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 | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.12 | 1.0 | 1821 | 0.0543 | 0.8387 | 0.8577 | 0.8481 | 0.9830 | | 0.0381 | 2.0 |... | 870fd809c41a81a4f8b6d6d62354e73c |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Large-V2 Hungarian This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.2075 - Wer: 17.4533 | d451ad29f1dea5507704e71e6a94c4f7 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.1751 | 0.67 | 1000 | 0.2075 | 17.4533 | | 3893ae6e3b79defbd0960bb813ab2740 |
apache-2.0 | ['pythae', 'reproducibility'] | false | This model was trained with pythae. It can be downloaded or reloaded using the method `load_from_hf_hub` ```python >>> from pythae.models import AutoModel >>> model = AutoModel.load_from_hf_hub(hf_hub_path="clementchadebec/reproduced_hvae") ``` | a11bbec628d214dad2de885987c19872 |
apache-2.0 | ['pythae', 'reproducibility'] | false | Reproducibility This trained model reproduces the results of Table 1 in [1]. | Model | Dataset | Metric | Obtained value | Reference value | |:---:|:---:|:---:|:---:|:---:| | HVAE (n_lf=4) | Binary MNIST | NLL (1000 IS) | 86.21 (0.01) | 86.40 | [1] Samlimans, T. et al, *Markov chain monte carlo and variational infer... | ac51d5f7b88fe09b0121ec2fe9c1f82a |
apache-2.0 | ['generated_from_trainer'] | false | mrpc_bert-base-uncased_81_v2 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the GLUE MRPC dataset. It achieves the following results on the evaluation set: - Loss: 0.6390 - Accuracy: 0.8088 - F1: 0.8717 - Combined Score: 0.8403 | 7445755fe6f6149e25ef9eadc71a184a |
cc-by-sa-4.0 | ['ainu', 'masked-lm'] | false | Model Description This is a RoBERTa model pre-trained on Ainu texts written in カタカナ, Roman, and Кириллица. You can fine-tune `roberta-base-ainu` for downstream tasks, such as [POS-tagging](https://huggingface.co/KoichiYasuoka/roberta-base-ainu-upos), [dependency-parsing](https://huggingface.co/KoichiYasuoka/roberta-b... | 0d8ae5e32d69ac2ba6b8a024fafb7f7a |
cc-by-sa-4.0 | ['ainu', 'masked-lm'] | false | How to Use ```py from transformers import AutoTokenizer,AutoModelForMaskedLM tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/roberta-base-ainu") model=AutoModelForMaskedLM.from_pretrained("KoichiYasuoka/roberta-base-ainu") ``` | 4b3c3faeca2b10ed14671e016230eb32 |
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: 0.0631 - Precision: 0.9207 - Recall: 0.9352 - F1: 0.9279 - Accuracy: 0.9832 | 1c5eb5c1f571f096b0e0e54761b196b1 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.2399 | 1.0 | 878 | 0.0678 | 0.9097 | 0.9211 | 0.9154 | 0.9804 | | 0.0502 | 2.0 |... | e957a85119aaa69bd71ef4519684ecda |
apache-2.0 | ['automatic-speech-recognition', 'fr'] | false | exp_w2v2r_fr_xls-r_age_teens-2_sixties-8_s82 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 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure tha... | f10299050037c2e9d9ef083354f01b84 |
apache-2.0 | [] | false | This is RoBERTa model pretrained on texts in the Japanese language. 3.45GB wikipedia text trained 1.65M step use the sentencepiece tokenizer. If you want to fine-tune model. Please use ```python from transformers import BertTokenizer, RobertaModel BertTokenizer.from_pretrained('') RoBERTModel.from_pret... | 029e3fa28780dbb0cd6120c1c7fcf748 |
apache-2.0 | ['generated_from_trainer'] | false | text-to-sparql-t5-small-2021-10-19_10-17_lastDS This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2335 - Gen Len: 19.0 - P: 0.5580 - R: 0.0884 - F1: 0.3129 - Score: 5.9585 - Bleu-precisions: [90.113... | 51a8f378652c1adbccf53f36e22e34b7 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Gen Len | P | R | F1 | Score | Bleu-precisions | Bleu-bp | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:------:|:------:|:------:|:------------------... | d7bd087bddbbdad68edb2a4fbbd8dc66 |
