license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Yksi, kaksi, kolme, neljä, viisi, kuusi, seitsemän, kahdeksan, yhdeksän, kymmenen. ``` 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-base-uk-fi") print(pipe("Африка є колискою... | fa28b608f56e1758e33a6251b73fa9e1 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Benchmarks * test set translations: [opusTCv20210807+pft+pbt_transformer-align_2022-03-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/ukr-fin/opusTCv20210807+pft+pbt_transformer-align_2022-03-17.test.txt) * test set scores: [opusTCv20210807+pft+pbt_transformer-align_2022-03-17.eval.txt](https://object.pou... | 3a604a57f9d5a74b5a309254ae9582a9 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-en 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: 0.4990 - F1: 0.7093 | 358667784eea0520713eb61fc34ab528 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.8727 | 1.0 | 295 | 0.5063 | 0.6186 | | 0.4633 | 2.0 | 590 | 0.5089 | 0.6561 | | 0.3075 | 3.0 | 885 | 0.4990 | 0.7093 | ... | e4bcd4707c5c3418a58f09dd404e937d |
apache-2.0 | ['generated_from_trainer'] | false | SST2_DistilBERT_5E 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: 0.4125 - Accuracy: 0.8933 | ee1d8290a73784f3201fe91222d28c04 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6744 | 0.12 | 50 | 0.6094 | 0.66 | | 0.4942 | 0.23 | 100 | 0.3772 | 0.8667 | | 0.3857 | 0.35 | 150 | 0.3256 | 0.... | 0b683aaa1e8bbc14ca5349f82d13174f |
apache-2.0 | ['generated_from_keras_callback'] | false | hsohn3/mayo-bert-visit-uncased-wordlevel-block512-batch4-ep10 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 3.2895 - Epoch: 9 | 7cf36e9981153eb9b91058220381ef6e |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Epoch | |:----------:|:-----:| | 4.1298 | 0 | | 3.5157 | 1 | | 3.4732 | 2 | | 3.4565 | 3 | | 3.4444 | 4 | | 3.4349 | 5 | | 3.4197 | 6 | | 3.4109 | 7 | | 3.3493 | 8 | | 3.2895 | 9 | | 0de823b1b4c29295e689eb6728fb2bae |
apache-2.0 | ['generated_from_trainer'] | false | distilroberta-base-finetuned-toxic This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.2768 | 2c41f8991b0ee0e871a7946d9131287d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.5338 | 1.0 | 313 | 2.3127 | | 2.4482 | 2.0 | 626 | 2.2985 | | 2.4312 | 3.0 | 939 | 2.2411 | | 23e42059d0142bb096c8fb3b2c687b42 |
apache-2.0 | ['multiberts', 'multiberts-seed_0', 'multiberts-seed_0-step_100k'] | false | MultiBERTs, Intermediate Checkpoint - Seed 0, Step 100k 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 ... | 9cc84c278b23795962b4dc153993cb32 |
apache-2.0 | ['multiberts', 'multiberts-seed_0', 'multiberts-seed_0-step_100k'] | 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_0-step_100k') model = TFBertModel.from_pretrained("google/multibe... | 2e5114ab5ab2a3ff9db86d059455d04c |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Model Details Neural machine translation model for translating from Italic languages (itc) to Hebrew (he). 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 the world. All mod... | a07f03aa937f27b8d4983f281cdc2254 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | How to Get Started With the Model A short example code: ```python from transformers import MarianMTModel, MarianTokenizer src_text = [ "La María és feminista.", "Contribuyan en Tatoeba." ] model_name = "pytorch-models/opus-mt-tc-big-itc-he" tokenizer = MarianTokenizer.from_pretrained(model_name) model = Ma... | c66f12578d828b5194ee53875202f27b |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | תרום לטאטואבה. ``` 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-itc-he") print(pipe("La María és feminista.")) | 0c763496713964e4055cedbf94e25860 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Training - **Data**: opusTCv20210807 ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge)) - **Pre-processing**: SentencePiece (spm32k,spm32k) - **Model Type:** transformer-big - **Original MarianNMT Model**: [opusTCv20210807_transformer-big_2022-08-03.