | 0.6657 | 2.0 |... | ebfb434a99dc13efe37fdc55f10cdab8 |
apache-2.0 | ['translation'] | false | opus-mt-es-sm * source languages: es * target languages: sm * OPUS readme: [es-sm](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/es-sm/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://... | 32a684aca1516f0dd7118fbeb3ff21ac |
apache-2.0 | ['generated_from_trainer'] | false | classification_chnsenticorp_eda_aug This model is a fine-tuned version of [hfl/chinese-roberta-wwm-ext](https://huggingface.co/hfl/chinese-roberta-wwm-ext) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.7802 - Accuracy: 0.55 | ba063129243b6782755f1a77aac27d32 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.4849 | 1.0 | 20 | 0.6880 | 0.4 | | 0.0979 | 2.0 | 40 | 0.8746 | 0.6 | | 0.0238 | 3.0 | 60 | 0.7802 | 0.... | 81bc31be4e30e44914683687797286e0 |
apache-2.0 | ['generated_from_keras_callback'] | false | text2text-example This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 4.9634 - Validation Loss: 3.2453 - Epoch: 4 | 75e8a458f2293ca1e9d807759d3bf31d |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 7.8274 | 6.5971 | 0 | | 6.5394 | 6.3717 | 1 | | 6.3486 | 6.3143 | 2 | | 6.1765 | 6.1031 | 3 | | 4.9634 | 3.2453 | 4 | | 7380bf2eddebb379cc7ffb24af34b61f |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Usage Using this model becomes easy when you have [ConGen](https://github.com/KornWtp/ConGen) installed: ``` pip install -U git+https://github.com/KornWtp/ConGen.git ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence",... | 3bc947834e7aa125480c695bbcc7e6ff |
apache-2.0 | ['exbert', 'multiberts', 'multiberts-seed-1'] | false | MultiBERTs Seed 1 Checkpoint 1500k (uncased) Seed 1 intermediate checkpoint 1500k 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... | 1e6535a19c161bc6e076175d65a40586 |
apache-2.0 | ['exbert', 'multiberts', 'multiberts-seed-1'] | 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-1-1500k') model = BertModel.from_pretrained("multiberts-seed-1-1500k") text = "Replace me by any text you'd lik... | 0fe7890a07e41c77db232f262f1c4295 |
mit | ['sentence-transformers', 'transformers', 'bert', 'pytorch', 'sentence-similarity'] | false | stjiris/bert-large-portuguese-cased-legal-mlm-mkd-nli-sts-v0 (Legal BERTimbau) This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search. stjiris/bert-large-portuguese-cased-legal-... | eb2e94342fb0b485b757c24ed0ad696b |
mit | ['sentence-transformers', 'transformers', 'bert', 'pytorch', 'sentence-similarity'] | 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 = ["Isto é um exemplo", "Isto ... | f5fb9ccd95357d10a9486b8fc7e00092 |
mit | ['sentence-transformers', 'transformers', 'bert', 'pytorch', 'sentence-similarity'] | false | Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('stjiris/bert-large-portuguese-cased-legal-mlm-mkd-nli-sts-v0') model = AutoModel.from_pretrained('stjiris/bert-large-portuguese-cased-legal-mlm-mkd-nli-sts-v0') | 8e244a3c30720a8717bcc3206a5cf98f |
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: 0.0641 - Precision: 0.9340 - Recall: 0.9495 - F1: 0.9417 - Accuracy: 0.9860 | d65f17df92f64c7a04b144be3c009f87 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0887 | 1.0 | 1756 | 0.0753 | 0.9149 | 0.9318 | 0.9233 | 0.9816 | | 0.033 | 2.0 |... | bd52321f4f4baabb4bc67f32fbd84e67 |
apache-2.0 | ['T5', 'chinese', 'sentencepiece'] | false | 模型分类 Model Taxonomy | 需求 Demand | 任务 Task | 系列 Series | 模型 Model | 参数 Parameter | 额外 Extra | | :----: | :----: | :----: | :----: | :----: | :----: | | 通用 General | 自然语言转换 NLT | 燃灯 Randeng | mT5 | 784M | 中文-Chinese | | 3b157476c8f5e7c05d8cb9b7be7993cb |
apache-2.0 | ['T5', 'chinese', 'sentencepiece'] | false | 模型信息 Model Information 我们基于mT5-large，训练了它的中文版。为了加速训练，我们仅使用T5分词器(sentence piece)中的中英文对应的词表，并且使用了语料库自适应预训练(Corpus-Adaptive Pre-Training, CAPT)技术在悟道语料库(180G版本)继续预训练。预训练目标为破坏span。具体地，我们在预训练阶段中使用了[封神框架](https://github.com/IDEA-CCNL/Fengshenbang-LM/tree/main/fengshen)大概花费了16张A100约96小时。 Based on mT5-large, we implement its... | 455dc20587e566e0a1a18f3e2d899e23 |
apache-2.0 | ['T5', 'chinese', 'sentencepiece'] | false | 使用 Usage ```python from transformers import T5ForConditionalGeneration, AutoTokenizer import torch tokenizer=AutoTokenizer.from_pretrained('IDEA-CCNL/Randeng-T5-784M', use_fast=false) model=T5ForConditionalGeneration.from_pretrained('IDEA-CCNL/Randeng-T5-784M') ``` | f3d73abe497e64e13054769a5dd68d3c |
