license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1 class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
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apache-2.0 | ['image-classification', 'vision', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 | 77ffd8903c06edf101416e3409b61537 |
apache-2.0 | ['image-classification', 'vision', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.3717 | 1.0 | 6375 | 0.0522 | 0.9893 | | 0.3453 | 2.0 | 12750 | 0.0370 | 0.9906 | | 0.3736 | 3.0 | 19125 | 0.0308 | 0.9916 | | 0.3224 | 4.0 | 25500 | 0.0269 | 0.9939 | | 0.2846 | 5.0 | 31875 | 0.0236 | 0.9949 | | 340d6efd3fb270b1af2fd5cb57e22a96 |
agpl-3.0 | [] | false | FastText model trained on Icelandic This model is trained on the lemmas of the Icelandic Gigaword Corpus version 20.05. It is trained using the gensim package, version 4.1.0. and parameters were set to default (100 dimensions, windows size 5) This model can not be loaded directly since it uses gensim, clone the repository and run the following to use it. ```python import gensim model = gensim.models.FastText.load("./rmh.w2v.model") ``` | f18becb2e37b3aa4fc7c33473d11d45d |
agpl-3.0 | [] | false | Example output ```bash In [1]: model.wv.most_similar("england") Out[1]: [('englands', 0.8778558969497681), ('southland', 0.8573296070098877), ('skotland', 0.846065878868103), ('englaland', 0.8320872187614441), ('hoogland', 0.8299505114555359), ('hoagland', 0.8277317881584167), ('totland', 0.8265103697776794), ('lackland', 0.8234561681747437), ('skarpengland', 0.8227219581604004), ('langland', 0.8222305774688721)] In [2]: model.wv.most_similar("kanína") Out[2]: [('loðkanína', 0.9271067976951599), ('dvergkanína', 0.9106121063232422), ('angórakanína', 0.895512044429779), ('angórukanína', 0.8741581439971924), ('feldkanína', 0.8696010708808899), ('kanínubangsi', 0.8562541604042053), ('holdakanína', 0.8543838858604431), ('villikanína', 0.8525990843772888), ('silkikanína', 0.8515204191207886), ('kaníni', 0.8445548415184021)] ``` | 785094e32b815826280bcf549b6ad0ca |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 512 - 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_ratio: 0.06 - num_epochs: 3.0 - mixed_precision_training: Native AMP | fcbecc70f3b73696a4cb107fe0b9a2e8 |
apache-2.0 | ['generated_from_keras_callback'] | false | abyaugustinek/distilbert-base-uncased-finetuned This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.3693 - Validation Loss: 1.2106 - Train Precision: 0.0 - Train Recall: 0.0 - Train F1: 0.0 - Train Accuracy: 0.6565 - Epoch: 2 | 57d907b9ec256ebd57673fe845b486a6 |
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': 30, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 | f38925f413b713607460e21a0de2632b |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Train Precision | Train Recall | Train F1 | Train Accuracy | Epoch | |:----------:|:---------------:|:---------------:|:------------:|:--------:|:--------------:|:-----:| | 2.0691 | 1.5942 | 0.0 | 0.0 | 0.0 | 0.6565 | 0 | | 1.4705 | 1.2376 | 0.0 | 0.0 | 0.0 | 0.6565 | 1 | | 1.3693 | 1.2106 | 0.0 | 0.0 | 0.0 | 0.6565 | 2 | | b176c180ce92cf3ddb4b8b1613959a5d |
apache-2.0 | ['generated_from_trainer'] | false | canine-s-finetuned-sst2 This model is a fine-tuned version of [google/canine-s](https://huggingface.co/google/canine-s) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.5259 - Accuracy: 0.8578 | 3e04e2af0b5017b6999fc4dc765c908d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.3524 | 1.0 | 4210 | 0.4762 | 0.8257 | | 0.2398 | 2.0 | 8420 | 0.4169 | 0.8567 | | 0.1797 | 3.0 | 12630 | 0.5259 | 0.8578 | | 0.152 | 4.0 | 16840 | 0.5996 | 0.8532 | | 0.1026 | 5.0 | 21050 | 0.6676 | 0.8578 | | daa970f436b9cbb0f48678c5b2242c37 |
apache-2.0 | ['translation'] | false | fin-eng * source group: Finnish * target group: English * OPUS readme: [fin-eng](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/fin-eng/README.md) * model: transformer-align * source language(s): fin * target language(s): eng * model: transformer-align * pre-processing: normalization + SentencePiece (spm32k,spm32k) * download original weights: [opus-2020-08-05.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/fin-eng/opus-2020-08-05.zip) * test set translations: [opus-2020-08-05.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/fin-eng/opus-2020-08-05.test.txt) * test set scores: [opus-2020-08-05.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/fin-eng/opus-2020-08-05.eval.txt) | 3211e27092bef267f336a9b5462d6a22 |
apache-2.0 | ['translation'] | false | Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | newsdev2015-enfi-fineng.fin.eng | 25.3 | 0.536 | | newstest2015-enfi-fineng.fin.eng | 26.9 | 0.547 | | newstest2016-enfi-fineng.fin.eng | 29.0 | 0.571 | | newstest2017-enfi-fineng.fin.eng | 32.3 | 0.594 | | newstest2018-enfi-fineng.fin.eng | 23.8 | 0.517 | | newstest2019-fien-fineng.fin.eng | 29.0 | 0.565 | | newstestB2016-enfi-fineng.fin.eng | 24.5 | 0.527 | | newstestB2017-enfi-fineng.fin.eng | 27.4 | 0.557 | | newstestB2017-fien-fineng.fin.eng | 27.4 | 0.557 | | Tatoeba-test.fin.eng | 53.4 | 0.697 | | 87e6f792eab32798ba3b1819d604b851 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: fin-eng - source_languages: fin - target_languages: eng - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/fin-eng/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['fi', 'en'] - src_constituents: {'fin'} - tgt_constituents: {'eng'} - src_multilingual: False - tgt_multilingual: False - prepro: normalization + SentencePiece (spm32k,spm32k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/fin-eng/opus-2020-08-05.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/fin-eng/opus-2020-08-05.test.txt - src_alpha3: fin - tgt_alpha3: eng - short_pair: fi-en - chrF2_score: 0.6970000000000001 - bleu: 53.4 - brevity_penalty: 0.99 - ref_len: 74651.0 - src_name: Finnish - tgt_name: English - train_date: 2020-08-05 - src_alpha2: fi - tgt_alpha2: en - prefer_old: False - long_pair: fin-eng - helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535 - transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b - port_machine: brutasse - port_time: 2020-08-21-14:41 | e2732edc82d1aea60e27df7faa9c426a |
apache-2.0 | ['generated_from_trainer'] | false | distilgpt2-finetuned-wikitext2 This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.6895 | 330dae0675777945786531d85ae88a7d |
apache-2.0 | ['automatic-speech-recognition', 'fr'] | false | exp_w2v2r_fr_xls-r_accent_france-0_belgium-10_s198 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 that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool. | f2e7464d33285680152fb1c5eb828635 |
