license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1 class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:| | No log | 1.0 | 23 | 1.1760 | 54.8264 | 32.0931 | 40.5826 | 52.2503 | 99.4505 | | No log | 2.0 | 46 | 0.9005 | 59.7325 | 38.3487 | 45.8861 | 56.9922 | 108.3846 | | No log | 3.0 | 69 | 0.8053 | 62.0348 | 41.9592 | 49.1046 | 59.4965 | 101.2747 | | 468e540b86f3129fc5bcaaaa20110a06 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 3 | 49856221bb4eda89e23087b8fea620c2 |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-uncased-finetuned-fin 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: 0.3931 - Accuracy: 0.8873 - F1: 0.8902 | 563e1de50be0b602e14a26d2b37ffde5 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.6478 | 1.0 | 134 | 0.4118 | 0.8293 | 0.8309 | | 0.3304 | 2.0 | 268 | 0.3315 | 0.8653 | 0.8694 | | 0.2221 | 3.0 | 402 | 0.3229 | 0.8756 | 0.8781 | | 0.1752 | 4.0 | 536 | 0.3192 | 0.8891 | 0.8921 | | 0.1457 | 5.0 | 670 | 0.3700 | 0.8840 | 0.8880 | | 0.1315 | 6.0 | 804 | 0.3774 | 0.8854 | 0.8882 | | 0.1172 | 7.0 | 938 | 0.3883 | 0.8849 | 0.8877 | | 0.112 | 8.0 | 1072 | 0.3931 | 0.8873 | 0.8902 | | 35ea70bdb77ddeb4abe5cf89852245c5 |
cc0-1.0 | ['kaggle'] | false | PreTraining | **Architecture** | **Weights** | **PreTraining Loss** | **PreTraining Perplexity** | |:-----------------------:|:---------------:|:----------------:|:----------------------:| | roberta-base | [huggingface/hub](https://huggingface.co/SauravMaheshkar/clr-pretrained-roberta-base) | **0.3488** | **3.992** | | bert-base-uncased | [huggingface/hub](https://huggingface.co/SauravMaheshkar/clr-pretrained-bert-base-uncased) | 0.3909 | 6.122 | | electra-large | [huggingface/hub](https://huggingface.co/SauravMaheshkar/clr-pretrained-electra-large) | 0.723 | 6.394 | | albert-base | [huggingface/hub](https://huggingface.co/SauravMaheshkar/clr-pretrained-albert-base) | 0.7343 | 7.76 | | electra-small | [huggingface/hub](https://huggingface.co/SauravMaheshkar/clr-pretrained-electra-small) | 0.9226 | 11.098 | | electra-base | [huggingface/hub](https://huggingface.co/SauravMaheshkar/clr-pretrained-electra-base) | 0.9468 | 8.783 | | distilbert-base-uncased | [huggingface/hub](https://huggingface.co/SauravMaheshkar/clr-pretrained-distilbert-base-uncased) | 1.082 | 7.963 | | f955f7060e3d2344fe06ee2f42aa5b78 |
apache-2.0 | ['translation'] | false | opus-mt-lue-fi * source languages: lue * target languages: fi * OPUS readme: [lue-fi](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/lue-fi/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-09.zip](https://object.pouta.csc.fi/OPUS-MT-models/lue-fi/opus-2020-01-09.zip) * test set translations: [opus-2020-01-09.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/lue-fi/opus-2020-01-09.test.txt) * test set scores: [opus-2020-01-09.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/lue-fi/opus-2020-01-09.eval.txt) | ae2dc91303edd07f49e7b97e992f2ae3 |
apache-2.0 | ['translation'] | false | zho-bul * source group: Chinese * target group: Bulgarian * OPUS readme: [zho-bul](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/zho-bul/README.md) * model: transformer * source language(s): cmn cmn_Hans cmn_Hant zho zho_Hans zho_Hant * target language(s): bul * model: transformer * pre-processing: normalization + SentencePiece (spm32k,spm32k) * download original weights: [opus-2020-07-03.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/zho-bul/opus-2020-07-03.zip) * test set translations: [opus-2020-07-03.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zho-bul/opus-2020-07-03.test.txt) * test set scores: [opus-2020-07-03.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zho-bul/opus-2020-07-03.eval.txt) | c65a6c953ed6aa90b36115aa6b3eb257 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: zho-bul - source_languages: zho - target_languages: bul - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/zho-bul/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['zh', 'bg'] - src_constituents: {'cmn_Hans', 'nan', 'nan_Hani', 'gan', 'yue', 'cmn_Kana', 'yue_Hani', 'wuu_Bopo', 'cmn_Latn', 'yue_Hira', 'cmn_Hani', 'cjy_Hans', 'cmn', 'lzh_Hang', 'lzh_Hira', 'cmn_Hant', 'lzh_Bopo', 'zho', 'zho_Hans', 'zho_Hant', 'lzh_Hani', 'yue_Hang', 'wuu', 'yue_Kana', 'wuu_Latn', 'yue_Bopo', 'cjy_Hant', 'yue_Hans', 'lzh', 'cmn_Hira', 'lzh_Yiii', 'lzh_Hans', 'cmn_Bopo', 'cmn_Hang', 'hak_Hani', 'cmn_Yiii', 'yue_Hant', 'lzh_Kana', 'wuu_Hani'} - tgt_constituents: {'bul', 'bul_Latn'} - src_multilingual: False - tgt_multilingual: False - prepro: normalization + SentencePiece (spm32k,spm32k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/zho-bul/opus-2020-07-03.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/zho-bul/opus-2020-07-03.test.txt - src_alpha3: zho - tgt_alpha3: bul - short_pair: zh-bg - chrF2_score: 0.49700000000000005 - bleu: 29.6 - brevity_penalty: 0.883 - ref_len: 3113.0 - src_name: Chinese - tgt_name: Bulgarian - train_date: 2020-07-03 - src_alpha2: zh - tgt_alpha2: bg - prefer_old: False - long_pair: zho-bul - helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535 - transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b - port_machine: brutasse - port_time: 2020-08-21-14:41 | a03f977d5dce4bc532e6b1a8ee4305cd |