apache-2.0 | ['generated_from_trainer'] | false | bart-large-asqa-cb This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.4791 - Rougelsum: 38.2862 | e7ea9cbcd0ad13567e6efba51368ebd2 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 - mixed_precision_training: Native AMP | 1030e24aec7eedb886d9c180e86c39cd |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:---------:| | 3.347 | 1.0 | 545 | 2.5353 | 37.3812 | | 2.7829 | 2.0 | 1090 | 2.5087 | 37.6431 | | 2.6973 | 3.0 | 1635 | 2.4906 ... | 2bdb3b7570eae07a010bed025981e626 |
apache-2.0 | ['generated_from_trainer'] | false | TSC_SentimentA_IMDBAmznTSC_2 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: 0.1985 - Accuracy: 0.9365 - F1: 0.9373 | 0b7429df3aad560fa59e4a4a9652a0d8 |
apache-2.0 | ['automatic-speech-recognition', 'ja'] | false | exp_w2v2t_ja_unispeech-sat_s635 Fine-tuned [microsoft/unispeech-sat-large](https://huggingface.co/microsoft/unispeech-sat-large) 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 your spee... | e83abb544f78415e639c069e814c86da |
apache-2.0 | ['automatic-speech-recognition', 'en'] | false | exp_w2v2r_en_xls-r_age_teens-2_sixties-8_s717 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 (en)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure th... | 5e0353ed6cc6ed460d3ce8d9ddaf9304 |
apache-2.0 | ['exbert', 'multiberts', 'multiberts-seed-0'] | false | MultiBERTs Seed 0 Checkpoint 80k (uncased) Seed 0 intermediate checkpoint 80k 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/googl... | 6a8020c8a1667547c1f0619e42b0bb13 |
apache-2.0 | ['exbert', 'multiberts', 'multiberts-seed-0'] | 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-0-80k') model = BertModel.from_pretrained("multiberts-seed-0-80k") text = "Replace me by any text you'd like." ... | 0be7da01059c03af2b85a63dc4772e45 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-ft1500_norm300_aug9 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: 1.0639 - Mse: 4.2557 - Mae: 1.3660 - R2: 0.4773 - Accuracy: 0.36... | 6760aad31cab76aeaa25f156c4681d3a |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 | 21a5aa0d910d1d8f1638c70392ec0d06 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Mse | Mae | R2 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:--------:| | 0.7595 | 1.0 | 3242 | 1.1009 | 4.4036 | 1.4148 | 0.4591 | 0.3440 | | 0.6024 | 2.0 | 6484 | 1... | 00456613c8d76e5912365665a12d2708 |
mit | [] | false | Test man on Stable Diffusion This is the `<Test-man>` 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 tra... | 918b51e7c4c43df0f8c81bd2b1e7e772 |
apache-2.0 | ['translation'] | false | zle-eng * source group: East Slavic languages * target group: English * OPUS readme: [zle-eng](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/zle-eng/README.md) * model: transformer * source language(s): bel bel_Latn orv_Cyrl rue rus ukr * target language(s): eng * model: transformer * pre-... | 901eb7863d496cb2f3ce962a46dac8e5 |
apache-2.0 | ['translation'] | false | Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | newstest2012-ruseng.rus.eng | 31.1 | 0.579 | | newstest2013-ruseng.rus.eng | 24.9 | 0.522 | | newstest2014-ruen-ruseng.rus.eng | 27.9 | 0.563 | | newstest2015-enru-ruseng.rus.eng | 26.8 | 0.541 | | newstest2016-en... | 6ca320b772bc285df237dfb16d4e4c52 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: zle-eng - source_languages: zle - target_languages: eng - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/zle-eng/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['be', 'ru', 'uk', 'zle', 'en'] - src_constituents: {... | a6e2928d08918f07533e08c909af6754 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | opus-mt-tc-big-ar-en Neural machine translation model for translating from Arabic (ar) to English (en). 