apache-2.0 | [] | false | Ernie-M ERNIE-M, proposed by Baidu, is a new training method that encourages the model to align the representation of multiple languages with monolingual corpora, to overcome the constraint that the parallel corpus size places on the model performance. The insight is to integrate back-translation into the pre-training process by generating pseudo-parallel sentence pairs on a monolingual corpus to enable the learning of semantic alignments between different languages, thereby enhancing the semantic modeling of cross-lingual models. Experimental results show that ERNIE-M outperforms existing cross-lingual models and delivers new state-of-the-art results in various cross-lingual downstream tasks. We proposed two novel methods to align the representation of multiple languages: Cross-Attention Masked Language Modeling(CAMLM): In CAMLM, we learn the multilingual semantic representation by restoring the MASK tokens in the input sentences. Back-Translation masked language modeling(BTMLM): We use BTMLM to train our model to generate pseudo-parallel sentences from the monolingual sentences. The generated pairs are then used as the input of the model to further align the cross-lingual semantics, thus enhancing the multilingual representation.  | e8267efb420aade9d7adb4bbe3d7652f |
apache-2.0 | [] | false | XNLI XNLI is a subset of MNLI and has been translated into 14 different kinds of languages including some low-resource languages. The goal of the task is to predict testual entailment (whether sentence A implies / contradicts / neither sentence B). | Model | en | fr | es | de | el | bg | ru | tr | ar | vi | th | zh | hi | sw | ur | Avg | | ---------------------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- | | Cross-lingual Transfer | | | | | | | | | | | | | | | | | | XLM | 85.0 | 78.7 | 78.9 | 77.8 | 76.6 | 77.4 | 75.3 | 72.5 | 73.1 | 76.1 | 73.2 | 76.5 | 69.6 | 68.4 | 67.3 | 75.1 | | Unicoder | 85.1 | 79.0 | 79.4 | 77.8 | 77.2 | 77.2 | 76.3 | 72.8 | 73.5 | 76.4 | 73.6 | 76.2 | 69.4 | 69.7 | 66.7 | 75.4 | | XLM-R | 85.8 | 79.7 | 80.7 | 78.7 | 77.5 | 79.6 | 78.1 | 74.2 | 73.8 | 76.5 | 74.6 | 76.7 | 72.4 | 66.5 | 68.3 | 76.2 | | INFOXLM | **86.4** | **80.6** | 80.8 | 78.9 | 77.8 | 78.9 | 77.6 | 75.6 | 74.0 | 77.0 | 73.7 | 76.7 | 72.0 | 66.4 | 67.1 | 76.2 | | **ERNIE-M** | 85.5 | 80.1 | **81.2** | **79.2** | **79.1** | **80.4** | **78.1** | **76.8** | **76.3** | **78.3** | **75.8** | **77.4** | **72.9** | **69.5** | **68.8** | **77.3** | | XLM-R Large | 89.1 | 84.1 | 85.1 | 83.9 | 82.9 | 84.0 | 81.2 | 79.6 | 79.8 | 80.8 | 78.1 | 80.2 | 76.9 | 73.9 | 73.8 | 80.9 | | INFOXLM Large | **89.7** | 84.5 | 85.5 | 84.1 | 83.4 | 84.2 | 81.3 | 80.9 | 80.4 | 80.8 | 78.9 | 80.9 | 77.9 | 74.8 | 73.7 | 81.4 | | VECO Large | 88.2 | 79.2 | 83.1 | 82.9 | 81.2 | 84.2 | 82.8 | 76.2 | 80.3 | 74.3 | 77.0 | 78.4 | 71.3 | **80.4** | **79.1** | 79.9 | | **ERNIR-M Large** | 89.3 | **85.1** | **85.7** | **84.4** | **83.7** | **84.5** | 82.0 | **81.2** | **81.2** | **81.9** | **79.2** | **81.0** | **78.6** | 76.2 | 75.4 | **82.0** | | Translate-Train-All | | | | | | | | | | | | | | | | | | XLM | 85.0 | 80.8 | 81.3 | 80.3 | 79.1 | 80.9 | 78.3 | 75.6 | 77.6 | 78.5 | 76.0 | 79.5 | 72.9 | 72.8 | 68.5 | 77.8 | | Unicoder | 85.6 | 81.1 | 82.3 | 80.9 | 79.5 | 81.4 | 79.7 | 76.8 | 78.2 | 77.9 | 77.1 | 80.5 | 73.4 | 73.8 | 69.6 | 78.5 | | XLM-R | 85.4 | 81.4 | 82.2 | 80.3 | 80.4 | 81.3 | 79.7 | 78.6 | 77.3 | 79.7 | 77.9 | 80.2 | 76.1 | 73.1 | 73.0 | 79.1 | | INFOXLM | 86.1 | 82.0 | 82.8 | 81.8 | 80.9 | 82.0 | 80.2 | 79.0 | 78.8 | 80.5 | 78.3 | 80.5 | 77.4 | 73.0 | 71.6 | 79.7 | | **ERNIE-M** | **86.2** | **82.5** | **83.8** | **82.6** | **82.4** | **83.4** | **80.2** | **80.6** | **80.5** | **81.1** | **79.2** | **80.5** | **77.7** | **75.0** | **73.3** | **80.6** | | XLM-R Large | 89.1 | 85.1 | 86.6 | 85.7 | 85.3 | 85.9 | 83.5 | 83.2 | 83.1 | 83.7 | 81.5 | **83.7** | **81.6** | 78.0 | 78.1 | 83.6 | | VECO Large | 88.9 | 82.4 | 86.0 | 84.7 | 85.3 | 86.2 | **85.8** | 80.1 | 83.0 | 77.2 | 80.9 | 82.8 | 75.3 | **83.1** | **83.0** | 83.0 | | **ERNIE-M Large** | **89.5** | **86.5** | **86.9** | **86.1** | **86.0** | **86.8** | 84.1 | **83.8** | **84.1** | **84.5** | **82.1** | 83.5 | 81.1 | 79.4 | 77.9 | **84.2** | | 596c850edbf583464785a9e3eb444d12 |
apache-2.0 | [] | false | Cross-lingual Named Entity Recognition * datasets：CoNLI | Model | en | nl | es | de | Avg | | ------------------------------ | --------- | --------- | --------- | --------- | --------- | | *Fine-tune on English dataset* | | | | | | | mBERT | 91.97 | 77.57 | 74.96 | 69.56 | 78.52 | | XLM-R | 92.25 | **78.08** | 76.53 | **69.60** | 79.11 | | **ERNIE-M** | **92.78** | 78.01 | **79.37** | 68.08 | **79.56** | | XLM-R LARGE | 92.92 | 80.80 | 78.64 | 71.40 | 80.94 | | **ERNIE-M LARGE** | **93.28** | **81.45** | **78.83** | **72.99** | **81.64** | | *Fine-tune on all dataset* | | | | | | | XLM-R | 91.08 | 89.09 | 87.28 | 83.17 | 87.66 | | **ERNIE-M** | **93.04** | **91.73** | **88.33** | **84.20** | **89.32** | | XLM-R LARGE | 92.00 | 91.60 | **89.52** | 84.60 | 89.43 | | **ERNIE-M LARGE** | **94.01** | **93.81** | 89.23 | **86.20** | **90.81** | | 302ac5d7c3d34cdc73c9d618a6dd72aa |
apache-2.0 | [] | false | Cross-lingual Question Answering * datasets：MLQA | Model | en | es | de | ar | hi | vi | zh | Avg | | ----------------- | --------------- | --------------- | --------------- | --------------- | --------------- | --------------- | --------------- | --------------- | | mBERT | 77.7 / 65.2 | 64.3 / 46.6 | 57.9 / 44.3 | 45.7 / 29.8 | 43.8 / 29.7 | 57.1 / 38.6 | 57.5 / 37.3 | 57.7 / 41.6 | | XLM | 74.9 / 62.4 | 68.0 / 49.8 | 62.2 / 47.6 | 54.8 / 36.3 | 48.8 / 27.3 | 61.4 / 41.8 | 61.1 / 39.6 | 61.6 / 43.5 | | XLM-R | 77.1 / 64.6 | 67.4 / 49.6 | 60.9 / 46.7 | 54.9 / 36.6 | 59.4 / 42.9 | 64.5 / 44.7 | 61.8 / 39.3 | 63.7 / 46.3 | | INFOXLM | 81.3 / 68.2 | 69.9 / 51.9 | 64.2 / 49.6 | 60.1 / 40.9 | 65.0 / 47.5 | 70.0 / 48.6 | 64.7 / **41.2** | 67.9 / 49.7 | | **ERNIE-M** | **81.6 / 68.5** | **70.9 / 52.6** | **65.8 / 50.7** | **61.8 / 41.9** | **65.4 / 47.5** | **70.0 / 49.2** | **65.6** / 41.0 | **68.7 / 50.2** | | XLM-R LARGE | 80.6 / 67.8 | 74.1 / 56.0 | 68.5 / 53.6 | 63.1 / 43.5 | 62.9 / 51.6 | 71.3 / 50.9 | 68.0 / 45.4 | 70.7 / 52.7 | | INFOXLM LARGE | **84.5 / 71.6** | **75.1 / 57.3** | **71.2 / 56.2** | **67.6 / 47.6** | 72.5 / 54.2 | **75.2 / 54.1** | 69.2 / 45.4 | 73.6 / 55.2 | | **ERNIE-M LARGE** | 84.4 / 71.5 | 74.8 / 56.6 | 70.8 / 55.9 | 67.4 / 47.2 | **72.6 / 54.7** | 75.0 / 53.7 | **71.1 / 47.5** | **73.7 / 55.3** | | 956a284a0fe5cde90ad23a2bdfb702e4 |
apache-2.0 | [] | false | Cross-lingual Paraphrase Identification * datasets：PAWS-X | Model | en | de | es | fr | ja | ko | zh | Avg | | ---------------------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- | | Cross-lingual Transfer | | | | | | | | | | mBERT | 94.0 | 85.7 | 87.4 | 87.0 | 73.0 | 69.6 | 77.0 | 81.9 | | XLM | 94.0 | 85.9 | 88.3 | 87.4 | 69.3 | 64.8 | 76.5 | 80.9 | | MMTE | 93.1 | 85.1 | 87.2 | 86.9 | 72.0 | 69.2 | 75.9 | 81.3 | | XLM-R LARGE | 94.7 | 89.7 | 90.1 | 90.4 | 78.7 | 79.0 | 82.3 | 86.4 | | VECO LARGE | **96.2** | 91.3 | 91.4 | 92.0 | 81.8 | 82.9 | 85.1 | 88.7 | | **ERNIE-M LARGE** | 96.0 | **91.9** | **91.4** | **92.2** | **83.9** | **84.5** | **86.9** | **89.5** | | Translate-Train-All | | | | | | | | | | VECO LARGE | 96.4 | 93.0 | 93.0 | 93.5 | 87.2 | 86.8 | 87.9 | 91.1 | | **ERNIE-M LARGE** | **96.5** | **93.5** | **93.3** | **93.8** | **87.9** | **88.4** | **89.2** | **91.8** | | 4dd289d2903017e9c179d25ff4011ebc |
