Datasets:
davanstrien
/
pytorchmodelmetadata

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Harveenchadha/vakyansh-wav2vec2-rajasthani-raj-45
fc46dea3821e439ca109f613e771344c5820b3a7
2021-12-17T17:58:17.000Z
[ "pytorch", "wav2vec2", "automatic-speech-recognition", "transformers" ]
automatic-speech-recognition
false
Harveenchadha
null
Harveenchadha/vakyansh-wav2vec2-rajasthani-raj-45
2
null
transformers
23,100
Entry not found
Harveenchadha/vakyansh-wav2vec2-telugu-tem-100
f0b4778462800eaa70163bfee6bf97710cc28f27
2021-08-02T19:00:30.000Z
[ "pytorch", "wav2vec2", "automatic-speech-recognition", "transformers" ]
automatic-speech-recognition
false
Harveenchadha
null
Harveenchadha/vakyansh-wav2vec2-telugu-tem-100
2
null
transformers
23,101
Entry not found
Heldhy/testingAgain
6f277ff120f2c32823a7a82e5c56e1cc628e4e79
2022-01-10T13:05:14.000Z
[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]
conversational
false
Heldhy
null
Heldhy/testingAgain
2
null
transformers
23,102
--- tags: - conversational --- # My Awesome Model
Heldhy/wav2vec2-base-timit-demo-colab
e87cccf584fe123d621686472f288fb2b914642a
2022-01-10T14:36:58.000Z
[ "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
automatic-speech-recognition
false
Heldhy
null
Heldhy/wav2vec2-base-timit-demo-colab
2
null
transformers
23,103
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-base-timit-demo-colab results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-base-timit-demo-colab This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4568 - Wer: 0.3422 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.3896 | 4.0 | 500 | 1.1573 | 0.8886 | | 0.5667 | 8.0 | 1000 | 0.4841 | 0.4470 | | 0.2126 | 12.0 | 1500 | 0.4201 | 0.3852 | | 0.1235 | 16.0 | 2000 | 0.4381 | 0.3623 | | 0.0909 | 20.0 | 2500 | 0.4784 | 0.3748 | | 0.0611 | 24.0 | 3000 | 0.4390 | 0.3577 | | 0.0454 | 28.0 | 3500 | 0.4568 | 0.3422 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.13.3 - Tokenizers 0.10.3
Helsinki-NLP/opus-mt-bcl-fr
3802edebeca6853fe87f6f0f6aa77437cd5c3846
2021-09-09T21:26:56.000Z
[ "pytorch", "marian", "text2text-generation", "bcl", "fr", "transformers", "translation", "license:apache-2.0", "autotrain_compatible" ]
translation
false
Helsinki-NLP
null
Helsinki-NLP/opus-mt-bcl-fr
2
null
transformers
23,104
--- tags: - translation license: apache-2.0 --- ### opus-mt-bcl-fr * source languages: bcl * target languages: fr * OPUS readme: [bcl-fr](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/bcl-fr/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-08.zip](https://object.pouta.csc.fi/OPUS-MT-models/bcl-fr/opus-2020-01-08.zip) * test set translations: [opus-2020-01-08.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/bcl-fr/opus-2020-01-08.test.txt) * test set scores: [opus-2020-01-08.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/bcl-fr/opus-2020-01-08.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | JW300.bcl.fr | 35.0 | 0.527 |
Helsinki-NLP/opus-mt-bg-uk
4615b57ec32e8f73c9a69d19c512f7d260ff7b91
2021-01-18T07:51:27.000Z
[ "pytorch", "marian", "text2text-generation", "bg", "uk", "transformers", "translation", "license:apache-2.0", "autotrain_compatible" ]
translation
false
Helsinki-NLP
null
Helsinki-NLP/opus-mt-bg-uk
2
null
transformers
23,105
--- language: - bg - uk tags: - translation license: apache-2.0 --- ### bul-ukr * source group: Bulgarian * target group: Ukrainian * OPUS readme: [bul-ukr](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/bul-ukr/README.md) * model: transformer-align * source language(s): bul * target language(s): ukr * model: transformer-align * pre-processing: normalization + SentencePiece (spm32k,spm32k) * download original weights: [opus-2020-06-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/bul-ukr/opus-2020-06-17.zip) * test set translations: [opus-2020-06-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/bul-ukr/opus-2020-06-17.test.txt) * test set scores: [opus-2020-06-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/bul-ukr/opus-2020-06-17.