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 models... | 2e964cc1d52edfadf0f66a85557d897f |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Model info * Release: 2022-03-09 * source language(s): afb ara arz * target language(s): eng * model: transformer-big * data: opusTCv20210807+bt ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge)) * tokenization: SentencePiece (spm32k,spm32k) * original model: [opusTCv20210807+bt_transformer-big_2022-03-09.... | 41ab8446a480a3668a55eb0201c1525d |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Usage A short example code: ```python from transformers import MarianMTModel, MarianTokenizer src_text = [ "اتبع قلبك فحسب.", "وين راهي دّوش؟" ] model_name = "pytorch-models/opus-mt-tc-big-ar-en" tokenizer = MarianTokenizer.from_pretrained(model_name) model = MarianMTModel.from_pretrained(model_name) trans... | 2ed804206f25d6e2e5dbc94098d21d43 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Wayne Rahi Dosh? ``` 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-ar-en") print(pipe("اتبع قلبك فحسب.")) | c540569afedb90d34e2176c664c060e0 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Benchmarks * test set translations: [opusTCv20210807+bt_transformer-big_2022-03-09.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/ara-eng/opusTCv20210807+bt_transformer-big_2022-03-09.test.txt) * test set scores: [opusTCv20210807+bt_transformer-big_2022-03-09.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-... | a0aae3b7d67db573f800b359a0d9a4aa |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | words | |----------|---------|-------|-------|-------|--------| | ara-eng | tatoeba-test-v2021-08-07 | 0.63477 | 47.3 | 10305 | 76975 | | ara-eng | flores101-devtest | 0.66987 | 42.6 | 1012 | 24721 | | ara-eng | tico19-test | 0.68521 | 44.4 | 2100 | 56323 | | 97d13c26f7fb98e0da8a57078136e455 |
apache-2.0 | ['distigpt2', 'hearthstone'] | false | h2 This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on [hearthstone](https://huggingface.co/datasets/dvitel/hearthstone). [GitHub repo](https://github.com/dvitel/nlp-sem-parsing/blob/master/h2.py). It achieves the following results on the evaluation set: - Loss: 2.5771 - Exact Mat... | c9ffb9ce16c7c8efd23e4fb0ebd23857 |
apache-2.0 | ['distigpt2', 'hearthstone'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 17 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 200 - mixed_precision_training: Native AMP | 92c3326450cc68c0b0d5e627f57b3c1d |
apache-2.0 | ['distigpt2', 'hearthstone'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Exact Match | Bleu | Codebleu | Ngram Match Score | Weighted Ngram Match Score | Syntax Match Score | Dataflow Match Score | Chrf | |:-------------:|:------:|:-----:|:---------------:|:-----------:|:------:|:--------:|:-----------------:|:----... | b339593aa8e0806fde502486ffc967dd |
apache-2.0 | ['generated_from_trainer'] | false | BERT_MC_OpenBookQA_w_wrong_context This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.7450 - Accuracy: 0.922 | 7fbe499dd7b47b24a521b8ab1baf690f |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 11 | 4dbc01f609d58df4521fb5fdbabadd87 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.3525 | 1.0 | 1859 | 0.2696 | 0.906 | | 0.2084 | 2.0 | 3718 | 0.3284 | 0.9143 | | 0.1263 | 3.0 | 5577 | 0.4205 ... | 81a7467d291453895c949a9fdb52421c |
mit | ['generated_from_trainer'] | false | roberta-lora-2 This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5790 - Mse: 0.5790 - Mae: 0.5751 - R2: 0.5572 - Accuracy: 0.5465 | 3307e3c0d8ea2f5b53a40e633b57649b |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 | 3886c2e1783605d972186ebc40241480 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Mse | Mae | R2 | Accuracy | |:-------------:|:-----:|:------:|:---------------:|:------:|:------:|:------:|:--------:| | 0.9268 | 0.02 | 2500 | 0.7467 | 0.7467 | 0.6737 | 0.4290 | 0.4621 | | 0.7651 | 0.05 | 50... | c25932dddadbfb1d1d605e1fa7e96d05 |
apache-2.0 | ['generated_from_keras_callback'] | false | nila-yuki/final_lab 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: 0.0240 - Validation Loss: 0.0593 - Epoch: 2 | 0a870d479a095244ed51f729f4d9ced5 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 1017, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay':... | a33afe2df42857b2692e808f5b119d7f |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 0.1059 | 0.0572 | 0 | | 0.0391 | 0.0542 | 1 | | 0.0240 | 0.0593 | 2 | | ef3b6b9e8a50b9f4dec17ae2ae884796 |