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/itc-h... | 302d32bcb556d06ae697dd7b503e0b54 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Evaluation * test set translations: [opusTCv20210807_transformer-big_2022-08-03.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/itc-heb/opusTCv20210807_transformer-big_2022-08-03.test.txt) * test set scores: [opusTCv20210807_transformer-big_2022-08-03.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/it... | 0cea89b439889796022dc5dc3639208d |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | words | |----------|---------|-------|-------|-------|--------| | fra-heb | tatoeba-test-v2021-08-07 | 0.60539 | 39.6 | 3281 | 20655 | | ita-heb | tatoeba-test-v2021-08-07 | 0.60264 | 40.0 | 1706 | 9796 | | por-heb | tatoeba-test-v2021-08-07 | 0.63087 | 44.4 | 719 | 4423 | | spa-heb | tatoeba-test-v2021-08-07 | 0.63883... | b240c15279912fc49457cf74a2a2a41b |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Tiny Indonesian This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the mozilla-foundation/common_voice_11_0 id dataset. It achieves the following results on the evaluation set: - Loss: 0.6202 - Wer: 32.4218 | c4c522e8ba237d1e9f323937036e42b9 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 32 - 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: 5000 - mixed_precis... | a65d26e4b3046d23442420e66a4fb19c |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.3823 | 4.95 | 500 | 0.5251 | 33.4732 | | 0.0495 | 9.9 | 1000 | 0.5700 | 33.3902 | | 0.0077 | 14.85 | 1500 | 0.6202 | 32.421... | 1a4d28ba05e034d00ded2b9a0d9045bd |
apache-2.0 | ['multiberts', 'multiberts-seed_3', 'multiberts-seed_3-step_80k'] | false | MultiBERTs, Intermediate Checkpoint - Seed 3, Step 80k 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... | ba9876779c712c1658e03c6998a89cd1 |
apache-2.0 | ['multiberts', 'multiberts-seed_3', 'multiberts-seed_3-step_80k'] | false | How to use Using code from [BERT-base uncased](https://huggingface.co/bert-base-uncased), here is an example based on Tensorflow: ``` from transformers import BertTokenizer, TFBertModel tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_3-step_80k') model = TFBertModel.from_pretrained("google/multiber... | 920438706c5fe8bf2b73a53d63f26955 |
apache-2.0 | ['masked-image-modeling', 'generated_from_trainer'] | false | dit-base-manuscripts This model is a fine-tuned version of [facebook/deit-base-distilled-patch16-224](https://huggingface.co/facebook/deit-base-distilled-patch16-224) on the davanstrien/iiif_manuscripts_label_ge_50 dataset. It achieves the following results on the evaluation set: - Loss: 1.1266 | 02f803b0e32d3164ceb097f980c62421 |
apache-2.0 | ['masked-image-modeling', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 1333 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1.0 | a3c37374d4247975b4cbd298ecb53620 |
cc-by-4.0 | ['espnet', 'audio', 'text-to-speech'] | false | Demo: How to use in ESPnet2 ```bash cd espnet git checkout 49a284e69308d81c142b89795de255b4ce290c54 pip install -e . cd egs2/talromur/tts1 ./run.sh --skip_data_prep false --skip_train true --download_model espnet/GunnarThor_talromur_c_fastspeech2 ``` | eb6a45cf0b8866d7a6819949b3597dec |
cc-by-4.0 | ['espnet', 'audio', 'text-to-speech'] | false | TTS config <details><summary>expand</summary> ``` config: conf/tuning/train_fastspeech2.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/c/tts_train_fastspeech2_raw_phn_none ngpu: 1 seed: 0 num_workers: 1 num_att_plot: 3 dist_backend: nccl dist_init_method: env:// dist_... | 2ae79e1ca9611cd59d3c628c63ed3fb8 |