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | Tatoeba-test.bul.ukr | 49.2 | 0.683 | ### System Info: - hf_name: bul-ukr - source_languages: bul - target_languages: ukr - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/bul-ukr/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['bg', 'uk'] - src_constituents: {'bul', 'bul_Latn'} - tgt_constituents: {'ukr'} - src_multilingual: False - tgt_multilingual: False - prepro: normalization + SentencePiece (spm32k,spm32k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/bul-ukr/opus-2020-06-17.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/bul-ukr/opus-2020-06-17.test.txt - src_alpha3: bul - tgt_alpha3: ukr - short_pair: bg-uk - chrF2_score: 0.6829999999999999 - bleu: 49.2 - brevity_penalty: 0.983 - ref_len: 4932.0 - src_name: Bulgarian - tgt_name: Ukrainian - train_date: 2020-06-17 - src_alpha2: bg - tgt_alpha2: uk - prefer_old: False - long_pair: bul-ukr - helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535 - transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b - port_machine: brutasse - port_time: 2020-08-21-14:41
Helsinki-NLP/opus-mt-cs-eo
1e23bed4e0074c567e4508059f4b8034e0319105
2021-01-18T07:55:41.000Z
[ "pytorch", "marian", "text2text-generation", "cs", "eo", "transformers", "translation", "license:apache-2.0", "autotrain_compatible" ]
translation
false
Helsinki-NLP
null
Helsinki-NLP/opus-mt-cs-eo
2
null
transformers
23,106
--- language: - cs - eo tags: - translation license: apache-2.0 --- ### ces-epo * source group: Czech * target group: Esperanto * OPUS readme: [ces-epo](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ces-epo/README.md) * model: transformer-align * source language(s): ces * target language(s): epo * model: transformer-align * pre-processing: normalization + SentencePiece (spm4k,spm4k) * download original weights: [opus-2020-06-16.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/ces-epo/opus-2020-06-16.zip) * test set translations: [opus-2020-06-16.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/ces-epo/opus-2020-06-16.test.txt) * test set scores: [opus-2020-06-16.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/ces-epo/opus-2020-06-16.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | Tatoeba-test.ces.epo | 26.0 | 0.459 | ### System Info: - hf_name: ces-epo - source_languages: ces - target_languages: epo - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ces-epo/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['cs', 'eo'] - src_constituents: {'ces'} - tgt_constituents: {'epo'} - src_multilingual: False - tgt_multilingual: False - prepro: normalization + SentencePiece (spm4k,spm4k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/ces-epo/opus-2020-06-16.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/ces-epo/opus-2020-06-16.test.txt - src_alpha3: ces - tgt_alpha3: epo - short_pair: cs-eo - chrF2_score: 0.45899999999999996 - bleu: 26.0 - brevity_penalty: 0.94 - ref_len: 24901.0 - src_name: Czech - tgt_name: Esperanto - train_date: 2020-06-16 - src_alpha2: cs - tgt_alpha2: eo - prefer_old: False - long_pair: ces-epo - helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535 - transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b - port_machine: brutasse - port_time: 2020-08-21-14:41
Helsinki-NLP/opus-mt-de-lt
e0105109d696baf37e2a4cca511a46f59fa97707
2021-09-09T21:32:20.000Z
[ "pytorch", "marian", "text2text-generation", "de", "lt", "transformers", "translation", "license:apache-2.0", "autotrain_compatible" ]
translation
false
Helsinki-NLP
null
Helsinki-NLP/opus-mt-de-lt
2
null
transformers
23,107