apache-2.0 | [] | false | Cross-lingual Sentence Retrieval * dataset：Tatoeba | Model | Avg | | --------------------------------------- | -------- | | XLM-R LARGE | 75.2 | | VECO LARGE | 86.9 | | **ERNIE-M LARGE** | **87.9** | | **ERNIE-M LARGE（ after fine-tuning）** | **93.3** | | 18e68a3c9efdd87bfee8544f65f0dce5 |
apache-2.0 | [] | false | Citation Info ```text @article{Ouyang2021ERNIEMEM, title={ERNIE-M: Enhanced Multilingual Representation by Aligning Cross-lingual Semantics with Monolingual Corpora}, author={Xuan Ouyang and Shuohuan Wang and Chao Pang and Yu Sun and Hao Tian and Hua Wu and Haifeng Wang}, journal={ArXiv}, year={2021}, volume={abs/2012.15674} } ``` | 20c3a33d8068969a6a70951ee595dedf |
mit | ['generated_from_trainer'] | false | predict-perception-xlmr-focus-victim 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: - Loss: 0.2546 - Rmse: 0.6301 - Rmse Focus::a Sulla vittima: 0.6301 - Mae: 0.5441 - Mae Focus::a Sulla vittima: 0.5441 - R2: 0.7205 - R2 Focus::a Sulla vittima: 0.7205 - Cos: 0.8261 - Pair: 0.0 - Rank: 0.5 - Neighbors: 0.7802 - Rsa: nan | 13317a201b8aa07b2ec59e3fa17f2c99 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rmse | Rmse Focus::a Sulla vittima | Mae | Mae Focus::a Sulla vittima | R2 | R2 Focus::a Sulla vittima | Cos | Pair | Rank | Neighbors | Rsa | |:-------------:|:-----:|:----:|:---------------:|:------:|:---------------------------:|:------:|:--------------------------:|:-------:|:-------------------------:|:------:|:----:|:----:|:---------:|:---:| | 1.0607 | 1.0 | 15 | 0.9261 | 1.2017 | 1.2017 | 0.9557 | 0.9557 | -0.0166 | -0.0166 | 0.4783 | 0.0 | 0.5 | 0.6332 | nan | | 1.0107 | 2.0 | 30 | 0.9481 | 1.2159 | 1.2159 | 0.9861 | 0.9861 | -0.0408 | -0.0408 | 0.4783 | 0.0 | 0.5 | 0.6332 | nan | | 0.9921 | 3.0 | 45 | 0.9068 | 1.1892 | 1.1892 | 0.9548 | 0.9548 | 0.0045 | 0.0045 | 0.4783 | 0.0 | 0.5 | 0.6332 | nan | | 0.7769 | 4.0 | 60 | 0.5014 | 0.8842 | 0.8842 | 0.7121 | 0.7121 | 0.4496 | 0.4496 | 0.7391 | 0.0 | 0.5 | 0.6232 | nan | | 0.5763 | 5.0 | 75 | 0.4019 | 0.7917 | 0.7917 | 0.6737 | 0.6737 | 0.5588 | 0.5588 | 0.8261 | 0.0 | 0.5 | 0.8155 | nan | | 0.4378 | 6.0 | 90 | 0.3594 | 0.7486 | 0.7486 | 0.5957 | 0.5957 | 0.6055 | 0.6055 | 0.7391 | 0.0 | 0.5 | 0.4442 | nan | | 0.3595 | 7.0 | 105 | 0.3452 | 0.7337 | 0.7337 | 0.6333 | 0.6333 | 0.6210 | 0.6210 | 0.5652 | 0.0 | 0.5 | 0.2649 | nan | | 0.3192 | 8.0 | 120 | 0.3275 | 0.7147 | 0.7147 | 0.6205 | 0.6205 | 0.6405 | 0.6405 | 0.7391 | 0.0 | 0.5 | 0.6561 | nan | | 0.2482 | 9.0 | 135 | 0.2978 | 0.6815 | 0.6815 | 0.5754 | 0.5754 | 0.6731 | 0.6731 | 0.7391 | 0.0 | 0.5 | 0.6715 | nan | | 0.2416 | 10.0 | 150 | 0.3018 | 0.6860 | 0.6860 | 0.5954 | 0.5954 | 0.6687 | 0.6687 | 0.5652 | 0.0 | 0.5 | 0.2553 | nan | | 0.2292 | 11.0 | 165 | 0.2764 | 0.6565 | 0.6565 | 0.5522 | 0.5522 | 0.6966 | 0.6966 | 0.9130 | 0.0 | 0.5 | 0.8408 | nan | | 0.1752 | 12.0 | 180 | 0.3070 | 0.6920 | 0.6920 | 0.5680 | 0.5680 | 0.6629 | 0.6629 | 0.7391 | 0.0 | 0.5 | 0.6715 | nan | | 0.1956 | 13.0 | 195 | 0.2923 | 0.6752 | 0.6752 | 0.5499 | 0.5499 | 0.6791 | 0.6791 | 0.8261 | 0.0 | 0.5 | 0.7843 | nan | | 0.1424 | 14.0 | 210 | 0.3163 | 0.7023 | 0.7023 | 0.6060 | 0.6060 | 0.6528 | 0.6528 | 0.9130 | 0.0 | 0.5 | 0.8408 | nan | | 0.152 | 15.0 | 225 | 0.2436 | 0.6164 | 0.6164 | 0.5127 | 0.5127 | 0.7326 | 0.7326 | 0.9130 | 0.0 | 0.5 | 0.8408 | nan | | 0.1277 | 16.0 | 240 | 0.2471 | 0.6208 | 0.6208 | 0.5367 | 0.5367 | 0.7287 | 0.7287 | 0.8261 | 0.0 | 0.5 | 0.7802 | nan | | 0.1269 | 17.0 | 255 | 0.2573 | 0.6334 | 0.6334 | 0.5329 | 0.5329 | 0.7175 | 0.7175 | 0.8261 | 0.0 | 0.5 | 0.7802 | nan | | 0.1058 | 18.0 | 270 | 0.2538 | 0.6291 | 0.6291 | 0.5530 | 0.5530 | 0.7214 | 0.7214 | 0.7391 | 0.0 | 0.5 | 0.2347 | nan | | 0.107 | 19.0 | 285 | 0.2568 | 0.6328 | 0.6328 | 0.5464 | 0.5464 | 0.7181 | 0.7181 | 0.8261 | 0.0 | 0.5 | 0.7802 | nan | | 0.1185 | 20.0 | 300 | 0.2452 | 0.6183 | 0.6183 | 0.5317 | 0.5317 | 0.7309 | 0.7309 | 0.7391 | 0.0 | 0.5 | 0.2347 | nan | | 0.1029 | 21.0 | 315 | 0.2419 | 0.6142 | 0.6142 | 0.5415 | 0.5415 | 0.7344 | 0.7344 | 0.7391 | 0.0 | 0.5 | 0.2347 | nan | | 0.0908 | 22.0 | 330 | 0.2462 | 0.6196 | 0.6196 | 0.5261 | 0.5261 | 0.7297 | 0.7297 | 0.8261 | 0.0 | 0.5 | 0.7802 | nan | | 0.0901 | 23.0 | 345 | 0.2528 | 0.6279 | 0.6279 | 0.5330 | 0.5330 | 0.7225 | 0.7225 | 0.8261 | 0.0 | 0.5 | 0.7802 | nan | | 0.0979 | 24.0 | 360 | 0.2800 | 0.6607 | 0.6607 | 0.5682 | 0.5682 | 0.6927 | 0.6927 | 0.9130 | 0.0 | 0.5 | 0.8408 | nan | | 0.0992 | 25.0 | 375 | 0.2502 | 0.6246 | 0.6246 | 0.5517 | 0.5517 | 0.7254 | 0.7254 | 0.6522 | 0.0 | 0.5 | 0.2372 | nan | | 0.0846 | 26.0 | 390 | 0.2570 | 0.6331 | 0.6331 | 0.5524 | 0.5524 | 0.7178 | 0.7178 | 0.8261 | 0.0 | 0.5 | 0.7802 | nan | | 0.0717 | 27.0 | 405 | 0.2562 | 0.6321 | 0.6321 | 0.5456 | 0.5456 | 0.7187 | 0.7187 | 0.8261 | 0.0 | 0.5 | 0.7802 | nan | | 0.0739 | 28.0 | 420 | 0.2570 | 0.6330 | 0.6330 | 0.5471 | 0.5471 | 0.7179 | 0.7179 | 0.8261 | 0.0 | 0.5 | 0.7802 | nan | | 0.0828 | 29.0 | 435 | 0.2553 | 0.6309 | 0.6309 | 0.5446 | 0.5446 | 0.7198 | 0.7198 | 0.8261 | 0.0 | 0.5 | 0.7802 | nan | | 0.086 | 30.0 | 450 | 0.2546 | 0.6301 | 0.6301 | 0.5441 | 0.5441 | 0.7205 | 0.7205 | 0.8261 | 0.0 | 0.5 | 0.7802 | nan | | 027e669cb66b05826321db3035266c42 |
apache-2.0 | [] | false | NT5, a T5 model trained to perform numerical reasoning T5-small model pre-trained on 3 million (partly synthetic) texts and fine-tuned on [DROP](https://allennlp.org/drop.html). It was introduced in the paper [NT5?! Training T5 to Perform Numerical Reasoning](https://arxiv.org/abs/2104.07307) by Yang et al. and first released in [this repository](https://github.com/lesterpjy/numeric-t5). As the original implementation was in Tensorflow 2, I've converted the weigths to PyTorch. This model corresponds to RC Experiment 1 (see the paper), their best performing model. Disclaimer: The team releasing NT5 did not write a model card for this model so this model card has been written by me. | 104019d0d6dd056b7b641affdeb2909e |
apache-2.0 | [] | false | Model description The NT5 model is a T5 model, in other words, an encoder-decoder Transformer. In order to encourage numerical reasoning, the model was further pre-trained on three datasets designed to strengthen skills necessary for numerical reasoning over text (NRoT) and general reading comprehension before being fine-tuned on the Discrete Reasoning over Text (DROP) dataset. | 2bdde5edf4fcd427e5685a159d9cd68f |