apache-2.0 | ['generated_from_trainer'] | false | distilroberta-base-finetuned-billy-ray-cyrus 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: 2.6282 | 6101d222f363711faf98a9c42e743010 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 47 | 2.5714 | | No log | 2.0 | 94 | 2.5574 | | No log | 3.0 | 141 | 2.6282 | | 2cac3a86fd3975fdf3dd1b04f1b9a078 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-emotion-test-01 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 1.7510 - Accuracy: 0.39 - F1: 0.2188 | 2b43e7cd168be2349791f9273c938285 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 2 | 1.7634 | 0.39 | 0.2188 | | No log | 2.0 | 4 | 1.7510 | 0.39 | 0.2188 | | e90790ba3f69433cdbc28980261b386d |
apache-2.0 | ['generated_from_trainer'] | false | MTL-bert-base-uncased 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: 1.9283 | b63cd9f7d95edce6b0a093cd9bb54d61 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.4409 | 1.0 | 99 | 2.1982 | | 2.2905 | 2.0 | 198 | 2.1643 | | 2.1974 | 3.0 | 297 | 2.1168 | | 2.15 | 4.0 | 396 | 2.0023 | | 2.0823 | 5.0 | 495 | 2.0199 | | 2.0752 | 6.0 | 594 | 1.9061 | | 2.0408 | 7.0 | 693 | 1.9770 | | 1.9984 | 8.0 | 792 | 1.9322 | | 1.9933 | 9.0 | 891 | 1.9167 | | 1.9806 | 10.0 | 990 | 1.9652 | | 1.9436 | 11.0 | 1089 | 1.9308 | | 1.9491 | 12.0 | 1188 | 1.9064 | | 1.929 | 13.0 | 1287 | 1.8831 | | 1.9096 | 14.0 | 1386 | 1.8927 | | 1.9032 | 15.0 | 1485 | 1.9117 | | d7ba2689931aef12dd607a37eee7ad54 |
apache-2.0 | ['generated_from_trainer'] | false | all-roberta-large-v1-auto_and_commute-7-16-5 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: 2.2614 - Accuracy: 0.4289 | 8879afd03527bb37848aeb85d8d5107a |
apache-2.0 | [] | false | ALBERT Large v1 Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in [this paper](https://arxiv.org/abs/1909.11942) and first released in [this repository](https://github.com/google-research/albert). This model, as all ALBERT models, is uncased: it does not make a difference between english and English. Disclaimer: The team releasing ALBERT did not write a model card for this model so this model card has been written by the Hugging Face team. | f8e179b0bb50c81aa107a5266e11138d |
apache-2.0 | [] | false | Model description ALBERT is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it was pretrained with two objectives: - Masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict the masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of the sentence. - Sentence Ordering Prediction (SOP): ALBERT uses a pretraining loss based on predicting the ordering of two consecutive segments of text. This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard classifier using the features produced by the ALBERT model as inputs. ALBERT is particular in that it shares its layers across its Transformer. Therefore, all layers have the same weights. Using repeating layers results in a small memory footprint, however, the computational cost remains similar to a BERT-like architecture with the same number of hidden layers as it has to iterate through the same number of (repeating) layers. This is the first version of the large model. Version 2 is different from version 1 due to different dropout rates, additional training data, and longer training. It has better results in nearly all downstream tasks. This model has the following configuration: - 24 repeating layers - 128 embedding dimension - 1024 hidden dimension - 16 attention heads - 17M parameters | d3e1d5b915a021922e317c9386bdd5aa |
apache-2.0 | [] | false | How to use You can use this model directly with a pipeline for masked language modeling: ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='albert-large-v1') >>> unmasker("Hello I'm a [MASK] model.") [ { "sequence":"[CLS] hello i'm a modeling model.[SEP]", "score":0.05816134437918663, "token":12807, "token_str":"â–modeling" }, { "sequence":"[CLS] hello i'm a modelling model.[SEP]", "score":0.03748830780386925, "token":23089, "token_str":"â–modelling" }, { "sequence":"[CLS] hello i'm a model model.[SEP]", "score":0.033725276589393616, "token":1061, "token_str":"â–model" }, { "sequence":"[CLS] hello i'm a runway model.[SEP]", "score":0.017313428223133087, "token":8014, "token_str":"â–runway" }, { "sequence":"[CLS] hello i'm a lingerie model.[SEP]", "score":0.014405295252799988, "token":29104, "token_str":"â–lingerie" } ] ``` Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import AlbertTokenizer, AlbertModel tokenizer = AlbertTokenizer.from_pretrained('albert-large-v1') model = AlbertModel.from_pretrained("albert-large-v1") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import AlbertTokenizer, TFAlbertModel tokenizer = AlbertTokenizer.from_pretrained('albert-large-v1') model = TFAlbertModel.from_pretrained("albert-large-v1") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` | 1175b48d0d996fccae7d6180da739397 |