apache-2.0 | ['generated_from_trainer'] | false | bert-large-uncased_stereoset_finetuned This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on the stereoset dataset. It achieves the following results on the evaluation set: - Loss: 1.0729 - Accuracy: 0.7716 | b5543fb054202bf0b6bfce8246ea923e |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 0.21 | 5 | 0.6925 | 0.5071 | | No log | 0.42 | 10 | 0.6978 | 0.5008 | | No log | 0.62 | 15 | 0.6891 | 0.... | 15ec2aedb45d5082113ed5b888d6ef00 |
apache-2.0 | ['generated_from_trainer'] | false | all-roberta-large-v1-small_talk-2-16-5 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: 2.3566 - Accuracy: 0.3855 | 9c7b93e01f4ef1b69f7311d1c6e4f83f |
['apache-2.0'] | ['xlnet', 'lm-head', 'causal-lm'] | false | Model description This model require Mecab and senetencepiece with XLNetTokenizer. See details https://qiita.com/mkt3/items/4d0ae36f3f212aee8002 This model uses NFKD as the normalization method for character encoding. Japanese muddle marks and semi-muddle marks will be lost. *日本語の濁点・半濁点がないモデルです* | 8f8ef6a60ef8bcf49bb9d13028bd04cd |
['apache-2.0'] | ['xlnet', 'lm-head', 'causal-lm'] | false | How to use ```python from fugashi import Tagger from transformers import ( pipeline, XLNetLMHeadModel, XLNetTokenizer ) class XLNet(): def __init__(self): self.m = Tagger('-Owakati') self.gen_model = XLNetLMHeadModel.from_pretrained("hajime9652/xlnet-japanese") self.gen_tok... | 019dc81e00a65ec7a64cdb1af293dd85 |
['apache-2.0'] | ['xlnet', 'lm-head', 'causal-lm'] | false | Important matter The company that created and published this model is called Stockmark. This repository is for use by HuggingFace and not for infringement. See this documents https://qiita.com/mkt3/items/4d0ae36f3f212aee8002 published by https://github.com/mkt3 | 1f61bd259c4737ae7e76d2e43e3234b2 |
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: 0.0639 - Precision: 0.9357 - Recall: 0.9507 - F1: 0.9432 - Accuracy: 0.9857 | 903243283c940dcaa5ea36d72899ac5c |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0847 | 1.0 | 1756 | 0.0636 | 0.9150 | 0.9387 | 0.9267 | 0.9840 | | 0.0399 | 2.0 |... | c983e96404a21580ea85eba61aece82e |
apache-2.0 | ['automatic-speech-recognition', 'ru'] | false | exp_w2v2t_ru_hubert_s451 Fine-tuned [facebook/hubert-large-ll60k](https://huggingface.co/facebook/hubert-large-ll60k) for speech recognition using the train split of [Common Voice 7.0 (ru)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is... | 6307da40ad7674228a9e909a27482781 |
apache-2.0 | ['translation'] | false | kor-spa * source group: Korean * target group: Spanish * OPUS readme: [kor-spa](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/kor-spa/README.md) * model: transformer-align * source language(s): kor kor_Hang kor_Latn * target language(s): spa * model: transformer-align * pre-processing: nor... | 85102e1e40d822bcf64b9b966e9d6391 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: kor-spa - source_languages: kor - target_languages: spa - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/kor-spa/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['ko', 'es'] - src_constituents: {'kor_Hani', 'kor_Ha... | 5c09ca791a3b47bdc3ac56d210785378 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_sa_GLUE_Experiment_logit_kd_data_aug_qnli_192 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE QNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.4463 - Accuracy: 0.5576 | ceaf244405ecfca07485f56308312f8f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.338 | 1.0 | 16604 | 0.4463 | 0.5576 | | 0.2791 | 2.0 | 33208 | 0.4560 | 0.5711 | | 0.256 | 3.0 | 49812 | 0.4603 ... | 3cbcd711ae716ff39736c51283395802 |