--- tags: - translation license: apache-2.0 --- ### opus-mt-de-lt * source languages: de * target languages: lt * OPUS readme: [de-lt](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/de-lt/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-20.zip](https://object.pouta.csc.fi/OPUS-MT-models/de-lt/opus-2020-01-20.zip) * test set translations: [opus-2020-01-20.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/de-lt/opus-2020-01-20.test.txt) * test set scores: [opus-2020-01-20.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/de-lt/opus-2020-01-20.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | Tatoeba.de.lt | 37.9 | 0.633 |
Helsinki-NLP/opus-mt-de-ny
595549133dfde470a3ea04e93674ff1c90c5ac5a
2021-09-09T21:32:42.000Z
[ "pytorch", "marian", "text2text-generation", "de", "ny", "transformers", "translation", "license:apache-2.0", "autotrain_compatible" ]
translation
false
Helsinki-NLP
null
Helsinki-NLP/opus-mt-de-ny
2
null
transformers
23,108
--- tags: - translation license: apache-2.0 --- ### opus-mt-de-ny * source languages: de * target languages: ny * OPUS readme: [de-ny](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/de-ny/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-20.zip](https://object.pouta.csc.fi/OPUS-MT-models/de-ny/opus-2020-01-20.zip) * test set translations: [opus-2020-01-20.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/de-ny/opus-2020-01-20.test.txt) * test set scores: [opus-2020-01-20.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/de-ny/opus-2020-01-20.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | JW300.de.ny | 21.4 | 0.481 |
Helsinki-NLP/opus-mt-en-pqw
e63c061ce57192d261cc19a46c0fe0c2678eb790
2021-01-18T08:14:49.000Z
[ "pytorch", "marian", "text2text-generation", "en", "pqw", "transformers", "translation", "license:apache-2.0", "autotrain_compatible" ]
translation
false
Helsinki-NLP
null
Helsinki-NLP/opus-mt-en-pqw
2
null
transformers
23,109
--- language: - en - pqw tags: - translation license: apache-2.0 --- ### eng-pqw * source group: English * target group: Western Malayo-Polynesian languages * OPUS readme: [eng-pqw](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-pqw/README.md) * model: transformer * source language(s): eng * target language(s): akl_Latn ceb cha dtp hil iba ilo ind jav jav_Java mad max_Latn min mlg pag pau sun tmw_Latn war zlm_Latn zsm_Latn * model: transformer * pre-processing: normalization + SentencePiece (spm32k,spm32k) * a sentence initial language token is required in the form of `>>id<<` (id = valid target language ID) * download original weights: [opus2m-2020-08-01.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-pqw/opus2m-2020-08-01.zip) * test set translations: [opus2m-2020-08-01.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-pqw/opus2m-2020-08-01.test.txt) * test set scores: [opus2m-2020-08-01.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-pqw/opus2m-2020-08-01.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | Tatoeba-test.eng-akl.eng.akl | 3.0 | 0.143 | | Tatoeba-test.eng-ceb.eng.ceb | 11.4 | 0.432 | | Tatoeba-test.eng-cha.eng.cha | 1.4 | 0.189 | | Tatoeba-test.eng-dtp.eng.dtp | 0.6 | 0.139 | | Tatoeba-test.eng-hil.eng.hil | 17.7 | 0.525 | | Tatoeba-test.eng-iba.eng.iba | 14.6 | 0.365 | | Tatoeba-test.eng-ilo.eng.ilo | 34.0 | 0.590 | | Tatoeba-test.eng-jav.eng.jav | 6.2 | 0.299 | | Tatoeba-test.eng-mad.eng.mad | 2.6 | 0.154 | | Tatoeba-test.eng-mlg.eng.mlg | 34.3 | 0.518 | | Tatoeba-test.eng-msa.eng.msa | 31.1 | 0.561 | | Tatoeba-test.eng.multi | 17.5 | 0.422 | | Tatoeba-test.eng-pag.eng.pag | 19.8 | 0.507 | | Tatoeba-test.eng-pau.eng.pau | 1.2 | 0.129 | | Tatoeba-test.eng-sun.eng.sun | 30.3 | 0.418 | | Tatoeba-test.eng-war.eng.war | 12.6 | 0.439 | ### System Info: - hf_name: eng-pqw - source_languages: eng - target_languages: pqw - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-pqw/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['en', 'pqw'] - src_constituents: {'eng'} - tgt_constituents: set() - src_multilingual: False - tgt_multilingual: True - prepro: normalization + SentencePiece (spm32k,spm32k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/eng-pqw/opus2m-2020-08-01.