apache-2.0 | [] | false | Limitations and bias Even if the training data used for this model could be characterized as fairly neutral, this model can have biased predictions: ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='albert-large-v1') >>> unmasker("The man worked as a [MASK].") [ { "sequence":"[CLS] the man worked as a chauffeur.[SEP]", "score":0.029577180743217468, "token":28744, "token_str":"â–chauffeur" }, { "sequence":"[CLS] the man worked as a janitor.[SEP]", "score":0.028865724802017212, "token":29477, "token_str":"â–janitor" }, { "sequence":"[CLS] the man worked as a shoemaker.[SEP]", "score":0.02581118606030941, "token":29024, "token_str":"â–shoemaker" }, { "sequence":"[CLS] the man worked as a blacksmith.[SEP]", "score":0.01849772222340107, "token":21238, "token_str":"â–blacksmith" }, { "sequence":"[CLS] the man worked as a lawyer.[SEP]", "score":0.01820771023631096, "token":3672, "token_str":"â–lawyer" } ] >>> unmasker("The woman worked as a [MASK].") [ { "sequence":"[CLS] the woman worked as a receptionist.[SEP]", "score":0.04604868218302727, "token":25331, "token_str":"â–receptionist" }, { "sequence":"[CLS] the woman worked as a janitor.[SEP]", "score":0.028220869600772858, "token":29477, "token_str":"â–janitor" }, { "sequence":"[CLS] the woman worked as a paramedic.[SEP]", "score":0.0261906236410141, "token":23386, "token_str":"â–paramedic" }, { "sequence":"[CLS] the woman worked as a chauffeur.[SEP]", "score":0.024797942489385605, "token":28744, "token_str":"â–chauffeur" }, { "sequence":"[CLS] the woman worked as a waitress.[SEP]", "score":0.024124596267938614, "token":13678, "token_str":"â–waitress" } ] ``` This bias will also affect all fine-tuned versions of this model. | a09ca6bb2e9b0c8bef0365a236dec59e |
apache-2.0 | ['generated_from_trainer'] | false | roberta-base-bne-finetuned-amazon_reviews_multi This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/roberta-base-bne) on the amazon_reviews_multi dataset. | 02f247211b242bbd93638dcdf7ce45dc |
mit | [] | false | Info >Model Used: Waifu Diffusion 1.2 >Steps: 3000 >Keyword: SAKURA-SABER (Use this in the prompt) >Class Phrase: 1girl_short_blonde_hair_black_scarf_blue_yukata_anime  | 3ca0afd7458bc97e21257178982f6cb9 |
mit | [] | false | 🇹🇷 BERTurk BERTurk is a community-driven cased BERT model for Turkish. Some datasets used for pretraining and evaluation are contributed from the awesome Turkish NLP community, as well as the decision for the model name: BERTurk. | 60551987190efa8bc6a22ed52fb96f2b |
mit | [] | false | Stats The current version of the model is trained on a filtered and sentence segmented version of the Turkish [OSCAR corpus](https://traces1.inria.fr/oscar/), a recent Wikipedia dump, various [OPUS corpora](http://opus.nlpl.eu/) and a special corpus provided by [Kemal Oflazer](http://www.andrew.cmu.edu/user/ko/). The final training corpus has a size of 35GB and 44,04,976,662 tokens. Thanks to Google's TensorFlow Research Cloud (TFRC) we could train a cased model on a TPU v3-8 for 2M steps. | 6e66babe87bd2376ee1aa259645f7a17 |
mit | [] | false | Model weights Currently only PyTorch-[Transformers](https://github.com/huggingface/transformers) compatible weights are available. If you need access to TensorFlow checkpoints, please raise an issue! | Model | Downloads | --------------------------------- | --------------------------------------------------------------------------------------------------------------- | `dbmdz/bert-base-turkish-cased` | [`config.json`](https://cdn.huggingface.co/dbmdz/bert-base-turkish-cased/config.json) • [`pytorch_model.bin`](https://cdn.huggingface.co/dbmdz/bert-base-turkish-cased/pytorch_model.bin) • [`vocab.txt`](https://cdn.huggingface.co/dbmdz/bert-base-turkish-cased/vocab.txt) | f237c1bbf762a39c6718f3897a1bb173 |
mit | [] | false | Usage With Transformers >= 2.3 our BERTurk cased model can be loaded like: ```python from transformers import AutoModel, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("dbmdz/bert-base-turkish-cased") model = AutoModel.from_pretrained("dbmdz/bert-base-turkish-cased") ``` | 66f0c6b73620670ef8c3f76acf622a84 |
mit | ['generated_from_trainer'] | false | microsoft_deberta-base_squad This model is a fine-tuned version of [microsoft/deberta-base](https://huggingface.co/microsoft/deberta-base) on the **squadV1** dataset. - "eval_exact_match": 86.30085146641439 - "eval_f1": 92.68502275661561 - "eval_samples": 10788 | eec86bb7e4cad7fb1b76d192358aa12b |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 12 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 | 5bda79a417c77e538ab5f9c82a25636d |
apache-2.0 | ['generated_from_trainer'] | false | small-mlm-tweet This model is a fine-tuned version of [google/bert_uncased_L-4_H-512_A-8](https://huggingface.co/google/bert_uncased_L-4_H-512_A-8) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.8171 | 70c8199e455ac874d537d3281eab4ed2 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.4028 | 11.11 | 500 | 3.4323 | | 2.8952 | 22.22 | 1000 | 3.4180 | | 2.6035 | 33.33 | 1500 | 3.6851 | | 2.3349 | 44.44 | 2000 | 3.4708 | | 2.1048 | 55.56 | 2500 | 3.8171 | | be365b81bd1da1f3d1e5cbf761ce7fd0 |
apache-2.0 | ['tensorflowtts', 'audio', 'text-to-speech', 'text-to-mel'] | false | FastSpeech trained on LJSpeech (Eng) This repository provides a pretrained [FastSpeech](https://arxiv.org/abs/1905.09263) trained on LJSpeech dataset (ENG). For a detail of the model, we encourage you to read more about [TensorFlowTTS](https://github.com/TensorSpeech/TensorFlowTTS). | 81c25f36435af1e61642f616c8e62a29 |
apache-2.0 | ['tensorflowtts', 'audio', 'text-to-speech', 'text-to-mel'] | false | Converting your Text to Mel Spectrogram ```python import numpy as np import soundfile as sf import yaml import tensorflow as tf from tensorflow_tts.inference import AutoProcessor from tensorflow_tts.inference import TFAutoModel processor = AutoProcessor.from_pretrained("tensorspeech/tts-fastspeech-ljspeech-en") fastspeech = TFAutoModel.from_pretrained("tensorspeech/tts-fastspeech-ljspeech-en") text = "How are you?" input_ids = processor.text_to_sequence(text) mel_before, mel_after, duration_outputs = fastspeech.inference( input_ids=tf.expand_dims(tf.convert_to_tensor(input_ids, dtype=tf.int32), 0), speaker_ids=tf.convert_to_tensor([0], dtype=tf.int32), speed_ratios=tf.convert_to_tensor([1.0], dtype=tf.float32), ) ``` | f34ef8cffecff3efd06ad19701782829 |