cc-by-4.0 | ['espnet', 'audio', 'text-to-speech'] | false | `kan-bayashi/vctk_tts_train_xvector_tacotron2_raw_phn_tacotron_g2p_en_no_space_train.loss.ave` ♻️ Imported from https://zenodo.org/record/4394600/ This model was trained by kan-bayashi using vctk/tts1 recipe in [espnet](https://github.com/espnet/espnet/). | 48330a61c967c5ed5e58790e0c5f21f1 |
creativeml-openrail-m | ['text-to-image'] | false | Duskfall's Digital Fantasy Dreambooth model trained by Duskfallcrew with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v1-5 base model You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/h... | bdf67bb43805d121d8614d550ef1cf69 |
apache-2.0 | ['generated_from_trainer'] | false | M4_MLM 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: 7.3456 | 6d0dd372fcd3cfd79ad1d7541e7442ee |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 8.7633 | 1.0 | 26 | 8.0400 | | 7.8899 | 2.0 | 52 | 7.6923 | | 7.589 | 3.0 | 78 | 7.4373 | | 0985edbac5ce8875df411e6f1d397363 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-xlsr-53-espeak-cv-ft-sah-ntsema-colab This model is a fine-tuned version of [facebook/wav2vec2-xlsr-53-espeak-cv-ft](https://huggingface.co/facebook/wav2vec2-xlsr-53-espeak-cv-ft) on the audiofolder dataset. It achieves the following results on the evaluation set: - Loss: 0.2143 - Wer: 0.2247 | 5ace61071cc0992a6ecae56f0ed930a3 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 2.7431 | 5.71 | 400 | 0.2879 | 0.4054 | | 0.1876 | 11.42 | 800 | 0.2349 | 0.3023 | | 0.0986 | 17.14 | 1200 | 0.2248 | 0.2701 | |... | 88239ad22822c94ca6b0d3e45601da77 |
apache-2.0 | ['automatic-speech-recognition', 'de'] | false | exp_w2v2r_de_vp-100k_gender_male-10_female-0_s504 Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (de)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using ... | 28d970fe40828d83a086272d23fc766d |
apache-2.0 | ['generated_from_trainer', 'summarization'] | false | arxiv27k-t5-abst-title-gen/ This model is a fine-tuned version of mt5-small on the arxiv-abstract-title dataset. It achieves the following results on the evaluation set: - Loss: 1.6002 - Rouge1: 32.8 - Rouge2: 21.9 - Rougel: 34.8 - | 8dcc6bae56791bce5c339308ceb7bca9 |
apache-2.0 | ['generated_from_trainer', 'summarization'] | false | Training args model_args = T5Args() model_args.max_seq_length = 256 model_args.train_batch_size = 8 model_args.eval_batch_size = 8 model_args.num_train_epochs = 6 model_args.evaluate_during_training = False model_args.use_multiprocessing = False model_args.fp16 = False model_args.save_steps = 40000 model_args.save_eva... | 66a15d88271e0d5c70e7243c53baa298 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'diffusers'] | false | Gerph Welcome to the Gerph model. This model is trained in the art of the talented artist Gerph and has three versions for you to choose from. These models can be highly NSFW and are trained mainly on characters, as the work primarily focuses on this subject. Take a look at the demo images below to see the differenc... | a414c859727f3e821626b706da7ecd7c |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'diffusers'] | false | License These models are 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 models to deliberately produce nor share illegal or harmful outputs or content 2. The authors claims no rights on the o... | 3b779891747e99735a128a95fe1b7904 |
creativeml-openrail-m | [] | false | Prompt with **"hutari"** **Training details:** - Trained with [TheLastBen's fast-DreamBooth notebook](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) - data set: around 20 concept images + around 50 custmized reg images, the concept images are then duplicate... | 0cae69c29cacbcfa4c30a3353f067ec6 |