apache-2.0 | ['automatic-speech-recognition', 'fr'] | false | exp_w2v2r_fr_xls-r_age_teens-2_sixties-8_s598 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 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure th... | 26a903c56f73c59978ce1995d78ac23e |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-timit-demo-google-colab This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5173 - Wer: 0.3399 | 494ab5bac8de52038c6a1fcc540998d3 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 3.5684 | 1.0 | 500 | 2.1662 | 1.0068 | | 0.9143 | 2.01 | 1000 | 0.5820 | 0.5399 | | 0.439 | 3.01 | 1500 | 0.4596 | 0.458... | 87b9407683c925eedd50d49553153dca |
mit | ['generated_from_keras_callback'] | false | juro95/xlm-roberta-finetuned-ner-cased_1_ratio This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0633 - Validation Loss: 0.0940 - Epoch: 3 | 3c5385dfc3074e1e165f8dc206c8a87c |
mit | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 14272, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay'... | 7ca7dc2d7d39d4b0f7f876ecfd392776 |
mit | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 0.3166 | 0.1533 | 0 | | 0.1376 | 0.1114 | 1 | | 0.0909 | 0.0988 | 2 | | 0.0633 | 0.0940 | 3 | | 915672459f9e1975488143aafdf12848 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-timit-demo-colab-3 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: 0.6622 - Wer: 0.5082 | 05b896b83ad688c0ab2645556809fc44 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 10 - 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: 800 - num_epochs: 35 - mixed_precision_tr... | dd88d481bdf7e09749e6cd2eae46cf8e |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 2.2195 | 8.77 | 500 | 0.9187 | 0.6635 | | 0.5996 | 17.54 | 1000 | 0.6569 | 0.5347 | | 0.2855 | 26.32 | 1500 | 0.6622 | 0.5082 | ... | 4e28410a67329170641b28746f639315 |
mit | ['generated_from_trainer'] | false | wmt-ptt5-colab-base-finetuned-en-to-pt This model is a fine-tuned version of [unicamp-dl/ptt5-base-portuguese-vocab](https://huggingface.co/unicamp-dl/ptt5-base-portuguese-vocab) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.9254 - Bleu: 43.9392 - Gen Len: 13.3303 | 1a5d95bd748a1769ac39d97d43df6813 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 - mixed_precision_training: Native AMP | b997c0a5b90aeac05764743f517f1190 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:| | 3.3376 | 1.0 | 109 | 1.8592 | 14.7486 | 13.552 | | 1.8331 | 2.0 | 218 | 1.4492 | 21.0901 | 13.2979 | | 1.4577 |... | 49a792cb272e36e3cee70f623f9ba634 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Small Basque 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 eu dataset. It achieves the following results on the evaluation set: - Loss: 0.3580 - Wer: 18.9337 | 340782db6dd22d8e186c4aabe2fd695f |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.1372 | 2.04 | 1000 | 0.3166 | 22.2335 | | 0.0175 | 4.07 | 2000 | 0.3356 | 19.9862 | | 0.0055 | 7.02 | 3000 | 0.3580 | 18.933... | a772f82b36bc2f903cf0ead9572fdef3 |
mit | ['vision', 'image-segmentation', 'universal-image-segmentation'] | false | OneFormer OneFormer model trained on the ADE20k dataset (large-sized version, Swin backbone). It was introduced in the paper [OneFormer: One Transformer to Rule Universal Image Segmentation](https://arxiv.org/abs/2211.06220) by Jain et al. and first released in [this repository](https://github.com/SHI-Labs/OneFormer)... | 4e3674b2fcbf733e04a088520716be0c |
mit | ['vision', 'image-segmentation', 'universal-image-segmentation'] | false | Model description OneFormer is the first multi-task universal image segmentation framework. It needs to be trained only once with a single universal architecture, a single model, and on a single dataset, to outperform existing specialized models across semantic, instance, and panoptic segmentation tasks. OneFormer us... | 8474d413418c5e4bde3a1ffe9fbd7679 |
mit | ['vision', 'image-segmentation', 'universal-image-segmentation'] | false | Intended uses & limitations You can use this particular checkpoint for semantic, instance and panoptic segmentation. See the [model hub](https://huggingface.co/models?search=oneformer) to look for other fine-tuned versions on a different dataset. | f3b8f307f7cbd9f7f473d3906e580734 |