apache-2.0 | [] | false | How to use Here is how to use this model: ```python from transformers import T5Tokenizer, T5ForConditionalGeneration context = """Saint Jean de Brébeuf was a French Jesuit missionary who travelled to New France in 1625. There he worked primarily with the Huron for the rest of his life, except for a few years in France from 1629 to 1633. He learned their language and culture, writing extensively about each to aid other missionaries. In 1649, Br´ebeuf and another missionary were captured when an Iroquois raid took over a Huron village . Together with Huron captives, the missionaries were ritually tortured and killed on March 16, 1649. Br´ebeuf was beatified in 1925 and among eight Jesuit missionaries canonized as saints in the Roman Catholic Church in 1930.""" question = "How many years did Saint Jean de Brébeuf stay in New France before he went back to France for a few years?" tokenizer = T5Tokenizer.from_pretrained("nielsr/nt5-small-rc1") model = T5ForConditionalGeneration.from_pretrained("nielsr/nt5-small-rc1") | cbab1a466320f970aa6ec9f4bf7f85c8 |
apache-2.0 | [] | false | encode context & question input_text = f"answer_me: {question} context: {context}" encoded_query = tokenizer( input_text, return_tensors='pt', padding='max_length', truncation=True, max_length=512) | cc35e06b0795987fcf4bb81ec93d4164 |
apache-2.0 | [] | false | generate answer generated_answer = model.generate(input_ids=encoded_query["input_ids"], attention_mask=encoded_query["attention_mask"], max_length=54) decoded_answer = tokenizer.decode(generated_answer.numpy()[0]) print("T5 Answer: ", decoded_answer) T5 Answer: 4 ``` | 0206b701bb8ce5425985d35b00611411 |
apache-2.0 | [] | false | BibTeX entry and citation info ```bibtex @misc{yang2021nt5, title={NT5?! Training T5 to Perform Numerical Reasoning}, author={Peng-Jian Yang and Ying Ting Chen and Yuechan Chen and Daniel Cer}, year={2021}, eprint={2104.07307}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ```bibtex @article{DBLP:journals/corr/abs-1903-00161, author = {Dheeru Dua and Yizhong Wang and Pradeep Dasigi and Gabriel Stanovsky and Sameer Singh and Matt Gardner}, title = {{DROP:} {A} Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs}, journal = {CoRR}, volume = {abs/1903.00161}, year = {2019}, url = {http://arxiv.org/abs/1903.00161}, archivePrefix = {arXiv}, eprint = {1903.00161}, timestamp = {Wed, 03 Jul 2019 07:17:04 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-1903-00161.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } a service of Schloss Dagstuhl - Leibniz Center for Informatics\\\\thomebrowsesearchabout ``` | 303f666e1be09c2f44281661aa6fcc19 |
apache-2.0 | ['generated_from_trainer'] | false | xlsr-53-bemba-10hrs This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3190 - Wer: 0.4032 | 8114f74ea6d95958cc231b792bd0ec62 |
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: 400 - num_epochs: 10 - mixed_precision_training: Native AMP | 05d2e54a393b19fd9df39eb00243c964 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.3207 | 1.07 | 400 | 0.3720 | 0.5923 | | 0.5688 | 2.14 | 800 | 0.3073 | 0.5002 | | 0.3927 | 3.22 | 1200 | 0.2678 | 0.4521 | | 0.316 | 4.29 | 1600 | 0.2703 | 0.4261 | | 0.2531 | 5.36 | 2000 | 0.2663 | 0.4198 | | 0.2051 | 6.43 | 2400 | 0.2614 | 0.4037 | | 0.1584 | 7.51 | 2800 | 0.2853 | 0.4046 | | 0.1343 | 8.58 | 3200 | 0.3072 | 0.4121 | | 0.1031 | 9.65 | 3600 | 0.3190 | 0.4032 | | 6ac1f9ccbba2ee80f8b200c41bbcce7a |
apache-2.0 | ['automatic-speech-recognition', 'de'] | false | exp_w2v2r_de_vp-100k_age_teens-2_sixties-8_s877 Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (de)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool. | 167e0e2804f9762ef988d8faa511072d |
apache-2.0 | ['generated_from_trainer'] | false | BERT_Mod_2 This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the glue dataset. It achieves the following results on the evaluation set: - eval_loss: 0.5659 - eval_accuracy: 0.9037 - eval_runtime: 0.3838 - eval_samples_per_second: 2271.724 - eval_steps_per_second: 143.285 - epoch: 0.01 - step: 49 | 4ae7342428fd4314b4bf3ad993dde921 |
apache-2.0 | ['generated_from_trainer'] | false | sarcasm-detection-Bert-base-uncased-POS 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: 3.1904 - Accuracy: 0.591 | d71bfa0d7df0a746c0de07ba5034e251 |
apache-2.0 | ['translation'] | false | opus-mt-sv-xh * source languages: sv * target languages: xh * OPUS readme: [sv-xh](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sv-xh/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/sv-xh/opus-2020-01-16.zip) * test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/sv-xh/opus-2020-01-16.test.txt) * test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/sv-xh/opus-2020-01-16.eval.txt) | c1562634ad6b5d0e3f3593be9f59a857 |
mit | ['generated_from_trainer'] | false | microsoft-deberta-v3-large_ner_conll2003 This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0293 - Precision: 0.9667 - Recall: 0.9724 - F1: 0.9695 - Accuracy: 0.9945 | a9716e0083ae09c3efc13735bd18cde5 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 5 | 01b921de3884b2169f9277974c53d448 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0986 | 1.0 | 878 | 0.0323 | 0.9453 | 0.9596 | 0.9524 | 0.9921 | | 0.0212 | 2.0 | 1756 | 0.0270 | 0.9571 | 0.9675 | 0.9623 | 0.9932 | | 0.009 | 3.0 | 2634 | 0.0280 | 0.9638 | 0.9714 | 0.9676 | 0.9940 | | 0.0035 | 4.0 | 3512 | 0.0290 | 0.9657 | 0.9712 | 0.9685 | 0.9943 | | 0.0022 | 5.0 | 4390 | 0.0293 | 0.9667 | 0.9724 | 0.9695 | 0.9945 | | c8a2dd46a829169d9713795455c7adab |
apache-2.0 | ['generated_from_trainer'] | false | distilgpt2-finetuned-imdb-lm This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.8512 | 069daa280ff32642a6d14bbb88eeec0b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 3.9577 | 1.0 | 7315 | 3.8818 | | 3.8965 | 2.0 | 14630 | 3.8570 | | 3.8561 | 3.0 | 21945 | 3.8512 | | 39dcaa62e7d473d959860a81ec392c58 |
apache-2.0 | ['automatic-speech-recognition', 'sv-SE'] | false | exp_w2v2t_sv-se_vp-it_s975 Fine-tuned [facebook/wav2vec2-large-it-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-it-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (sv-SE)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool. | ba6f01f474ed74027b87d4a4fd188777 |
mit | ['generated_from_trainer'] | false | roberta-large-finetuned-code-mixed-DS This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.1340 - Accuracy: 0.7203 - Precision: 0.6584 - Recall: 0.6548 - F1: 0.6558 | 6a59685800b019f17b25ba03a61e7256 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 32 - seed: 43 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 | 1bd27eacc11ceb46219fb7174b2ee178 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | 0.9729 | 1.0 | 248 | 0.7491 | 0.6922 | 0.6434 | 0.6625 | 0.6358 | | 0.7474 | 1.99 | 496 | 0.6947 | 0.7183 | 0.6712 | 0.6915 | 0.6760 | | 0.5938 | 2.99 | 744 | 0.7370 | 0.7123 | 0.6624 | 0.6839 | 0.6642 | | 0.4264 | 3.98 | 992 | 0.8820 | 0.7123 | 0.6540 | 0.6636 | 0.6492 | | 0.2806 | 4.98 | 1240 | 1.2022 | 0.7404 | 0.6807 | 0.6694 | 0.6742 | | 0.2239 | 5.98 | 1488 | 1.3933 | 0.7223 | 0.6593 | 0.6587 | 0.6568 | | 0.1585 | 6.97 | 1736 | 1.8543 | 0.7304 | 0.6730 | 0.6763 | 0.6737 | | 0.1302 | 7.97 | 1984 | 2.0783 | 0.7143 | 0.6495 | 0.6520 | 0.6504 | | 0.1008 | 8.96 | 2232 | 2.3523 | 0.7183 | 0.6588 | 0.6561 | 0.6552 | | 0.0793 | 9.96 | 2480 | 2.5260 | 0.7163 | 0.6516 | 0.6566 | 0.6538 | | 0.0498 | 10.96 | 2728 | 2.6074 | 0.7425 | 0.6902 | 0.6817 | 0.6830 | | 0.0484 | 11.95 | 2976 | 2.6758 | 0.7284 | 0.6687 | 0.6734 | 0.6709 | | 0.0409 | 12.95 | 3224 | 2.8658 | 0.7425 | 0.6817 | 0.6756 | 0.6781 | | 0.0239 | 13.94 | 3472 | 2.9484 | 0.7465 | 0.6980 | 0.6818 | 0.6870 | | 0.025 | 14.94 | 3720 | 3.0827 | 0.7304 | 0.6778 | 0.6577 | 0.6641 | | 0.0286 | 15.94 | 3968 | 3.0011 | 0.7183 | 0.6509 | 0.6475 | 0.6491 | | 0.0264 | 16.93 | 4216 | 3.1581 | 0.7264 | 0.6645 | 0.6563 | 0.6595 | | 0.009 | 17.93 | 4464 | 3.1200 | 0.7223 | 0.6589 | 0.6561 | 0.6569 | | 0.012 | 18.92 | 4712 | 3.1364 | 0.7203 | 0.6573 | 0.6503 | 0.6525 | | 0.017 | 19.92 | 4960 | 3.1340 | 0.7203 | 0.6584 | 0.6548 | 0.6558 | | 2913e542d6be06f564d36df187ee783f |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2214 - Accuracy: 0.9275 - F1: 0.9274 | 2a692206e4111e57aea849f0e69f1c74 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8568 | 1.0 | 250 | 0.3328 | 0.9 | 0.8947 | | 0.2576 | 2.0 | 500 | 0.2214 | 0.9275 | 0.9274 | | 117e967171d8761d5588d79cf73e10c4 |
bsd-3-clause | [] | false | Model description CodeGen is a family of autoregressive language models for **program synthesis** from the paper: [A Conversational Paradigm for Program Synthesis](https://arxiv.org/abs/2203.13474) by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, Caiming Xiong. The models are originally released in [this repository](https://github.com/salesforce/CodeGen), under 3 pre-training data variants (`NL`, `Multi`, `Mono`) and 4 model size variants (`350M`, `2B`, `6B`, `16B`). The checkpoint included in this repository is denoted as **CodeGen-Mono 6B** in the paper, where "Mono" means the model is initialized with *CodeGen-Multi 6B* and further pre-trained on a Python programming language dataset, and "6B" refers to the number of trainable parameters. | 14c3a073ee1120d5405fe75bd9716fab |
bsd-3-clause | [] | false | Training data This checkpoint (CodeGen-Mono 6B) was firstly initialized with *CodeGen-Multi 6B*, and then pre-trained on BigPython dataset. The data consists of 71.7B tokens of Python programming language. See Section 2.1 of the [paper](https://arxiv.org/abs/2203.13474) for more details. | 541e6c244c008c8793f28ee70354d4e7 |
bsd-3-clause | [] | false | How to use This model can be easily loaded using the `AutoModelForCausalLM` functionality: ```python from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Salesforce/codegen-6B-mono") model = AutoModelForCausalLM.from_pretrained("Salesforce/codegen-6B-mono") text = "def hello_world():" input_ids = tokenizer(text, return_tensors="pt").input_ids generated_ids = model.generate(input_ids, max_length=128) print(tokenizer.decode(generated_ids[0], skip_special_tokens=True)) ``` | 4271680068ffb19041edf87c678a81d5 |
mit | [] | false | Italian BERT The source data for the Italian BERT model consists of a recent Wikipedia dump and various texts from the [OPUS corpora](http://opus.nlpl.eu/) collection. The final training corpus has a size of 13GB and 2,050,057,573 tokens. For sentence splitting, we use NLTK (faster compared to spacy). Our cased and uncased models are training with an initial sequence length of 512 subwords for ~2-3M steps. For the XXL Italian models, we use the same training data from OPUS and extend it with data from the Italian part of the [OSCAR corpus](https://traces1.inria.fr/oscar/). Thus, the final training corpus has a size of 81GB and 13,138,379,147 tokens. Note: Unfortunately, a wrong vocab size was used when training the XXL models. This explains the mismatch of the "real" vocab size of 31102, compared to the vocab size specified in `config.json`. However, the model is working and all evaluations were done under those circumstances. See [this issue](https://github.com/dbmdz/berts/issues/7) for more information. The Italian ELECTRA model was trained on the "XXL" corpus for 1M steps in total using a batch size of 128. We pretty much following the ELECTRA training procedure as used for [BERTurk](https://github.com/stefan-it/turkish-bert/tree/master/electra). | a4c9ec6ec72c5c0a4c394de1282f454d |
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-italian-cased` | [`config.json`](https://cdn.huggingface.co/dbmdz/bert-base-italian-cased/config.json) • [`pytorch_model.bin`](https://cdn.huggingface.co/dbmdz/bert-base-italian-cased/pytorch_model.bin) • [`vocab.txt`](https://cdn.huggingface.co/dbmdz/bert-base-italian-cased/vocab.txt) | `dbmdz/bert-base-italian-uncased` | [`config.json`](https://cdn.huggingface.co/dbmdz/bert-base-italian-uncased/config.json) • [`pytorch_model.bin`](https://cdn.huggingface.co/dbmdz/bert-base-italian-uncased/pytorch_model.bin) • [`vocab.txt`](https://cdn.huggingface.co/dbmdz/bert-base-italian-uncased/vocab.txt) | `dbmdz/bert-base-italian-xxl-cased` | [`config.json`](https://cdn.huggingface.co/dbmdz/bert-base-italian-xxl-cased/config.json) • [`pytorch_model.bin`](https://cdn.huggingface.co/dbmdz/bert-base-italian-xxl-cased/pytorch_model.bin) • [`vocab.txt`](https://cdn.huggingface.co/dbmdz/bert-base-italian-xxl-cased/vocab.txt) | `dbmdz/bert-base-italian-xxl-uncased` | [`config.json`](https://cdn.huggingface.co/dbmdz/bert-base-italian-xxl-uncased/config.json) • [`pytorch_model.bin`](https://cdn.huggingface.co/dbmdz/bert-base-italian-xxl-uncased/pytorch_model.bin) • [`vocab.txt`](https://cdn.huggingface.co/dbmdz/bert-base-italian-xxl-uncased/vocab.txt) | `dbmdz/electra-base-italian-xxl-cased-discriminator` | [`config.json`](https://s3.amazonaws.com/models.huggingface.co/bert/dbmdz/electra-base-italian-xxl-cased-discriminator/config.json) • [`pytorch_model.bin`](https://cdn.huggingface.co/dbmdz/electra-base-italian-xxl-cased-discriminator/pytorch_model.bin) • [`vocab.txt`](https://cdn.huggingface.co/dbmdz/electra-base-italian-xxl-cased-discriminator/vocab.txt) | `dbmdz/electra-base-italian-xxl-cased-generator` | [`config.json`](https://s3.amazonaws.com/models.huggingface.co/bert/dbmdz/electra-base-italian-xxl-cased-generator/config.json) • [`pytorch_model.bin`](https://cdn.huggingface.co/dbmdz/electra-base-italian-xxl-cased-generator/pytorch_model.bin) • [`vocab.txt`](https://cdn.huggingface.co/dbmdz/electra-base-italian-xxl-cased-generator/vocab.txt) | 8bd5d0476e64f7c4781169404739bfeb |
mit | [] | false | Usage With Transformers >= 2.3 our Italian BERT models can be loaded like: ```python from transformers import AutoModel, AutoTokenizer model_name = "dbmdz/bert-base-italian-cased" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModel.from_pretrained(model_name) ``` To load the (recommended) Italian XXL BERT models, just use: ```python from transformers import AutoModel, AutoTokenizer model_name = "dbmdz/bert-base-italian-xxl-cased" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModel.from_pretrained(model_name) ``` To load the Italian XXL ELECTRA model (discriminator), just use: ```python from transformers import AutoModel, AutoTokenizer model_name = "dbmdz/electra-base-italian-xxl-cased-discriminator" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelWithLMHead.from_pretrained(model_name) ``` | 485fe184a45506443ea6aa0adf9082f5 |