apache-2.0 | ['generated_from_trainer'] | false | canine-c-finetuned-mrpc This model is a fine-tuned version of [google/canine-c](https://huggingface.co/google/canine-c) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.4066 - Accuracy: 0.8627 - F1: 0.9014 | 453c4a66faa61ed4bad4fb2a91965e7c |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 230 | 0.5014 | 0.7696 | 0.8479 | | No log | 2.0 | 460 | 0.4755 | 0.7892 | 0.8622 | | 0.5096 |... | f7091aa0ca8be196b69af08abf58571c |
apache-2.0 | ['automatic-speech-recognition', 'es'] | false | exp_w2v2r_es_xls-r_age_teens-5_sixties-5_s62 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 tha... | e4d18fb588825a295e89f50bcc07a565 |
apache-2.0 | ['generated_from_trainer'] | false | finetuning-sentiment-model-25000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.3711 - Accuracy: 0.9314 - F1: 0.9320 | 96174958c4542bb5e4c3db1c329c0f0b |
afl-3.0 | ['generated_from_trainer', 'sentiment', 'emotion'] | false | electricidad-small-discriminator-finetuned-clasificacion-texto-suicida
This model is a fine-tuned version of [mrm8488/electricidad-small-discriminator](https://huggingface.co/mrm8488/electricidad-small-discriminator) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0458
- A... | 5bc4eb79add91d598f4e7fd98b3e8699 |
afl-3.0 | ['generated_from_trainer', 'sentiment', 'emotion'] | false | Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- lr_scheduler_type: linear
- num_epochs: 15
| a5430c4c545b3e367ea970cafe59ebe4 |
afl-3.0 | ['generated_from_trainer', 'sentiment', 'emotion'] | false | Training results
| Training Loss | Epoch | Validation Loss | Accuracy |
|:-------------:|:-----:|:---------------:|:--------:|
| 0.161100 | 1.0 | 0.133057 | 0.952718 |
| 0.134500 | 2.0 | 0.110966 | 0.960804 |
| 0.108500 | 3.0 | 0.086417 | 0.970835 |
| 0.099400 ... | c2e06a9b7993e1da3fe699aa3e39d9f9 |
creativeml-openrail-m | [] | false | mT5-small based Azerbaijani Summarization In this model, [Google's Multilingual T5-small](https://github.com/google-research/multilingual-t5) is fine-tuned on [Azerbaijani News Summary Dataset](https://huggingface.co/datasets/nijatzeynalov/azerbaijani-multi-news) for **Summarization** downstream task. The model is tr... | 0b39b4696bbc9f86a793698070b8ba98 |
creativeml-openrail-m | [] | false | Text-to-Text Transfer Transformer (T5) The paper [“Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer”](https://arxiv.org/pdf/1910.10683.pdf) presents a large-scale empirical survey to determine which transfer learning techniques work best and apply these insights at scale to create a n... | 11ea434a27fd37bfcde2b7e0b1c2eeef |
creativeml-openrail-m | [] | false | Multilingual t5 ["mt5"](https://arxiv.org/pdf/2010.11934v3.pdf) is a multilingual variant of T5 that was pre-trained on a new Common Crawl-based dataset covering 101 languages. mT5 is pre-trained only by unsupervised manner with multiple languages, and it’s not trained for specific downstream tasks. To dare say, t... | bc9981fde6cdcf03e316d4b6628ccd5e |
creativeml-openrail-m | [] | false | Training hyperparameters __mT5-based-azerbaijani-summarize__ model training took almost 12 hours on GPU instance with Ubuntu Server 20.04 LTS image in Microsoft Azure. The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 2 - eval_batch_size: 1 - seed: 42 - gradient_acc... | 44775a5ea0f23b7c27a24c11479bc94b |
creativeml-openrail-m | [] | false | Dataset Model was trained on [__az-news-summary__ dataset](https://huggingface.co/datasets/nijatzeynalov/azerbaijani-multi-news), a comprehensive and diverse dataset comprising 143k (143,448) Azerbaijani news articles extracted using a set of carefully designed heuristics. The dataset covers common topics for news... | a3c52744f17f552c0ade056c2145c3fb |