mit | ['vision', 'image-segmentation', 'universal-image-segmentation'] | false | How to use Here is how to use this model: ```python from transformers import OneFormerProcessor, OneFormerForUniversalSegmentation from PIL import Image import requests url = "https://huggingface.co/datasets/shi-labs/oneformer_demo/blob/main/ade20k.jpeg" image = Image.open(requests.get(url, stream=True).raw) | b2db39e5c4d4ab5995e07d7ddb2e8d42 |
mit | ['vision', 'image-segmentation', 'universal-image-segmentation'] | false | Loading a single model for all three tasks processor = OneFormerProcessor.from_pretrained("shi-labs/oneformer_ade20k_swin_large") model = OneFormerForUniversalSegmentation.from_pretrained("shi-labs/oneformer_ade20k_swin_large") | 0a58057a98b45f433eed02399d8725f0 |
mit | ['vision', 'image-segmentation', 'universal-image-segmentation'] | false | pass through image_processor for postprocessing predicted_semantic_map = processor.post_process_panoptic_segmentation(outputs, target_sizes=[image.size[::-1]])[0]["segmentation"] ``` For more examples, please refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/oneformer). | 6b929304ecf781dbf7ee10907454b277 |
mit | ['vision', 'image-segmentation', 'universal-image-segmentation'] | false | Citation ```bibtex @article{jain2022oneformer, title={{OneFormer: One Transformer to Rule Universal Image Segmentation}}, author={Jitesh Jain and Jiachen Li and MangTik Chiu and Ali Hassani and Nikita Orlov and Humphrey Shi}, journal={arXiv}, year={2022} } ``` | 835be333ac0d8cc8fb05f2fe90ba62d6 |
cc-by-4.0 | ['question generation'] | false | Model Card of `lmqg/mbart-large-cc25-ruquad-qg` This model is fine-tuned version of [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) for question generation task on the [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) (dataset_name: default) via [`lmqg`](https://github.com/... | 46024ca39577606afdcb549e7ca9179a |
cc-by-4.0 | ['question generation'] | false | Overview - **Language model:** [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) - **Language:** ru - **Training data:** [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) (default) - **Online Demo:** [https://autoqg.net/](https://autoqg.net/) - **Repository:** [https://g... | 6cc51518dee14cc1d5b4f9c8e41112d2 |
cc-by-4.0 | ['question generation'] | false | model prediction questions = model.generate_q(list_context="Нелишним будет отметить, что, развивая это направление, Д. И. Менделеев, поначалу априорно выдвинув идею о температуре, при которой высота мениска будет нулевой, в мае 1860 года провёл серию опытов.", list_answer="в мае 1860 года") ``` - With `transformers`... | 3ce3607ef73ba16ecf9af50f15150475 |
cc-by-4.0 | ['question generation'] | false | Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/lmqg/mbart-large-cc25-ruquad-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_ruquad.default.json) | | Score | Type | Dataset | ... | 00e8fdbe335d3be4321213fca7d8f1d8 |
cc-by-4.0 | ['question generation'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_ruquad - dataset_name: default - input_types: ['paragraph_answer'] - output_types: ['question'] - prefix_types: None - model: facebook/mbart-large-cc25 - max_length: 512 - max_length_output: 32 - epoc... | 800f2c5c7c14cfdeb908d781037b758f |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-multilingual-cased-tuned-smartcat This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0000 | e6c3e7de06cc656c7402c67fc3aee9eb |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 0.0006 | 1.0 | 11586 | 0.0000 | | 0.0003 | 2.0 | 23172 | 0.0000 | | 0.0 | 3.0 | 34806 | 0.0000 | | df8586b5b8a2ed46cbd1c3bf0a2312d0 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 3.3558 | 1.0 | 23769 | 3.2316 | | 3.2558 | 2.0 | 47538 | 3.1683 | | 3.2321 | 3.0 | 71307 | 3.1516 | | 0fb0d97dedcccb72a87492512b017b79 |