apache-2.0 | ['tensorflowtts', 'audio', 'text-to-speech', 'text-to-mel'] | false | Referencing FastSpeech ``` @article{DBLP:journals/corr/abs-1905-09263, author = {Yi Ren and Yangjun Ruan and Xu Tan and Tao Qin and Sheng Zhao and Zhou Zhao and Tie{-}Yan Liu}, title = {FastSpeech: Fast, Robust and Controllable Text to Speech}, journal = {CoRR}, volume = {abs/1905.09263}, year = {2019}, url = {http://arxiv.org/abs/1905.09263}, archivePrefix = {arXiv}, eprint = {1905.09263}, timestamp = {Wed, 11 Nov 2020 08:48:07 +0100}, biburl = {https://dblp.org/rec/journals/corr/abs-1905-09263.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ``` | a820c8b6656217a55e215dd4af85b65a |
cc-by-sa-4.0 | ['generated_from_trainer'] | false | legal-bert-small-uncased-filtered-filtered-cuad This model is a fine-tuned version of [nlpaueb/legal-bert-small-uncased](https://huggingface.co/nlpaueb/legal-bert-small-uncased) on the cuad dataset. It achieves the following results on the evaluation set: - Loss: 0.0604 | 6fc0c9b64cfc3bac6d09f03e69e3d1e6 |
cc-by-sa-4.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.0768 | 1.0 | 2571 | 0.0701 | | 0.0667 | 2.0 | 5142 | 0.0638 | | 0.0548 | 3.0 | 7713 | 0.0604 | | 533d2f7e0108e6b3ec687deb489155e9 |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'animal'] | false | DreamBooth model for Pseudagrilus trained by johnowhitaker on the johnowhitaker/Pseudagrilus dataset. This is a Stable Diffusion model fine-tuned the Pseudagrilus concept taught to Stable Diffusion with DreamBooth. It can be used by modifying the `instance_prompt`: **a photo of Pseudagrilus beetle** This model was created as part of the DreamBooth Hackathon 🔥. Visit the [organisation page](https://huggingface.co/dreambooth-hackathon) for instructions on how to take part! | 27a6868516bf006dbe1672d2ca057036 |
apache-2.0 | ['setfit', 'sentence-transformers', 'text-classification'] | false | fathyshalab/massive_transport-roberta-large-v1-3-3 This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. | 7782f57b7aecf0644f0b5240e2c9c2bc |
mit | ['generated_from_trainer'] | false | bertimbau-base-finetuned-lener-br-finetuned-peticoes-assuntos This model is a fine-tuned version of [Luciano/bertimbau-base-finetuned-lener-br](https://huggingface.co/Luciano/bertimbau-base-finetuned-lener-br) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.9930 - Accuracy: 0.3575 | 1dbd47fd2149aab6effacdbf727dfef3 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 4.7305 | 1.0 | 898 | 3.6586 | 0.2533 | | 3.4793 | 2.0 | 1796 | 3.2827 | 0.3029 | | 3.0791 | 3.0 | 2694 | 3.0938 | 0.3427 | | 2.83 | 4.0 | 3592 | 3.0101 | 0.3477 | | 2.7427 | 5.0 | 4490 | 2.9930 | 0.3575 | | 2f2ebda2f3558134aabf90bc50ec5fd5 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-clinc This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset. It achieves the following results on the evaluation set: - Loss: 0.7713 - Accuracy: 0.9174 | d6baeb9155d98487813fef03ed22d302 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 4.2892 | 1.0 | 318 | 3.2831 | 0.7426 | | 2.6244 | 2.0 | 636 | 1.8739 | 0.8335 | | 1.5442 | 3.0 | 954 | 1.1525 | 0.8926 | | 1.0096 | 4.0 | 1272 | 0.8569 | 0.91 | | 0.793 | 5.0 | 1590 | 0.7713 | 0.9174 | | f347b1354f6ccbcf6385bd2adb573fde |
apache-2.0 | ['generated_from_trainer'] | false | fnet-large-finetuned-qqp This model is a fine-tuned version of [google/fnet-large](https://huggingface.co/google/fnet-large) on the GLUE QQP dataset. It achieves the following results on the evaluation set: - Loss: 0.5515 - Accuracy: 0.8943 - F1: 0.8557 - Combined Score: 0.8750 | ac029d0f210266f3ae0ccf8d6f01222a |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score | |:-------------:|:-----:|:------:|:---------------:|:--------:|:------:|:--------------:| | 0.4574 | 1.0 | 90962 | 0.4946 | 0.8694 | 0.8297 | 0.8496 | | 0.3387 | 2.0 | 181924 | 0.4745 | 0.8874 | 0.8437 | 0.8655 | | 0.2029 | 3.0 | 272886 | 0.5515 | 0.8943 | 0.8557 | 0.8750 | | 35916dfb2010b8702820c60a310ccfe2 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---:| | No log | 1.0 | 436 | 0.0001 | 1.0 | 0.0 | | 3dc24184b7065874ef79cffd939d3ad3 |
creativeml-openrail-m | ['text-to-image'] | false | Saad Dreambooth model trained by HusseinHE 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/main/diffusers/sd_dreambooth_inference.ipynb). Don't forget to use the concept prompts! Sample pictures of: sksaad (use that on your prompt)  | 041e840a071411bce11fc61f93e73bc1 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.1468 | 02f6ed12039a8611df7f3b4e4a47d91e |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.2257 | 1.0 | 5533 | 1.1557 | | 0.9632 | 2.0 | 11066 | 1.1215 | | 0.762 | 3.0 | 16599 | 1.1468 | | 91525be12717ddc644ee3474de7c1897 |
mit | ['text-classification'] | false | Load model and tokenizer from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForQuestionAnswering.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) | 8934c285f967e17115977274a59c7c7f |