creativeml-openrail-m | [] | false | Training results with comparison __mT5-based-azerbaijani-summarize__ model rouge scores on the test set: - Rouge1: 39.4222 - Rouge2: 24.8624 - Rougel: 32.2487 For __Azerbaijani text summarization downstream task__, mT5-multilingual-XLSum has also been developed on the 45 languages of [XL-Sum](https://huggingface.co... | b5dc3208eb32b26424c21e2b47800ae7 |
creativeml-openrail-m | [] | false | Using this model in transformers ```python !pip install sentencepiece !pip install transformers ``` ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM article_text = """Ötən il Azərbaycana 74 577 avtomobil idxal edilib. Bu da 2021-ci illə müqayisədə 16 617 ədəd və ya 18,2% azdır. Xezerxeber.az-... | 62afc36d4cc2ce1677ec512ae2044f2e |
creativeml-openrail-m | [] | false | Citation If you use this model, please cite: ``` @misc {nijatzeynalov_2023, author = { {NijatZeynalov} }, title = { mT5-based-azerbaijani-summarize (Revision 19930ab) }, year = 2023, url = { https://huggingface.co/nijatzeynalov/mT5-based-azerbaijani-summarize }, doi = { 10... | 81775bffdc49c08b2909b0a2886494ab |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-ner This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0612 - Precision: 0.9237 - Recall: 0.9343 - F1: 0.9290 - Accuracy: 0.9833 | fc725b9d412060f916db92070d375f61 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.2462 | 1.0 | 878 | 0.0708 | 0.9118 | 0.9149 | 0.9133 | 0.9803 | | 0.0548 | 2.0 |... | 33d31dce70636ab99f4abd9e755d6f11 |
apache-2.0 | ['deep-narrow'] | false | T5-Efficient-BASE-EL6 (Deep-Narrow version) T5-Efficient-BASE-EL6 is a variation of [Google's original T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) following the [T5 model architecture](https://huggingface.co/docs/transformers/model_doc/t5). It is a *pretrained-only* checkpoint and ... | 0d5adf608012f3d9eb3c999c80977399 |
apache-2.0 | ['deep-narrow'] | false | Details model architecture This model checkpoint - **t5-efficient-base-el6** - is of model type **Base** with the following variations: - **el** is **6** It has **180.45** million parameters and thus requires *ca.* **721.8 MB** of memory in full precision (*fp32*) or **360.9 MB** of memory in half precision (*fp16... | 1eda830494f407f11f2eadccc670aa35 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_sa_GLUE_Experiment_logit_kd_qqp 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: 0.6308 - Accuracy: 0.6473 - F1: 0.0880 - Combined Score: 0.3676 | 823a4065edf5a5660e5306ea048b7980 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:| | 0.7821 | 1.0 | 1422 | 0.7485 | 0.6318 | 0.0 | 0.3159 | | 0.7105 | 2.0 | 2844 | ... | 9377be9c82af5f9a349dd89d8ad3a5cb |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-en 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: 0.4085 - F1: 0.6985 | 2ec85ef59cce4f204aaf527e9a445bed |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 1.1067 | 1.0 | 50 | 0.6303 | 0.4922 | | 0.5183 | 2.0 | 100 | 0.4321 | 0.6524 | | 0.3688 | 3.0 | 150 | 0.4085 | 0.6985 | ... | 48235f3284c6a971f5795ed7895981c1 |
apache-2.0 | ['generated_from_trainer'] | false | tiny-mlm-glue-cola-custom-tokenizer-expand-vocab-target-glue-cola This model is a fine-tuned version of [muhtasham/tiny-mlm-glue-cola-custom-tokenizer-expand-vocab](https://huggingface.co/muhtasham/tiny-mlm-glue-cola-custom-tokenizer-expand-vocab) on the None dataset. It achieves the following results on the evaluati... | 346dd9275737660da78989d04ec8bb8b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.6117 | 1.87 | 500 | 0.6224 | 0.0 | | 0.5987 | 3.73 | 1000 | 0.6217 | 0.0181 | | 0.5... | 37f87d6a821ea460376812d14bb9e661 |