mit | [] | false | Acknowledgments Research supported with Cloud TPUs from Google's TensorFlow Research Cloud (TFRC). Thanks for providing access to the TFRC ❤️ Thanks to the generous support from the [Hugging Face](https://huggingface.co/) team, it is possible to download both cased and uncased models from their S3 storage 🤗 | 3e14c990444ad72e6c08e244ad6b36d0 |
mit | ['vision'] | false | GIT (GenerativeImage2Text), large-sized, fine-tuned on TextVQA GIT (short for GenerativeImage2Text) model, large-sized version, fine-tuned on TextVQA. It was introduced in the paper [GIT: A Generative Image-to-text Transformer for Vision and Language](https://arxiv.org/abs/2205.14100) by Wang et al. and first released in [this repository](https://github.com/microsoft/GenerativeImage2Text). Disclaimer: The team releasing GIT did not write a model card for this model so this model card has been written by the Hugging Face team. | 751110eb7303e9f077de1b01ec19800c |
mit | ['vision'] | false | Intended uses & limitations You can use the raw model for visual question answering (VQA). See the [model hub](https://huggingface.co/models?search=microsoft/git) to look for fine-tuned versions on a task that interests you. | cc1683e448b6f54516b9b8dbb77876cb |
mit | ['vision'] | false | Training data From the paper: > We collect 0.8B image-text pairs for pre-training, which include COCO (Lin et al., 2014), Conceptual Captions (CC3M) (Sharma et al., 2018), SBU (Ordonez et al., 2011), Visual Genome (VG) (Krishna et al., 2016), Conceptual Captions (CC12M) (Changpinyo et al., 2021), ALT200M (Hu et al., 2021a), and an extra 0.6B data following a similar collection procedure in Hu et al. (2021a). => however this is for the model referred to as "GIT" in the paper, which is not open-sourced. This checkpoint is "GIT-large", which is a smaller variant of GIT trained on 20 million image-text pairs. Next, the model was fine-tuned on TextVQA. See table 11 in the [paper](https://arxiv.org/abs/2205.14100) for more details. | f6323826fe4a2dffe88a87725626a773 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-imdb 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: 2.4722 | 2da3f69ea2c8ecb2634ae7d32711c117 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.7117 | 1.0 | 157 | 2.4977 | | 2.5783 | 2.0 | 314 | 2.4241 | | 2.5375 | 3.0 | 471 | 2.4358 | | fa935f5a81f0cf6a8bcb25e106bac180 |
mit | [] | false | Model Details **Model Description:** RoBERTa base OpenAI Detector is the GPT-2 output detector model, obtained by fine-tuning a RoBERTa base model with the outputs of the 1.5B-parameter GPT-2 model. The model can be used to predict if text was generated by a GPT-2 model. This model was released by OpenAI at the same time as OpenAI released the weights of the [largest GPT-2 model](https://huggingface.co/gpt2-xl), the 1.5B parameter version. - **Developed by:** OpenAI, see [GitHub Repo](https://github.com/openai/gpt-2-output-dataset/tree/master/detector) and [associated paper](https://d4mucfpksywv.cloudfront.net/papers/GPT_2_Report.pdf) for full author list - **Model Type:** Fine-tuned transformer-based language model - **Language(s):** English - **License:** MIT - **Related Models:** [RoBERTa base](https://huggingface.co/roberta-base), [GPT-XL (1.5B parameter version)](https://huggingface.co/gpt2-xl), [GPT-Large (the 774M parameter version)](https://huggingface.co/gpt2-large), [GPT-Medium (the 355M parameter version)](https://huggingface.co/gpt2-medium) and [GPT-2 (the 124M parameter version)](https://huggingface.co/gpt2) - **Resources for more information:** - [Research Paper](https://d4mucfpksywv.cloudfront.net/papers/GPT_2_Report.pdf) (see, in particular, the section beginning on page 12 about Automated ML-based detection). - [GitHub Repo](https://github.com/openai/gpt-2-output-dataset/tree/master/detector) - [OpenAI Blog Post](https://openai.com/blog/gpt-2-1-5b-release/) - [Explore the detector model here](https://huggingface.co/openai-detector ) | 84612bbf7f542457fd19809ab16ac5ce |
mit | [] | false | Downstream Use The model's developers have stated that they developed and released the model to help with research related to synthetic text generation, so the model could potentially be used for downstream tasks related to synthetic text generation. See the [associated paper](https://d4mucfpksywv.cloudfront.net/papers/GPT_2_Report.pdf) for further discussion. | 229065d4b416603af937b1dadc44e5b9 |
mit | [] | false | Misuse and Out-of-scope Use The model should not be used to intentionally create hostile or alienating environments for people. In addition, the model developers discuss the risk of adversaries using the model to better evade detection in their [associated paper](https://d4mucfpksywv.cloudfront.net/papers/GPT_2_Report.pdf), suggesting that using the model for evading detection or for supporting efforts to evade detection would be a misuse of the model. | 34338f5bb0901cc0db4744f24d3c8865 |
mit | [] | false | Risks, Limitations and Biases **CONTENT WARNING: Readers should be aware this section may contain content that is disturbing, offensive, and can propagate historical and current stereotypes.** Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. | 176761778958ef627e18fbec06c98c27 |
mit | [] | false | Risks and Limitations In their [associated paper](https://d4mucfpksywv.cloudfront.net/papers/GPT_2_Report.pdf), the model developers discuss the risk that the model may be used by bad actors to develop capabilities for evading detection, though one purpose of releasing the model is to help improve detection research. In a related [blog post](https://openai.com/blog/gpt-2-1-5b-release/), the model developers also discuss the limitations of automated methods for detecting synthetic text and the need to pair automated detection tools with other, non-automated approaches. They write: > We conducted in-house detection research and developed a detection model that has detection rates of ~95% for detecting 1.5B GPT-2-generated text. We believe this is not high enough accuracy for standalone detection and needs to be paired with metadata-based approaches, human judgment, and public education to be more effective. The model developers also [report](https://openai.com/blog/gpt-2-1-5b-release/) finding that classifying content from larger models is more difficult, suggesting that detection with automated tools like this model will be increasingly difficult as model sizes increase. The authors find that training detector models on the outputs of larger models can improve accuracy and robustness. | 5550c9fa3c513164f9f95b02b3f9b0eb |