apache-2.0 | ['hf-asr-leaderboard', 'whisper-medium', 'mozilla-foundation/common_voice_11_0', 'greek', 'whisper-event', 'generated_from_trainer', 'whisper-event'] | false | Whisper Medium El Greco This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - eval_loss: 0.4245 - eval_wer: 10.7448 - eval_runtime: 1107.1212 - eval_samples_per_second: 1.... | 3a2dea56d94768b8ab27aab008998ded |
apache-2.0 | ['hf-asr-leaderboard', 'whisper-medium', 'mozilla-foundation/common_voice_11_0', 'greek', 'whisper-event', 'generated_from_trainer', 'whisper-event'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 16 - 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: 7000 - mixed_precis... | 01c8cfe9af5da4247ee0f7d29fdf33d0 |
mit | [] | false | hitokomoru-style Artist: <https://www.pixiv.net/en/users/30837811> This is the `<hitokomoru-style>` 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_conceptuali... | 2efa3a54c7974ea1f7d66cc31baca825 |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | UD v2.5 benchmarking pipeline for UD_Korean-Kaist | Feature | Description | | --- | --- | | **Name** | `ko_udv25_koreankaist_trf` | | **Version** | `0.0.1` | | **spaCy** | `>=3.2.1,<3.3.0` | | **Default Pipeline** | `experimental_char_ner_tokenizer`, `transformer`, `tagger`, `morphologizer`, `parser`, `experimental_ed... | 207104f65bdd06f5e602e0f86de927d1 |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | Label Scheme <details> <summary>View label scheme (5329 labels for 6 components)</summary> | Component | Labels | | --- | --- | | **`experimental_char_ner_tokenizer`** | `TOKEN` | | **`senter`** | `I`, `S` | | **`tagger`** | `ecs`, `etm`, `f`, `f+f+jcj`, `f+f+jcs`, `f+f+jct`, `f+f+jxt`, `f+jca`, `f+jca+jp+ecc`, `f+... | 4bb7a54f828513bad0cfc9e41e2f6388 |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | Accuracy | Type | Score | | --- | --- | | `TOKEN_F` | 100.00 | | `TOKEN_P` | 100.00 | | `TOKEN_R` | 100.00 | | `TOKEN_ACC` | 100.00 | | `SENTS_F` | 100.00 | | `SENTS_P` | 100.00 | | `SENTS_R` | 100.00 | | `TAG_ACC` | 88.93 | | `POS_ACC` | 96.52 | | `MORPH_ACC` | 100.00 | | `MORPH_PER_FEAT` | 0.00 | | `DEP_UAS` | 89.4... | 9608075264308a63c768079d8a423335 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.2761 - F1: 0.8350 | 0933c9ff1174c331bd150a0699454177 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.5826 | 1.0 | 191 | 0.3409 | 0.7713 | | 0.2674 | 2.0 | 382 | 0.2889 | 0.8314 | | 0.1738 | 3.0 | 573 | 0.2761 | 0.8350 | ... | 617ff9e2e59b9e5331d5784cff331e10 |
apache-2.0 | ['generated_from_keras_callback'] | false | keras-io/sentiment-analysis This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.6865 - Validation Loss: 0.7002 - Train Accuracy: 0.4908 - Epoch: 4 | c5befc24ed51c58085bb9e0bb344756b |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'learning_rate': 1e-04, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} - training_precision: float32 | 04b7527dc75bb92175209a1c17e57115 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Train Accuracy | Epoch | |:----------:|:---------------:|:--------------:|:-----:| | 0.6865 | 0.6975 | 0.4908 | 0 | | 0.6865 | 0.6973 | 0.4908 | 1 | | 0.6865 | 0.6976 | 0.4908 | 2 | | 0.6865 ... | dcc65b6f60175a2516a3f37c2ded97b7 |
creativeml-openrail-m | ['text-to-image'] | false | m123ugg Dreambooth model trained by duja1 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/huggingface/notebooks/blob/... | 9eaf389bbc94b87b4663afa8c3685770 |
apache-2.0 | ['text generation', 'pytorch', 'the Pile', 'causal-lm'] | false | Model Description GPT-Neo 2.7B is a transformer model designed using EleutherAI's replication of the GPT-3 architecture. GPT-Neo refers to the class of models, while 2.7B represents the number of parameters of this particular pre-trained model. | f82c4066d4f22ea25623181a7f36bcd0 |
apache-2.0 | ['text generation', 'pytorch', 'the Pile', 'causal-lm'] | false | Intended Use and Limitations This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks. The model is best at what it was pretrained for however, which is generating texts from a prompt. | e03a8926528cf912b05fff9bbcb94917 |