mit | [] | false | This model has been pretrained on MS MARCO corpus and then finetuned on MS MARCO training data with implicit distributionally robust optimization (iDRO), following the approach described in the paper **COCO-DR: Combating Distribution Shifts in Zero-Shot Dense Retrieval with Contrastive and Distributionally Robust Lear... | b1c69143865555bf54c5c4c0db76e741 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Small Italian This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the mozilla-foundation/common_voice_11_0 it dataset. It achieves the following results on the evaluation set: - Loss: 0.2534 - Wer: 12.3040 | a6e892facce90d7f0b999ccb2f834767 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.2737 | 2.01 | 1000 | 0.2728 | 13.4097 | | 0.1536 | 4.02 | 2000 | 0.2611 | 12.9897 | | 0.0905 | 6.03 | 3000 | 0.2686 | 12.927... | d33bbf6e95bb17598128ffe3f690b3b1 |
mit | ['generated_from_trainer'] | false | BiBert-Classification-V2 This model is a fine-tuned version of [nlptown/bert-base-multilingual-uncased-sentiment](https://huggingface.co/nlptown/bert-base-multilingual-uncased-sentiment) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.7627 - Accuracy: 0.8180 | 4efd0f0a1969cea80138f196b719af23 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_ep... | 040036f06c8635bec1517e2061889c9c |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.8285 | 1.0 | 4290 | 0.8182 | 0.7934 | | 0.7496 | 2.0 | 8580 | 0.7750 | 0.8108 | | 0.6738 | 3.0 | 12870 | 0.7627 ... | c3f82c0bf302f907558abaa0b8975088 |
apache-2.0 | ['generated_from_keras_callback'] | false | distilbert1000e 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: | 2252d17596507d9a65b4f0c5fa352c8c |
mit | ['ja', 'japanese', 'gpt-neox', 'text-generation', 'lm', 'nlp'] | false | japanese-gpt-neox-small  This repository provides a small-sized Japanese GPT-NeoX model. The model was trained using code based on [EleutherAI/gpt-neox](https://github.com/EleutherAI/gpt-neox). | d0c7c213cbc76fbf40e939af652190ce |
mit | ['ja', 'japanese', 'gpt-neox', 'text-generation', 'lm', 'nlp'] | false | How to use the model *NOTE:* * Use `T5Tokenizer` to load its corresponding tokenizer. * The files for modeling and configuration are not in the Transformers library yet. In order to load the model, use files from [this PR in EleutherAI/gpt-neox](https://github.com/EleutherAI/gpt-neox/pull/480). ~~~~ from transformer... | 3a275e86694a2b421d51022eb96ec59c |
mit | ['ja', 'japanese', 'gpt-neox', 'text-generation', 'lm', 'nlp'] | false | Training The model was trained on [Japanese CC-100](http://data.statmt.org/cc-100/ja.txt.xz), [Japanese C4](https://huggingface.co/datasets/mc4), and [Japanese Wikipedia](https://dumps.wikimedia.org/other/cirrussearch) to optimize a traditional language modelling objective. | 52d40dc64355ee1eae0e82d3808c4a8e |
mit | ['ja', 'japanese', 'gpt-neox', 'text-generation', 'lm', 'nlp'] | false | A toy prefix-tuning weight file Along with pretrained model, we also release a [prefix-tuning](https://arxiv.org/abs/2101.00190) weight file named `smileface_suffix.task0.weight` for demonstration. The toy prefix-tuning weights here is trained to encourage the model to end every generated sentence with a smiling face ... | d99c6cb7a1027c8329b00ccaaa5b4b25 |
mit | ['ja', 'japanese', 'gpt-neox', 'text-generation', 'lm', 'nlp'] | false | Inference with FasterTransformer After version 5.1, [NVIDIA FasterTransformer](https://github.com/NVIDIA/FasterTransformer) now supports both inference for GPT-NeoX and a variety of soft prompts (including prefix-tuning). The released pretrained model and prefix weights in this repo have been verified to work with Fas... | 54ae838aacbfe027bf38e5162424a75c |