mit | [] | false | Bias Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). Predictions generated by RoBERTa base and GPT-2 1.5B (which this model is built/fine-tuned on) can include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups (see the [RoBERTa base](https://huggingface.co/roberta-base) and [GPT-2 XL](https://huggingface.co/gpt2-xl) model cards for more information). The developers of this model discuss these issues further in their [paper](https://d4mucfpksywv.cloudfront.net/papers/GPT_2_Report.pdf). | 07d9a6ca6ef966493c28d41cf69a3c48 |
mit | [] | false | Training Data The model is a sequence classifier based on RoBERTa base (see the [RoBERTa base model card](https://huggingface.co/roberta-base) for more details on the RoBERTa base training data) and then fine-tuned using the outputs of the 1.5B GPT-2 model (available [here](https://github.com/openai/gpt-2-output-dataset)). | 859e79409bc56e8b1b892fefb4704a87 |
mit | [] | false | Training Procedure The model developers write that: > We based a sequence classifier on RoBERTaBASE (125 million parameters) and fine-tuned it to classify the outputs from the 1.5B GPT-2 model versus WebText, the dataset we used to train the GPT-2 model. They later state: > To develop a robust detector model that can accurately classify generated texts regardless of the sampling method, we performed an analysis of the model’s transfer performance. See the [associated paper](https://d4mucfpksywv.cloudfront.net/papers/GPT_2_Report.pdf) for further details on the training procedure. | 1506de0f0c83571d5eed9740f797293b |
mit | [] | false | Testing Data, Factors and Metrics The model is intended to be used for detecting text generated by GPT-2 models, so the model developers test the model on text datasets, measuring accuracy by: > testing 510-token test examples comprised of 5,000 samples from the WebText dataset and 5,000 samples generated by a GPT-2 model, which were not used during the training. | 4213d6a4bfbc86de4b591da39a83d7d1 |
mit | [] | false | Results The model developers [find](https://d4mucfpksywv.cloudfront.net/papers/GPT_2_Report.pdf): > Our classifier is able to detect 1.5 billion parameter GPT-2-generated text with approximately 95% accuracy...The model’s accuracy depends on sampling methods used when generating outputs, like temperature, Top-K, and nucleus sampling ([Holtzman et al., 2019](https://arxiv.org/abs/1904.09751). Nucleus sampling outputs proved most difficult to correctly classify, but a detector trained using nucleus sampling transfers well across other sampling methods. As seen in Figure 1 [in the paper], we found consistently high accuracy when trained on nucleus sampling. See the [associated paper](https://d4mucfpksywv.cloudfront.net/papers/GPT_2_Report.pdf), Figure 1 (on page 14) and Figure 2 (on page 16) for full results. | 22d59bd4d6ceb56463b2648af9236f1f |
mit | [] | false | compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** Unknown - **Hours used:** Unknown - **Cloud Provider:** Unknown - **Compute Region:** Unknown - **Carbon Emitted:** Unknown | 92f85173b1d742c1cffa9aa1188f58d9 |
mit | [] | false | Technical Specifications The model developers write that: See the [associated paper](https://d4mucfpksywv.cloudfront.net/papers/GPT_2_Report.pdf) for further details on the modeling architecture and training details. | 649bdb209b415142cbe15af2d2da9f54 |
mit | [] | false | Citation Information ```bibtex @article{solaiman2019release, title={Release strategies and the social impacts of language models}, author={Solaiman, Irene and Brundage, Miles and Clark, Jack and Askell, Amanda and Herbert-Voss, Ariel and Wu, Jeff and Radford, Alec and Krueger, Gretchen and Kim, Jong Wook and Kreps, Sarah and others}, journal={arXiv preprint arXiv:1908.09203}, year={2019} } ``` APA: - Solaiman, I., Brundage, M., Clark, J., Askell, A., Herbert-Voss, A., Wu, J., ... & Wang, J. (2019). Release strategies and the social impacts of language models. arXiv preprint arXiv:1908.09203. | d4a12b6f633c2bb40c0e19a4301c0b7b |
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 None dataset. It achieves the following results on the evaluation set: - Loss: 0.0826 - Accuracy: 0.9761 - Precision: 0.9727 - Recall: 0.9654 - F1: 0.9691 | d28c8f82ecdbbb62f7161f96333eed97 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Wav2Vec2-Large-XLSR-53-Breton Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Breton using the [Common Voice](https://huggingface.co/datasets/common_voice) When using this model, make sure that your speech input is sampled at 16kHz. | 2cec800c0ec89fa1fdcc5a182018e238 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Usage The model can be used directly (without a language model) as follows: ```python import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor test_dataset = load_dataset("common_voice", "br", split="test[:2%]"). processor = Wav2Vec2Processor.from_pretrained("manandey/wav2vec2-large-xlsr-breton") model = Wav2Vec2ForCTC.from_pretrained("manandey/wav2vec2-large-xlsr-breton") resampler = torchaudio.transforms.Resample(48_000, 16_000) | 4f2fbb4cea77a35233c2c61aaf366692 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Evaluation The model can be evaluated as follows on the {language} test data of Common Voice. ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re test_dataset = load_dataset("common_voice", "br", split="test") wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("manandey/wav2vec2-large-xlsr-breton") model = Wav2Vec2ForCTC.from_pretrained("manandey/wav2vec2-large-xlsr-breton") model.to("cuda") chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\”\�\’\–\(\)\/\«\»\½\…]' resampler = torchaudio.transforms.Resample(48_000, 16_000) | d2a43cf9f4a3198338f3b3bd122ea22f |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | We need to read the aduio files as arrays def speech_file_to_array_fn(batch): batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) | 1c54da87bb35cbcc02764faf59a58c90 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | We need to read the aduio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch.argmax(logits, dim=-1) batch["pred_strings"] = processor.batch_decode(pred_ids) return batch result = test_dataset.map(evaluate, batched=True, batch_size=8) print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"]))) ``` **Test Result**: 54.04% | d13b103d571d2554f93f98a51a648eb4 |
mit | ['generated_from_trainer'] | false | bart-large-cnn-finetuned-roundup-2-2 This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.1521 - Rouge1: 52.6634 - Rouge2: 32.537 - Rougel: 33.3148 - Rougelsum: 50.148 - Gen Len: 142.0 | 66b40cd46a21d2a3d4e514f2b025db7e |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 - mixed_precision_training: Native AMP | 0cfeb50b16369f84740a92d25bf14d29 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | No log | 1.0 | 167 | 1.2139 | 52.546 | 32.4912 | 32.9529 | 49.8241 | 142.0 | | No log | 2.0 | 334 | 1.1521 | 52.6634 | 32.537 | 33.3148 | 50.148 | 142.0 | | 8b8e77eeb37fc4eb30bba3643e6e9875 |