mit | ['text-classification'] | false | Use pipeline from transformers import pipeline model_name = "aychang/distilbert-base-cased-trec-coarse" nlp = pipeline("sentiment-analysis", model=model_name, tokenizer=model_name) results = nlp(["Where did the queen go?", "Why did the Queen hire 1000 ML Engineers?"]) ``` | 77d04b5302103aed91ed0d811e44e5b3 |
mit | ['text-classification'] | false | AdaptNLP ```python from adaptnlp import EasySequenceClassifier model_name = "aychang/distilbert-base-cased-trec-coarse" texts = ["Where did the queen go?", "Why did the Queen hire 1000 ML Engineers?"] classifer = EasySequenceClassifier results = classifier.tag_text(text=texts, model_name_or_path=model_name, mini_batch_size=2) ``` | acf1ae34f88efe6526c1daf0d42bf67d |
mit | ['text-classification'] | false | Hyperparameters and Training Args ```python from transformers import TrainingArguments training_args = TrainingArguments( output_dir='./models', overwrite_output_dir=False, num_train_epochs=2, per_device_train_batch_size=16, per_device_eval_batch_size=16, warmup_steps=500, weight_decay=0.01, evaluation_strategy="steps", logging_dir='./logs', fp16=False, eval_steps=500, save_steps=300000 ) ``` | ea4b83c8b423886134f74d066f9312ab |
mit | ['text-classification'] | false | Eval results ``` {'epoch': 2.0, 'eval_accuracy': 0.97, 'eval_f1': array([0.98220641, 0.91620112, 1. , 0.97709924, 0.98678414, 0.97560976]), 'eval_loss': 0.14275787770748138, 'eval_precision': array([0.96503497, 0.96470588, 1. , 0.96969697, 0.98245614, 0.96385542]), 'eval_recall': array([1. , 0.87234043, 1. , 0.98461538, 0.99115044, 0.98765432]), 'eval_runtime': 0.9731, 'eval_samples_per_second': 513.798} ``` | bc12d26f284884eb45f408393df39b43 |
apache-2.0 | ['generated_from_trainer'] | false | xlsr-wav2vec2-2 This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5884 - Wer: 0.4301 | b7d0f42018981ae8cea60db1e2bbf6b0 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 800 - num_epochs: 60 - mixed_precision_training: Native AMP | 2b8567f4a9fd3003b710f0662eba1c24 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 6.6058 | 1.38 | 400 | 3.1894 | 1.0 | | 2.3145 | 2.76 | 800 | 0.7193 | 0.7976 | | 0.6737 | 4.14 | 1200 | 0.5338 | 0.6056 | | 0.4651 | 5.52 | 1600 | 0.5699 | 0.6007 | | 0.3968 | 6.9 | 2000 | 0.4608 | 0.5221 | | 0.3281 | 8.28 | 2400 | 0.5264 | 0.5209 | | 0.2937 | 9.65 | 2800 | 0.5366 | 0.5096 | | 0.2619 | 11.03 | 3200 | 0.4902 | 0.5021 | | 0.2394 | 12.41 | 3600 | 0.4706 | 0.4908 | | 0.2139 | 13.79 | 4000 | 0.5526 | 0.4871 | | 0.2034 | 15.17 | 4400 | 0.5396 | 0.5108 | | 0.1946 | 16.55 | 4800 | 0.4959 | 0.4866 | | 0.1873 | 17.93 | 5200 | 0.4898 | 0.4877 | | 0.1751 | 19.31 | 5600 | 0.5488 | 0.4932 | | 0.1668 | 20.69 | 6000 | 0.5645 | 0.4986 | | 0.1638 | 22.07 | 6400 | 0.5367 | 0.4946 | | 0.1564 | 23.45 | 6800 | 0.5282 | 0.4898 | | 0.1566 | 24.83 | 7200 | 0.5489 | 0.4841 | | 0.1522 | 26.21 | 7600 | 0.5439 | 0.4821 | | 0.1378 | 27.59 | 8000 | 0.5796 | 0.4866 | | 0.1459 | 28.96 | 8400 | 0.5603 | 0.4875 | | 0.1406 | 30.34 | 8800 | 0.6773 | 0.5005 | | 0.1298 | 31.72 | 9200 | 0.5858 | 0.4827 | | 0.1268 | 33.1 | 9600 | 0.6007 | 0.4790 | | 0.1204 | 34.48 | 10000 | 0.5716 | 0.4734 | | 0.113 | 35.86 | 10400 | 0.5866 | 0.4748 | | 0.1088 | 37.24 | 10800 | 0.5790 | 0.4752 | | 0.1074 | 38.62 | 11200 | 0.5966 | 0.4721 | | 0.1018 | 40.0 | 11600 | 0.5720 | 0.4668 | | 0.0968 | 41.38 | 12000 | 0.5826 | 0.4698 | | 0.0874 | 42.76 | 12400 | 0.5937 | 0.4634 | | 0.0843 | 44.14 | 12800 | 0.6056 | 0.4640 | | 0.0822 | 45.52 | 13200 | 0.5531 | 0.4569 | | 0.0806 | 46.9 | 13600 | 0.5669 | 0.4484 | | 0.072 | 48.28 | 14000 | 0.5683 | 0.4484 | | 0.0734 | 49.65 | 14400 | 0.5735 | 0.4437 | | 0.0671 | 51.03 | 14800 | 0.5455 | 0.4394 | | 0.0617 | 52.41 | 15200 | 0.5838 | 0.4365 | | 0.0607 | 53.79 | 15600 | 0.6233 | 0.4397 | | 0.0593 | 55.17 | 16000 | 0.5649 | 0.4340 | | 0.0551 | 56.55 | 16400 | 0.5923 | 0.4392 | | 0.0503 | 57.93 | 16800 | 0.5858 | 0.4325 | | 0.0496 | 59.31 | 17200 | 0.5884 | 0.4301 | | cf00ce8cdc2e298bab8d0e20b1783016 |
apache-2.0 | [] | false | distilbert-base-en-fr-ar-cased We are sharing smaller versions of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) that handle a custom number of languages. Our versions give exactly the same representations produced by the original model which preserves the original accuracy. For more information please visit our paper: [Load What You Need: Smaller Versions of Multilingual BERT](https://www.aclweb.org/anthology/2020.sustainlp-1.16.pdf). | 4dbae0882556855ed5870c250fb54fc6 |
apache-2.0 | [] | false | How to use ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Geotrend/distilbert-base-en-fr-ar-cased") model = AutoModel.from_pretrained("Geotrend/distilbert-base-en-fr-ar-cased") ``` To generate other smaller versions of multilingual transformers please visit [our Github repo](https://github.com/Geotrend-research/smaller-transformers). | 60919f749840c5aee899e2198f90f677 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-bbc-news 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: 0.0107 - Accuracy: 0.9955 - F1: 0.9955 | 2ec74006d28f01eadb670510cddb6dad |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 3 - eval_batch_size: 3 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 | 491e2d6c5a50eb01321a007fcc22157a |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.3463 | 0.84 | 500 | 0.0392 | 0.9865 | 0.9865 | | 0.0447 | 1.68 | 1000 | 0.0107 | 0.9955 | 0.9955 | | 42b1100f046a6bbfe727a0b9b333e851 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.1500 | 0ddc9c793fe3a4b4f93ffb65d9dcd303 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.3149 | 1.0 | 2767 | 1.2079 | | 1.053 | 2.0 | 5534 | 1.1408 | | 0.8809 | 3.0 | 8301 | 1.1500 | | eba75496e3071e3432782469d2c02c97 |