mit | ['vision', 'video-classification'] | false | X-CLIP (base-sized model) X-CLIP model (base-sized, patch resolution of 32) trained fully-supervised on [Kinetics-400](https://www.deepmind.com/open-source/kinetics). It was introduced in the paper [Expanding Language-Image Pretrained Models for General Video Recognition](https://arxiv.org/abs/2208.02816) by Ni et a... | a3b93e5fba02a1c23253482596f63e5a |
apache-2.0 | ['generated_from_keras_callback'] | false | juliietth/mt5-small-finetuned-amazon-en-es This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 5.9197 - Validation Loss: 3.6988 - Epoch: 1 | a893dbae6e25a1e78ac2259fcfeebaa4 |
apache-2.0 | ['summarization'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:-------:|:---------:|:-------:| | 1.2875 | 1.0 | 5754 | 1.6294 | 11.009 | 7.4618 | 10.5573 | 10.8087 | 58.3382 ... | 8b7d5f2bb9b40d32de8c907e8e40e3b6 |
mit | [] | false | Model description The Time Series Transformer is a vanilla encoder-decoder Transformer for time-series forecasting. The model is trained in the same way as one trains a Transformer for machine translation. At inference time, the model autoregressively generates samples, one time step at a time. | 5faa8d129de755d0c16738e0e37ed9ae |
apache-2.0 | [] | false | Tokenizer The *WordPiece* tokenizer uses several components: * **Normalization**: lowercase and then NFKD unicode normalization. * **Pretokenization**: splits by whitespace and punctuation. * **Postprocessing**: single sentences are output in format `[CLS] sentence A [SEP]` and pair sentences in format `[CLS] senten... | 97751473c3e5e4dbb68ee172fa15337b |
apache-2.0 | [] | false | Training Training was performed over 16M+ Dhivehi sentences/paragraphs put together by [@ashraq](https://huggingface.co/ashraq). An Adam optimizer with weighted decay was used with following parameters: * Learning rate: 1e-5 * Weight decay: 0.1 * Warmup steps: 10% of data | 8a73aef940cbf5d003c07ceec3c4472c |
apache-2.0 | ['token-classification'] | false | How to use ```python from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline model_name = "IlyaGusev/ru-word-stress-transformer" tokenizer = AutoTokenizer.from_pretrained( model_name, trust_remote_code=True, revision="bae83dd" ) model = AutoModelForTokenClassification.from_pretr... | 096f93b5542dda8a34f9b7a2a1cd65f7 |
apache-2.0 | [] | false | KeyBART KeyBART as described in "Learning Rich Representations of Keyphrase from Text" published in the Findings of NAACL 2022 (https://aclanthology.org/2022.findings-naacl.67.pdf), pre-trains a BART-based architecture to produce a concatenated sequence of keyphrases in the CatSeqD format. We provide some examples on... | 0961f8f733bf33e171891c968f886679 |
apache-2.0 | [] | false | Keyphrase Generation ``` from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("bloomberg/KeyBART") model = AutoModelForSeq2SeqLM.from_pretrained("bloomberg/KeyBART") from datasets import load_dataset dataset = load_dataset("midas/kp20k") ``` Reported Results: | f5d0738323a377115c44b058f6fca682 |
apache-2.0 | [] | false | Present Keyphrase Generation | | Inspec | | NUS | | Krapivin | | SemEval | | KP20k | | |---------------|--------|-------|-------|-------|----------|-------|---------|-------|-------|-------| | Model | F1@5 | F1@M | F1@5 | F1@M | F1@5 | F1@M | F1@5 | F1... | 8091b3e19ab2098d3382761c16fbd916 |
apache-2.0 | [] | false | Absent Keyphrase Generation | | Inspec | | NUS | | Krapivin | | SemEval | | KP20k | | |---------------|--------|------|------|------|----------|------|---------|------|-------|------| | Model | F1@5 | F1@M | F1@5 | F1@M | F1@5 | F1@M | F1@5 | F1@M | F1@5 | F1@M... | 9e0126a373cd4165d8f16d5bcec510f7 |
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