apache-2.0 | ['translation'] | false | opus-mt-sv-lv * source languages: sv * target languages: lv * OPUS readme: [sv-lv](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sv-lv/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://... | 3a586fa4ac42ceecdeefe070ba2d9262 |
apache-2.0 | [] | false | 8209;NCC|[🤗](https://huggingface.co/north/t5_small_NCC)|✔|[🤗](https://huggingface.co/north/t5_large_NCC)|[🤗](https://huggingface.co/north/t5_xl_NCC)|[🤗](https://huggingface.co/north/t5_xxl_NCC)|| |North-T5& | fc305621719a0af848e1c6fb971a6460 |
apache-2.0 | ['translation'] | false | opus-mt-rw-fr * source languages: rw * target languages: fr * OPUS readme: [rw-fr](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/rw-fr/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://... | 5d942a5b6d66ec8378085562df90d56d |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | opus-mt-tc-big-en-gmq Neural machine translation model for translating from English (en) to North Germanic languages (gmq). This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in t... | 6a46bc526a84afdbb1377a1d587d88da |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Model info * Release: 2022-03-17 * source language(s): eng * target language(s): dan fao isl nno nob nor swe * valid target language labels: >>dan<< >>fao<< >>isl<< >>nno<< >>nob<< >>nor<< >>swe<< * model: transformer-big * data: opusTCv20210807+bt ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge)) * token... | de382d9f6f325831ae36451082874b1c |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Usage A short example code: ```python from transformers import MarianMTModel, MarianTokenizer src_text = [ ">>nno<< The United States borders Canada.", ">>nob<< This is the biggest hotel in this city." ] model_name = "pytorch-models/opus-mt-tc-big-en-gmq" tokenizer = MarianTokenizer.from_pretrained(model_n... | bf47270678c0c8d987284d0061ea8b2c |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Dette er det største hotellet i denne byen. ``` You can also use OPUS-MT models with the transformers pipelines, for example: ```python from transformers import pipeline pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-en-gmq") print(pipe(">>nno<< The United States borders Canada.")) | e9ac762d2acea3c54358e62c0660cd7a |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Benchmarks * test set translations: [opusTCv20210807+bt_transformer-big_2022-03-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-gmq/opusTCv20210807+bt_transformer-big_2022-03-17.test.txt) * test set scores: [opusTCv20210807+bt_transformer-big_2022-03-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-... | 8c566da8ae25ffee211cb99721143c22 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | words | |----------|---------|-------|-------|-------|--------| | eng-dan | tatoeba-test-v2021-08-07 | 0.75165 | 61.6 | 10795 | 79385 | | eng-fao | tatoeba-test-v2021-08-07 | 0.40395 | 18.3 | 294 | 1933 | | eng-isl | tatoeba-test-v2021-08-07 | 0.59731 | 39.9 | 2503 | 19023 | | eng-nno | tatoeba-test-v2021-08-07 | 0.612... | a9ef60c696526cc2c67b73d796687c5d |
mit | ['generated_from_trainer'] | false | roberta-base.CEBaB_confounding.food_service_positive.absa.5-class.seed_44 This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the OpenTable OPENTABLE-ABSA dataset. It achieves the following results on the evaluation set: - Loss: 0.7906 - Accuracy: 0.8058 - Macro-f1: 0.8045 - W... | 7f47c3d818b750c42bc84538b14fc083 |
apache-2.0 | ['translation'] | false | opus-mt-fi-et * source languages: fi * target languages: et * OPUS readme: [fi-et](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fi-et/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-08.zip](https://... | 57c40915a4b405e8f9b9f0141a3fa7f0 |
mit | ['generated_from_trainer'] | false | gpt2_summarization_reward_model This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7376 - Accuracy: 0.6020 | 0ad226f8dc2622b8c0625892baeb1dbe |
mit | ['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 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - total_eval_batch_size: 32 - optimizer: Adam with... | 1c4f365297f6dad09e3bf828ea490930 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6521 | 1.0 | 1451 | 0.6670 | 0.6037 | | 0.6101 | 2.0 | 2902 | 0.6763 | 0.6022 | | 0.5772 | 3.0 | 4353 | 0.7034 | 0.... | 1d907c8307fc40fd22fd582f58d7311e |