mit | ['generated_from_trainer'] | false | camembert-base-finetuned-sans-symbole-dd This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2642 - Precision: 0.8856 - Recall: 0.9176 - F1: 0.9013 - Accuracy: 0.9364 | e1b126a0389fb24d3750c41707779063 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.1961 | 1.0 | 4317 | 0.2216 | 0.8675 | 0.9039 | 0.8853 | 0.9319 | | 0.161 | 2.0 | 8634 | 0.2243 | 0.8614 | 0.9158 | 0.8878 | 0.9237 | | 0.1169 | 3.0 | 12951 | 0.2507 | 0.8752 | 0.9154 | 0.8949 | 0.9329 | | 0.0875 | 4.0 | 17268 | 0.2642 | 0.8856 | 0.9176 | 0.9013 | 0.9364 | | 0bb5b6726acac3b52c4acea0d41c63a6 |
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: 0.1699 - F1: 0.8725 | 6b7f51781f12c1507acd66687748c2b7 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 | 16fea87efb8666f6c725e38b2d447d2b |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.5975 | 1.0 | 191 | 0.2612 | 0.8237 | | 0.2798 | 2.0 | 382 | 0.1699 | 0.8725 | | c2ec34fca873a08845376d287060a735 |
cc-by-sa-4.0 | ['coptic', 'token-classification', 'pos', 'dependency-parsing'] | false | Model Description This is a RoBERTa model pre-trained with [UD_Coptic](https://universaldependencies.org/cop/) for POS-tagging and dependency-parsing, derived from [roberta-base-coptic](https://huggingface.co/KoichiYasuoka/roberta-base-coptic). Every word is tagged by [UPOS](https://universaldependencies.org/u/pos/) (Universal Part-Of-Speech). | 71e1acdbdea777cf9b8a7f1702caedcf |
cc-by-sa-4.0 | ['coptic', 'token-classification', 'pos', 'dependency-parsing'] | false | How to Use ```py from transformers import AutoTokenizer,AutoModelForTokenClassification tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/roberta-base-coptic-upos") model=AutoModelForTokenClassification.from_pretrained("KoichiYasuoka/roberta-base-coptic-upos") ``` or ``` import esupar nlp=esupar.load("KoichiYasuoka/roberta-base-coptic-upos") ``` | 006ac87629364cb09d0b84b59c16ae9b |
afl-3.0 | [] | false | Example of usage: ```python from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("kyryl0s/gpt2-uk-xxs") model = AutoModelForCausalLM.from_pretrained("kyryl0s/gpt2-uk-xxs") input_ids = tokenizer.encode("Путін — ", add_special_tokens=False, return_tensors='pt') outputs = model.generate( input_ids, do_sample=True, num_return_sequences=3, max_length=50 ) for i, out in enumerate(outputs): print("{}: {}".format(i, tokenizer.decode(out))) ``` | c41e617a8c51a8713ffdf33d22beca98 |
apache-2.0 | ['generated_from_trainer'] | false | distilbart-cnn-6-6-finetuned-xsum-intro-test This model is a fine-tuned version of [sshleifer/distilbart-cnn-6-6](https://huggingface.co/sshleifer/distilbart-cnn-6-6) on the xsum dataset. It achieves the following results on the evaluation set: - Loss: 1.9036 - Rouge1: 32.0474 - Rouge2: 12.3779 - Rougel: 23.5491 - Rougelsum: 24.251 - Gen Len: 60.8594 | aa47502aa82a7f53b25e4fff85512e41 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 1.9432 | 1.0 | 12753 | 1.9036 | 32.0474 | 12.3779 | 23.5491 | 24.251 | 60.8594 | | 109ef3f646171fb24b81322d595dff10 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2209 - Accuracy: 0.9225 - F1: 0.9226 | 67dc8224c7f26f158270c6403ab91d6c |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8477 | 1.0 | 250 | 0.3204 | 0.9025 | 0.9000 | | 0.2559 | 2.0 | 500 | 0.2209 | 0.9225 | 0.9226 | | 9e427b35faa86db72614e09302942a50 |
mit | ['generated_from_trainer'] | false | distilbert-base-turkish-cased-finetuned-emotion This model is a fine-tuned version of [dbmdz/distilbert-base-turkish-cased](https://huggingface.co/dbmdz/distilbert-base-turkish-cased) on the turkish-multiclass-dataset dataset. It achieves the following results on the evaluation set: - Loss: 0.4861 - F1: {'f1': 0.8276613385259164} | 124128da502ebca2770793fe867b1dca |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------------------------:| | 0.2578 | 1.0 | 313 | 0.5459 | {'f1': 0.8212239281513611} | | 0.381 | 2.0 | 626 | 0.4861 | {'f1': 0.8276613385259164} | | 970d08625cd1e32e66fce11bdccf34cf |
creativeml-openrail-m | ['text-to-image'] | false | noggles6000 on Stable Diffusion via Dreambooth trained on the [fast-DreamBooth.ipynb by TheLastBen](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook | a18e8d2a7793ca60c6c942668ac598c5 |
creativeml-openrail-m | ['text-to-image'] | false | Model by alxdfy This your the Stable Diffusion model fine-tuned the noggles6000 concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt(s)`: **nounsbud.jpg** You can also train your own concepts and upload them to the library by using [the fast-DremaBooth.ipynb by TheLastBen](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb). You can run your new concept via A1111 Colab :[Fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Or you can 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), [Spaces with the Public Concepts loaded](https://huggingface.co/spaces/sd-dreambooth-library/stable-diffusion-dreambooth-concepts) Sample pictures of this concept: nounsbud.jpg  | 2bae90acdfc29bff3268265fd4013f0b |
apache-2.0 | ['translation'] | false | opus-mt-ro-fi * source languages: ro * target languages: fi * OPUS readme: [ro-fi](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/ro-fi/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/ro-fi/opus-2020-01-16.zip) * test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/ro-fi/opus-2020-01-16.test.txt) * test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/ro-fi/opus-2020-01-16.eval.txt) | c38bed1ae6875cef924c68e3a98c02f5 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | opus-mt-tc-big-he-en Neural machine translation model for translating from Hebrew (he) 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 are originally trained using the amazing framework of [Marian NMT](https://marian-nmt.github.io/), an efficient NMT implementation written in pure C++. The models have been converted to pyTorch using the transformers library by huggingface. Training data is taken from [OPUS](https://opus.nlpl.eu/) and training pipelines use the procedures of [OPUS-MT-train](https://github.com/Helsinki-NLP/Opus-MT-train). * Publications: [OPUS-MT – Building open translation services for the World](https://aclanthology.org/2020.eamt-1.61/) and [The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT](https://aclanthology.org/2020.wmt-1.139/) (Please, cite if you use this model.) ``` @inproceedings{tiedemann-thottingal-2020-opus, title = "{OPUS}-{MT} {--} Building open translation services for the World", author = {Tiedemann, J{\"o}rg and Thottingal, Santhosh}, booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation", month = nov, year = "2020", address = "Lisboa, Portugal", publisher = "European Association for Machine Translation", url = "https://aclanthology.org/2020.eamt-1.61", pages = "479--480", } @inproceedings{tiedemann-2020-tatoeba, title = "The Tatoeba Translation Challenge {--} Realistic Data Sets for Low Resource and Multilingual {MT}", author = {Tiedemann, J{\"o}rg}, booktitle = "Proceedings of the Fifth Conference on Machine Translation", month = nov, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2020.wmt-1.139", pages = "1174--1182", } ``` | 956855ade5e85c9b7b96d37e04dd7c44 |
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