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/eng-pqw/opus2m-2020-08-01.test.txt - src_alpha3: eng - tgt_alpha3: pqw - short_pair: en-pqw - chrF2_score: 0.42200000000000004 - bleu: 17.5 - brevity_penalty: 1.0 - ref_len: 66758.0 - src_name: English - tgt_name: Western Malayo-Polynesian languages - train_date: 2020-08-01 - src_alpha2: en - tgt_alpha2: pqw - prefer_old: False - long_pair: eng-pqw - helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535 - transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b - port_machine: brutasse - port_time: 2020-08-21-14:41
Helsinki-NLP/opus-mt-fr-mfe
219ad5d7811a8ebdebc130810a0cffbeb307c172
2021-09-09T21:55:28.000Z
[ "pytorch", "marian", "text2text-generation", "fr", "mfe", "transformers", "translation", "license:apache-2.0", "autotrain_compatible" ]
translation
false
Helsinki-NLP
null
Helsinki-NLP/opus-mt-fr-mfe
2
null
transformers
23,110
--- tags: - translation license: apache-2.0 --- ### opus-mt-fr-mfe * source languages: fr * target languages: mfe * OPUS readme: [fr-mfe](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fr-mfe/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-20.zip](https://object.pouta.csc.fi/OPUS-MT-models/fr-mfe/opus-2020-01-20.zip) * test set translations: [opus-2020-01-20.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/fr-mfe/opus-2020-01-20.test.txt) * test set scores: [opus-2020-01-20.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/fr-mfe/opus-2020-01-20.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | JW300.fr.mfe | 26.1 | 0.451 |
Helsinki-NLP/opus-mt-guw-sv
c4c633c6753fa182a42f1751259e3be57fc320f4
2021-09-09T21:59:51.000Z
[ "pytorch", "marian", "text2text-generation", "guw", "sv", "transformers", "translation", "license:apache-2.0", "autotrain_compatible" ]
translation
false
Helsinki-NLP
null
Helsinki-NLP/opus-mt-guw-sv
2
null
transformers
23,111
--- tags: - translation license: apache-2.0 --- ### opus-mt-guw-sv * source languages: guw * target languages: sv * OPUS readme: [guw-sv](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/guw-sv/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/guw-sv/opus-2020-01-09.zip) * test set translations: [opus-2020-01-09.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/guw-sv/opus-2020-01-09.test.txt) * test set scores: [opus-2020-01-09.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/guw-sv/opus-2020-01-09.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | JW300.guw.sv | 31.2 | 0.498 |
Helsinki-NLP/opus-mt-it-lt
26a8c917ebd56b458913eab87144f7e1099b44c5
2020-08-21T14:42:46.000Z
[ "pytorch", "marian", "text2text-generation", "it", "lt", "transformers", "translation", "license:apache-2.0", "autotrain_compatible" ]
translation
false
Helsinki-NLP
null
Helsinki-NLP/opus-mt-it-lt
2
null
transformers
23,112
--- language: - it - lt tags: - translation license: apache-2.0 --- ### ita-lit * source group: Italian * target group: Lithuanian * OPUS readme: [ita-lit](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ita-lit/README.md) * model: transformer-align * source language(s): ita * target language(s): lit * model: transformer-align * pre-processing: normalization + SentencePiece (spm32k,spm32k) * download original weights: [opus-2020-06-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/ita-lit/opus-2020-06-17.zip) * test set translations: [opus-2020-06-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/ita-lit/opus-2020-06-17.test.txt) * test set scores: [opus-2020-06-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/ita-lit/opus-2020-06-17.