apache-2.0 | ['classification'] | false | Sample Usage from transformers import BertTokenizer, BertForSequenceClassification device = torch.device("cuda" if torch.cuda.is_available() else "cpu") checkpoint = "Herais/pred_genre" tokenizer = BertTokenizer.from_pretrained(checkpoint, problem_typ... | 236e552107e2ad8538d4d6690f39c84c |
apache-2.0 | ['translation'] | false | opus-mt-sl-fi * source languages: sl * target languages: fi * OPUS readme: [sl-fi](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sl-fi/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://... | e3864d96b88a157315ce522b0d2b7dd5 |
apache-2.0 | ['translation'] | false | opus-mt-tll-fi * source languages: tll * target languages: fi * OPUS readme: [tll-fi](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/tll-fi/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](http... | 594a900d9ddfa40b985bac932d78b58e |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'animal'] | false | DreamBooth model for the adelacq concept trained by AdelaZ. This is a Stable Diffusion model fine-tuned on the adelacq concept with DreamBooth. It can be used by modifying the `instance_prompt`: **a photo of adelacq dog** This model was created as part of the DreamBooth Hackathon 🔥. Visit the [organisation page](ht... | 412c8812a461828e1bed4d1fce8891c3 |
apache-2.0 | ['generated_from_trainer'] | false | openai/whisper-medium This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4748 - Wer: 18.8791 | 0bb073713f4a4f095fc34bbddb2a54f2 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 2 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 100 - mixed_precision... | 9a3f4c8b4b9a3d78976658ee620c7045 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.3702 | 1.0 | 100 | 0.4748 | 18.8791 | | 56c0fd273fda8794bc69677dac1d47f2 |
mit | ['generated_from_trainer'] | false | bart-large-cnn-finetuned-pubmed-finetuned-roundup-e8 This model is a fine-tuned version of [theojolliffe/bart-large-cnn-finetuned-pubmed](https://huggingface.co/theojolliffe/bart-large-cnn-finetuned-pubmed) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.1034 - Rouge1: 48.460... | 00ec853c1b803215201da3a864ce0d26 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | No log | 1.0 | 25 | 1.4278 | 47.952 | 29.4059 | 34.273 | 45.7244 | 14... | 2d56df825b93217516f86ed73810a9a4 |
apache-2.0 | ['exbert', 'multiberts', 'multiberts-seed-2'] | false | MultiBERTs Seed 2 Checkpoint 200k (uncased) Seed 2 intermediate checkpoint 200k 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/goo... | 95a88fc20e96647bce1ebab18e73afb6 |
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-2-200k') model = BertModel.from_pretrained("multiberts-seed-2-200k") text = "Replace me by any text you'd like.... | 5c10c9cbb6974231234d0c9170d05d21 |
apache-2.0 | ['translation'] | false | opus-mt-lg-fi * source languages: lg * target languages: fi * OPUS readme: [lg-fi](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/lg-fi/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-24.zip](https://... | be92b69d2fe19f48fa9ae66b3b0f22b0 |
apache-2.0 | ['generated_from_trainer'] | false | openai/whisper-large-v2 This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4041 - Wer: 15.7710 - Cer: 7.6691 | 696b9cf3c8a6888721486dbea60c2f43 |
apache-2.0 | ['generated_from_trainer'] | false | Training and evaluation data Training data: * [mozilla-foundation/common_voice_11_0](https://huggingface.co/openai/whisper-large-v2) * [google/fleurs](https://huggingface.co/datasets/google/fleurs) Evaluation data: * [mozilla-foundation/common_voice_11_0](https://huggingface.co/openai/whisper-large-v2) | 348dbccdb442a40a932f20d89fe6e319 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-07 - train_batch_size: 8 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 3 - total_train_batch_size: 24 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sched... | 96e88de394fc20306f42c88ea50ecd27 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:| | 0.3983 | 0.1 | 500 | 0.5338 | 19.5876 | 10.6391 | | 0.2277 | 1.08 | 1000 | 0.4134 | 16.5826 | 8.2668 | | 0.172 |... | 7ef5c706af010c43503310e899f2317f |
apache-2.0 | ['generated_from_trainer'] | false | mobilebert_add_GLUE_Experiment_logit_kd_stsb_256 This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE STSB dataset. It achieves the following results on the evaluation set: - Loss: 1.1476 - Pearson: 0.0175 - Spearmanr: 0.0051 - Combined Score:... | 89ef9cfee279521f2edfed96bd7afd91 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Pearson | Spearmanr | Combined Score | |:-------------:|:-----:|:----:|:---------------:|:-------:|:---------:|:--------------:| | 2.1451 | 1.0 | 45 | 1.1476 | 0.0175 | 0.0051 | 0.0113 | | 1.0864 | 2.0 | 90 ... | 8cd2b70ef70881db57b21bf4080ed650 |