apache-2.0 | ['generated_from_trainer'] | false | finetuning-sentiment-model-3000-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: 0.7339 - Accuracy: 0.6567 - F1: 0.6979 | 125f76a9a7e6dd8ed980690897cfa247 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - eval_loss: 0.5180 - eval_matthews_correlation: 0.4063 - eval_runtime: 0.8532 - eval_samples_per_second: 1222.419 - eval_steps_per_second: 77.353 - epoch: 1.0 - step: 535 | ff064a9ea32b141a5e4885eb92d57897 |
cc-by-4.0 | ['question generation'] | false | Model Card of `lmqg/mbart-large-cc25-squad-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_squad](https://huggingface.co/datasets/lmqg/qg_squad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation). | 9e77ebfda3cf9b2c05859b9f7d0e198c |
cc-by-4.0 | ['question generation'] | false | Overview - **Language model:** [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) - **Language:** en - **Training data:** [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (default) - **Online Demo:** [https://autoqg.net/](https://autoqg.net/) - **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation) - **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992) | 167a0c70eae0315f0adf69c5eebeb8c4 |
cc-by-4.0 | ['question generation'] | false | model prediction questions = model.generate_q(list_context="William Turner was an English painter who specialised in watercolour landscapes", list_answer="William Turner") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "lmqg/mbart-large-cc25-squad-qg") output = pipe("<hl> Beyonce <hl> further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records.") ``` | 88d35882da2a5b729ea9e025f441bf0a |
cc-by-4.0 | ['question generation'] | false | Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/lmqg/mbart-large-cc25-squad-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_squad.default.json) | | Score | Type | Dataset | |:-----------|--------:|:--------|:---------------------------------------------------------------| | BERTScore | 90.36 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | Bleu_1 | 56 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | Bleu_2 | 39.41 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | Bleu_3 | 29.76 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | Bleu_4 | 23.03 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | METEOR | 25.1 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | MoverScore | 63.63 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | ROUGE_L | 50.58 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | - ***Metrics (Question Generation, Out-of-Domain)*** | Dataset | Type | BERTScore| Bleu_4 | METEOR | MoverScore | ROUGE_L | Link | |:--------|:-----|---------:|-------:|-------:|-----------:|--------:|-----:| | [lmqg/qg_dequad](https://huggingface.co/datasets/lmqg/qg_dequad) | default | 11.05 | 0.0 | 1.05 | 44.94 | 3.4 | [link](https://huggingface.co/lmqg/mbart-large-cc25-squad-qg/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_dequad.default.json) | | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | default | 60.73 | 0.57 | 5.27 | 48.76 | 18.99 | [link](https://huggingface.co/lmqg/mbart-large-cc25-squad-qg/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_esquad.default.json) | | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) | default | 16.47 | 0.02 | 1.55 | 45.35 | 5.13 | [link](https://huggingface.co/lmqg/mbart-large-cc25-squad-qg/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_frquad.default.json) | | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | default | 41.46 | 0.48 | 3.84 | 47.28 | 13.25 | [link](https://huggingface.co/lmqg/mbart-large-cc25-squad-qg/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_itquad.default.json) | | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) | default | 19.89 | 0.06 | 1.74 | 45.51 | 6.11 | [link](https://huggingface.co/lmqg/mbart-large-cc25-squad-qg/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_jaquad.default.json) | | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) | default | 31.67 | 0.38 | 3.06 | 46.59 | 10.34 | [link](https://huggingface.co/lmqg/mbart-large-cc25-squad-qg/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_koquad.default.json) | | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) | default | 26.19 | 0.18 | 2.65 | 46.09 | 8.34 | [link](https://huggingface.co/lmqg/mbart-large-cc25-squad-qg/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_ruquad.default.json) | | e72e80ea8368bcf16baaf6c263b73ced |