apache-2.0 | ['text generation', 'pytorch', 'the Pile', 'causal-lm'] | false | How to use You can use this model directly with a pipeline for text generation. This example generates a different sequence each time it's run: ```py >>> from transformers import pipeline >>> generator = pipeline('text-generation', model='EleutherAI/gpt-neo-2.7B') >>> generator("EleutherAI has", do_sample=True, min_le... | c461ccc14a7480f4709e7b33613c5d59 |
apache-2.0 | ['text generation', 'pytorch', 'the Pile', 'causal-lm'] | false | Limitations and Biases GPT-Neo was trained as an autoregressive language model. This means that its core functionality is taking a string of text and predicting the next token. While language models are widely used for tasks other than this, there are a lot of unknowns with this work. GPT-Neo was trained on the Pile, ... | 1da124631288e28f61916df2df0861fb |
apache-2.0 | ['text generation', 'pytorch', 'the Pile', 'causal-lm'] | false | Eval results All evaluations were done using our [evaluation harness](https://github.com/EleutherAI/lm-evaluation-harness). Some results for GPT-2 and GPT-3 are inconsistent with the values reported in the respective papers. We are currently looking into why, and would greatly appreciate feedback and further testing o... | bdf6e6de44cb49c2a232a6b8ec1643bc |
apache-2.0 | ['text generation', 'pytorch', 'the Pile', 'causal-lm'] | false | Linguistic Reasoning | Model and Size | Pile BPB | Pile PPL | Wikitext PPL | Lambada PPL | Lambada Acc | Winogrande | Hellaswag | | ---------------- | ---------- | ---------- | ------------- | ----------- | ----------- | ---------- | ----------- | | GPT-Neo 1.3B | 0.7527 | 6.159 | 13.10 |... | 706c8152a9359456fe3df25e2b0a244a |
apache-2.0 | ['text generation', 'pytorch', 'the Pile', 'causal-lm'] | false | Physical and Scientific Reasoning | Model and Size | MathQA | PubMedQA | Piqa | | ---------------- | ---------- | ---------- | ----------- | | GPT-Neo 1.3B | 24.05% | 54.40% | 71.11% | | GPT-2 1.5B | 23.64% | 58.33% | 70.78% | | **GPT-Neo 2.7B** | **24.72%** | **57.54%** ... | f0e3e8e87d718c31e05d51bc2af659cf |
apache-2.0 | ['text generation', 'pytorch', 'the Pile', 'causal-lm'] | false | BibTeX entry and citation info To cite this model, use ```bibtex @article{gao2020pile, title={The Pile: An 800GB Dataset of Diverse Text for Language Modeling}, author={Gao, Leo and Biderman, Stella and Black, Sid and Golding, Laurence and Hoppe, Travis and Foster, Charles and Phang, Jason and He, Horace and Thite... | daa026dce38ef597dc0671e9b4cf9407 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image', 'safetensors'] | false | PVC v2  ``` masterpiece, best quality, high quality, 1girl, cat ears, silver, blue, frills, bow, looking at viewer, ultra detailed Negative prompt: nsfw, worst quality, low quality, medium quality, deleted, lowres, bad anatomy, b... | 7677b3fded3ca72f5b883ec3d194c9b3 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image', 'safetensors'] | false | Model links - [**pvc-v2.safetensors**](https://huggingface.co/p1atdev/pvc/resolve/main/pvc-v2.safetensors) - [**pvc-v2.ckpt**](https://huggingface.co/p1atdev/pvc/resolve/main/pvc-v2.ckpt) - [pvc-v2.yaml](https://huggingface.co/p1atdev/pvc/blob/main/pvc-v2.yaml) (needed if you want to use the model in AUTOMATIC1111's ... | 12dc369d61c101b784bcb5c700f7f096 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image', 'safetensors'] | false | Prompt guide It is recommended to add the quality tags **"masterpiece, best quality"** at the beginning of the prompt when using this model, which is a derivative of the WD. **Recommended negative prompt** ``` nsfw, worst quality, low quality, medium quality, deleted, lowres, bad anatomy, bad hands, text, error, mis... | c3085962b53ef63872e6814695e6b1d6 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image', 'safetensors'] | false | Samples  ``` masterpiece, best quality, 1girl, green hair, sweater, beanie, turtleneck, looking at viewer, night, Negative prompt: nsfw, worst quality, low quality, medium quality, deleted, lowres, bad anatomy, bad hands, text, err... | 54c2a0d7e814874f5e666c9edc9e4e11 |
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