apache-2.0 | ['generated_from_trainer'] | false | distil_bert_uncased-finetuned-relations 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: 0.4191 - Accuracy: 0.8866 - Prec: 0.8771 - Recall: 0.8866 - F1: 0.8808 | 06c8c246db7806e32afeb75ce0fb9ccf |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Prec | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:------:| | 1.1823 | 1.0 | 232 | 0.5940 | 0.8413 | 0.8273 | 0.8413 | 0.8224 | | 0.4591 | 2.0 | 464 | 0... | 3d44eced7806231ed4cb8742d361e8a6 |
apache-2.0 | ['generated_from_trainer'] | false | small-sentiment-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: - Loss: 0.3325 - Accuracy: 0.8633 | dfa00d148c3058a1f0fa51bcc53e458b |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-paws This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the pawsx dataset. It achieves the following results on the evaluation set: - Loss: 0.3850 - Accuracy: 0.8355 - F1: 0.8362 | b94f73e5e60ab880f62369f2ee852450 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.6715 | 1.0 | 772 | 0.5982 | 0.6785 | 0.6799 | | 0.4278 | 2.0 | 1544 | 0.3850 | 0.8355 | 0.8362 | | 3fc8c4dd603b65fbc92b9e944a4d66a9 |
apache-2.0 | ['translation'] | false | eng-sal * source group: English * target group: Salishan languages * OPUS readme: [eng-sal](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-sal/README.md) * model: transformer * source language(s): eng * target language(s): shs_Latn * model: transformer * pre-processing: normalization + ... | d142c365af0355d8a74514f63ace02a0 |
apache-2.0 | ['translation'] | false | Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | Tatoeba-test.eng.multi | 32.6 | 0.585 | | Tatoeba-test.eng.shs | 1.1 | 0.072 | | Tatoeba-test.eng-shs.eng.shs | 1.2 | 0.065 | | bb4bd7a48834cdce0712598578fefbcb |
apache-2.0 | ['translation'] | false | System Info: - hf_name: eng-sal - source_languages: eng - target_languages: sal - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-sal/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['en', 'sal'] - src_constituents: {'eng'} - tgt_cons... | fac46efd09d9f082a4d6d9f741466f40 |
apache-2.0 | ['generated_from_trainer'] | false | keyword_category_classifier 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: 0.2184 - Accuracy: 0.9333 | 1c130cb707051aa487be55ac048cb457 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.5646 | 1.0 | 917 | 0.2161 | 0.9298 | | 0.2032 | 2.0 | 1834 | 0.2184 | 0.9333 | | 31de35d41bb293433636e7c6b445f330 |
apache-2.0 | ['argumentation'] | false | Generate a chain of reasoning from one claim to another This model is a version of [`gpt-neo-2.7B`](https://huggingface.co/EleutherAI/gpt-neo-2.7B), where some parameters (only the bias parameters, not weights) have been finetuned on the task of generating a sequence of claims (a 'chain of reasoning') that joins one ... | e7974be6cabb192ca47425a9d44f27cb |
mit | ['mbart-50'] | false | Knight-errant Knight is a text style transfer model for knight-errant style. This model is for Chinese Knight-errant style transfer. paper link: [To be a Knight-errant Novel Master: Knight-errant Style Transfer via Contrastive Learning](https://openreview.net/forum?id=FDw2hdpiWNO) ```python | ec8c7e51de781a01c260d02713d744ae |
mit | ['mbart-50'] | false | inference from transformers import MBartForConditionalGeneration, MBart50TokenizerFast model = MBartForConditionalGeneration.from_pretrained("Anonymous-TST/knight-errant-TST-zh") tokenizer = MBart50TokenizerFast.from_pretrained("facebook/mbart-large-50", src_lang="zh_CN", tgt_lang="zh_CN") model.cuda() model.eval() ... | 566ddee125ef91a45f17b8b806b1c8a4 |
mit | [] | false | mechasoulall on Stable Diffusion This is the `<mechasoulall>` 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 ... | c66f7695a44935337c26c378f255b3a2 |
apache-2.0 | ['speech', 'xls_r', 'xls_r_translation', 'automatic-speech-recognition'] | false | Wav2Vec2-XLS-R-300M-EN-15 Facebook's Wav2Vec2 XLS-R fine-tuned for **Speech Translation.**  This is a [SpeechEncoderDecoderModel](https://huggingface.co/transformers/model_doc/speechencoderdecoder.html) model. The ... | a771c9edbfffec24ed4c556be16f7c93 |
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