cc-by-4.0 | ['question generation'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_squad - 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 - epoch: 6 - batch: 32 - lr: 0.0001 - fp16: False - random_seed: 1 - gradient_accumulation_steps: 2 - label_smoothing: 0.15 The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/mbart-large-cc25-squad-qg/raw/main/trainer_config.json). | 97554240015c48217f3afc206f35f2fd |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_sa_GLUE_Experiment_data_aug_mnli_96 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE MNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.9477 - Accuracy: 0.5655 | 9783a523161dd74576120a278e71cc8d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:------:|:---------------:|:--------:| | 0.9142 | 1.0 | 31440 | 0.9328 | 0.5686 | | 0.8099 | 2.0 | 62880 | 0.9523 | 0.5752 | | 0.7371 | 3.0 | 94320 | 1.0072 | 0.5737 | | 0.6756 | 4.0 | 125760 | 1.0606 | 0.5750 | | 0.6229 | 5.0 | 157200 | 1.1116 | 0.5739 | | 0.5784 | 6.0 | 188640 | 1.1396 | 0.5795 | | e8d877365e46a7b4c1f6f67ab4e71feb |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | bart-base-finetuned-summarization-cnn-ver1.3 This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the cnn_dailymail dataset. It achieves the following results on the evaluation set: - Loss: 2.3148 - Bertscore-mean-precision: 0.8890 - Bertscore-mean-recall: 0.8603 - Bertscore-mean-f1: 0.8742 - Bertscore-median-precision: 0.8874 - Bertscore-median-recall: 0.8597 - Bertscore-median-f1: 0.8726 | 80256125461e32903cad6a2c3b595e00 |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 | 6557dc06c5370266f0a993335290ee41 |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Bertscore-mean-precision | Bertscore-mean-recall | Bertscore-mean-f1 | Bertscore-median-precision | Bertscore-median-recall | Bertscore-median-f1 | |:-------------:|:-----:|:-----:|:---------------:|:------------------------:|:---------------------:|:-----------------:|:--------------------------:|:-----------------------:|:-------------------:| | 2.3735 | 1.0 | 5742 | 2.2581 | 0.8831 | 0.8586 | 0.8705 | 0.8834 | 0.8573 | 0.8704 | | 1.744 | 2.0 | 11484 | 2.2479 | 0.8920 | 0.8620 | 0.8765 | 0.8908 | 0.8603 | 0.8752 | | 1.3643 | 3.0 | 17226 | 2.3148 | 0.8890 | 0.8603 | 0.8742 | 0.8874 | 0.8597 | 0.8726 | | c4de6a38e50fe6f20e43630b60cc6f82 |
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: 0.4923 - F1: 0.7205 | 7016ab1e7e95819c5d7c9522b8cbd4b6 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.9902 | 1.0 | 148 | 0.6183 | 0.5830 | | 0.4903 | 2.0 | 296 | 0.5232 | 0.6675 | | 0.3272 | 3.0 | 444 | 0.4923 | 0.7205 | | 01bf78b5b6f763912b9f8cb2313f195e |
apache-2.0 | ['generated_from_trainer'] | false | distilgpt2-sd-prompts This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on [Stable-Diffusion-Prompts](https://huggingface.co/datasets/Gustavosta/Stable-Diffusion-Prompts). It achieves the following results on the evaluation set: - Loss: 0.9450 | a0e2718d6d678d04b2bc3b7f633e36e6 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 500 - num_epochs: 8 - mixed_precision_training: Native AMP | 69f690fdd0f95ac0ae7925f6ab1a197b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.5122 | 1.93 | 500 | 1.5211 | | 1.2912 | 3.86 | 1000 | 1.1045 | | 0.9313 | 5.79 | 1500 | 0.9704 | | 0.7744 | 7.72 | 2000 | 0.9450 | | bd0c25dd495968f66463b60062f40b83 |
apache-2.0 | ['multiberts', 'multiberts-seed_1', 'multiberts-seed_1-step_1900k'] | false | MultiBERTs, Intermediate Checkpoint - Seed 1, Step 1900k 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 random seeds, which causes variations in the initial weights and order of training instances. The aim is to distinguish findings that apply to a specific artifact (i.e., a particular instance of the model) from those that apply to the more general procedure. We also provide 140 intermediate checkpoints captured during the course of pre-training (we saved 28 checkpoints for the first 5 runs). The models were originally released through [http://goo.gle/multiberts](http://goo.gle/multiberts). We describe them in our paper [The MultiBERTs: BERT Reproductions for Robustness Analysis](https://arxiv.org/abs/2106.16163). This is model | 124a96879535336a7a618bd8d6c1cd98 |
apache-2.0 | ['multiberts', 'multiberts-seed_1', 'multiberts-seed_1-step_1900k'] | 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_1-step_1900k') model = TFBertModel.from_pretrained("google/multiberts-seed_1-step_1900k") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` PyTorch version: ``` from transformers import BertTokenizer, BertModel tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_1-step_1900k') model = BertModel.from_pretrained("google/multiberts-seed_1-step_1900k") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` | af787d5997fb129a19fd93cd453af539 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-de-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1584 - F1: 0.8537 | d793f33d6e898e58d9ad374986b83fdf |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 358 | 0.1776 | 0.8263 | | 0.2394 | 2.0 | 716 | 0.1599 | 0.8447 | | 0.2394 | 3.0 | 1074 | 0.1584 | 0.8537 | | 0eeb97093de02edace40d442e071f384 |
apache-2.0 | ['generated_from_trainer'] | false | whisper-small-hi This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4281 - Wer: 31.9521 | d8fd9aea2bb4a5db8e0c2340ea6e128b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0879 | 2.44 | 1000 | 0.2908 | 33.7933 | | 0.0216 | 4.89 | 2000 | 0.3440 | 33.0229 | | 0.0014 | 7.33 | 3000 | 0.4063 | 32.2611 | | 0.0005 | 9.78 | 4000 | 0.4281 | 31.9521 | | b45ee4826f5882056f7e7b158a2b173f |
apache-2.0 | ['generated_from_trainer'] | false | mobilebert_sa_GLUE_Experiment_logit_kd_cola_128 This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE COLA dataset. It achieves the following results on the evaluation set: - Loss: 0.6807 - Matthews Correlation: 0.0 | ec4cd7dc96fcbfd2394e982c94c12f9f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.8228 | 1.0 | 67 | 0.6863 | 0.0 | | 0.7969 | 2.0 | 134 | 0.6870 | 0.0 | | 0.7965 | 3.0 | 201 | 0.6834 | 0.0 | | 0.795 | 4.0 | 268 | 0.6835 | 0.0 | | 0.7939 | 5.0 | 335 | 0.6807 | 0.0 | | 0.7451 | 6.0 | 402 | 0.6986 | 0.0672 | | 0.6395 | 7.0 | 469 | 0.7051 | 0.0875 | | 0.6042 | 8.0 | 536 | 0.7293 | 0.1094 | | 0.5756 | 9.0 | 603 | 0.7376 | 0.1173 | | 0.5558 | 10.0 | 670 | 0.7879 | 0.1123 | | a929320b36fb5e9b03fc61183311aa75 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Small Danish - Robust 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 da dataset. It achieves the following results on the evaluation set: - Loss: 0.7926 - Wer: 32.3251 | 3d8ca66cca501fbf79d8b415c37f26e4 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-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_precision_training: Native AMP | 3e67488f4fa87cf963f1d387b9789947 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0232 | 15.15 | 1000 | 0.7538 | 35.5813 | | 0.0061 | 30.3 | 2000 | 0.7933 | 34.3766 | | 0.0016 | 45.45 | 3000 | 0.7993 | 33.5823 | | 0.0003 | 60.61 | 4000 | 0.7986 | 31.6097 | | 0.0002 | 75.76 | 5000 | 0.7901 | 32.1357 | | 55e518a4d8b4f9fd99e265d1922ae971 |
apache-2.0 | ['generated_from_trainer'] | false | mobilebert_add_GLUE_Experiment_logit_kd_wnli_256 This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE WNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.3452 - Accuracy: 0.5634 | 5b65abb607d43c1ed0599c50822bec81 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.3473 | 1.0 | 5 | 0.3452 | 0.5634 | | 0.3469 | 2.0 | 10 | 0.3464 | 0.5634 | | 0.3467 | 3.0 | 15 | 0.3465 | 0.5634 | | 0.3465 | 4.0 | 20 | 0.3456 | 0.5634 | | 0.3466 | 5.0 | 25 | 0.3453 | 0.5634 | | 0.3466 | 6.0 | 30 | 0.3455 | 0.5634 | | 816f47b00f6d76213d05a408747ea56d |
mit | ['pytorch', 'diffusers', 'unconditional-audio-generation', 'diffusion-models-class'] | false | Model Card for Unit 4 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional audio generation of music in the genre Rock | e2c53179bd57638d42a8141233ae4a34 |
mit | ['pytorch', 'diffusers', 'unconditional-audio-generation', 'diffusion-models-class'] | false | Usage ```python from IPython.display import Audio from diffusers import DiffusionPipeline pipe = DiffusionPipeline.from_pretrained("StatsGary/audio-diffusion-electro-rock") output = pipe() display(output.images[0]) display(Audio(output.audios[0], rate=pipe.mel.get_sample_rate())) ``` | 7a9882cb2ce945771e3e25f3f6a5d078 |
cc-by-4.0 | ['generated_from_trainer'] | false | roberta-base-squad2-coffee20230108 This model is a fine-tuned version of [deepset/roberta-base-squad2](https://huggingface.co/deepset/roberta-base-squad2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 4.2379 | 1e2302b93f4b65d82156a9aa1737cfe2 |
cc-by-4.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 90 | 1.6912 | | 1.8817 | 2.0 | 180 | 1.7054 | | 1.3233 | 3.0 | 270 | 1.6376 | | 0.9894 | 4.0 | 360 | 2.1005 | | 0.7526 | 5.0 | 450 | 2.7104 | | 0.6553 | 6.0 | 540 | 2.2928 | | 0.5512 | 7.0 | 630 | 2.6380 | | 0.4148 | 8.0 | 720 | 2.8010 | | 0.2964 | 9.0 | 810 | 3.1167 | | 0.2538 | 10.0 | 900 | 3.5313 | | 0.2538 | 11.0 | 990 | 3.6620 | | 0.1918 | 12.0 | 1080 | 4.1138 | | 0.1363 | 13.0 | 1170 | 4.0901 | | 0.1606 | 14.0 | 1260 | 4.2286 | | 0.1162 | 15.0 | 1350 | 4.2379 | | c387a3ffafc58b4cc6dff9b3de7110b2 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-misogyny-sexism-indomain-mix-trans This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8397 - Accuracy: 0.797 - F1: 0.7691 - Precision: 0.8918 - Recall: 0.676 - Mae: 0.203 - Tn: 459 - Fp: 41 - Fn: 162 - Tp: 338 | 012fbc639903f1f5bf9bddd3c50591a5 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Mae | Tn | Fp | Fn | Tp | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|:-----:|:---:|:--:|:---:|:---:| | 0.2914 | 1.0 | 2711 | 0.5846 | 0.794 | 0.7726 | 0.8621 | 0.7 | 0.206 | 444 | 56 | 150 | 350 | | 0.2836 | 2.0 | 5422 | 0.6752 | 0.785 | 0.7491 | 0.8992 | 0.642 | 0.215 | 464 | 36 | 179 | 321 | | 0.2516 | 3.0 | 8133 | 0.7715 | 0.769 | 0.7214 | 0.9088 | 0.598 | 0.231 | 470 | 30 | 201 | 299 | | 0.2047 | 4.0 | 10844 | 0.8397 | 0.797 | 0.7691 | 0.8918 | 0.676 | 0.203 | 459 | 41 | 162 | 338 | | 98759252c4336a99c0fe5857ddf65e04 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-large-xls-r-300m-singlish-colab This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the li_singlish dataset. It achieves the following results on the evaluation set: - Loss: 0.7199 - Wer: 0.3337 | d2f0d18423adb57da5210057ea30b095 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 4.2984 | 4.76 | 400 | 2.9046 | 1.0 | | 1.1895 | 9.52 | 800 | 0.7725 | 0.4535 | | 0.1331 | 14.28 | 1200 | 0.7068 | 0.3847 | | 0.0701 | 19.05 | 1600 | 0.7547 | 0.3617 | | 0.0509 | 23.8 | 2000 | 0.7123 | 0.3444 | | 0.0385 | 28.57 | 2400 | 0.7199 | 0.3337 | | 494b5c8e5bf319effc1975ca5d1c779a |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'landscape', 'classical-art'] | false | DreamBooth model for the painting of mixed style between Claude-Monet and Hokusai This is a Stable Diffusion model fine-tuned to generate mixed styled paintings between Claude-Monet and Hokusai taught to Stable Diffusion with DreamBooth. It can be used by modifying the `instance_prompt`: **a painting in $M | 3a8cc7e60e2ca6d549c7ba0dd6dd02a5 |
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