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | Tatoeba-test.ita.lit | 38.1 | 0.652 | ### System Info: - hf_name: ita-lit - source_languages: ita - target_languages: lit - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ita-lit/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['it', 'lt'] - src_constituents: {'ita'} - tgt_constituents: {'lit'} - src_multilingual: False - tgt_multilingual: False - prepro: normalization + SentencePiece (spm32k,spm32k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/ita-lit/opus-2020-06-17.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/ita-lit/opus-2020-06-17.test.txt - src_alpha3: ita - tgt_alpha3: lit - short_pair: it-lt - chrF2_score: 0.652 - bleu: 38.1 - brevity_penalty: 0.9590000000000001 - ref_len: 1321.0 - src_name: Italian - tgt_name: Lithuanian - train_date: 2020-06-17 - src_alpha2: it - tgt_alpha2: lt - prefer_old: False - long_pair: ita-lit - helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535 - transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b - port_machine: brutasse - port_time: 2020-08-21-14:41
Helsinki-NLP/opus-mt-ja-he
2aa51fc3e068d90e5a719ae93aed18da46122e54
2020-08-21T14:42:47.000Z
[ "pytorch", "marian", "text2text-generation", "ja", "he", "transformers", "translation", "license:apache-2.0", "autotrain_compatible" ]
translation
false
Helsinki-NLP
null
Helsinki-NLP/opus-mt-ja-he
2
null
transformers
23,113
--- language: - ja - he tags: - translation license: apache-2.0 --- ### jpn-heb * source group: Japanese * target group: Hebrew * OPUS readme: [jpn-heb](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/jpn-heb/README.md) * model: transformer-align * source language(s): jpn_Hani jpn_Hira jpn_Kana * target language(s): heb * model: transformer-align * pre-processing: normalization + SentencePiece (spm32k,spm32k) * download original weights: [opus-2020-06-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/jpn-heb/opus-2020-06-17.zip) * test set translations: [opus-2020-06-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/jpn-heb/opus-2020-06-17.test.txt) * test set scores: [opus-2020-06-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/jpn-heb/opus-2020-06-17.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | Tatoeba-test.jpn.heb | 20.2 | 0.397 | ### System Info: - hf_name: jpn-heb - source_languages: jpn - target_languages: heb - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/jpn-heb/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['ja', 'he'] - src_constituents: {'jpn_Hang', 'jpn', 'jpn_Yiii', 'jpn_Kana', 'jpn_Hani', 'jpn_Bopo', 'jpn_Latn', 'jpn_Hira'} - tgt_constituents: {'heb'} - src_multilingual: False - tgt_multilingual: False - prepro: normalization + SentencePiece (spm32k,spm32k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/jpn-heb/opus-2020-06-17.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/jpn-heb/opus-2020-06-17.test.txt - src_alpha3: jpn - tgt_alpha3: heb - short_pair: ja-he - chrF2_score: 0.397 - bleu: 20.2 - brevity_penalty: 1.0 - ref_len: 1598.0 - src_name: Japanese - tgt_name: Hebrew - train_date: 2020-06-17 - src_alpha2: ja - tgt_alpha2: he - prefer_old: False - long_pair: jpn-heb - helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535 - transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b - port_machine: brutasse - port_time: 2020-08-21-14:41
Helsinki-NLP/opus-mt-nl-ca
33ee8c40483bc1b75318a7a1eab0d4f88ddc0f4b
2020-08-21T14:42:48.000Z
[ "pytorch", "marian", "text2text-generation", "nl", "ca", "transformers", "translation", "license:apache-2.0", "autotrain_compatible" ]
translation
false
Helsinki-NLP
null
Helsinki-NLP/opus-mt-nl-ca
2
null
transformers
23,114
--- language: - nl - ca tags: - translation license: apache-2.0 --- ### nld-cat * source group: Dutch * target group: Catalan * OPUS readme: [nld-cat](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/nld-cat/README.md) * model: transformer-align * source language(s): nld * target language(s): cat * model: transformer-align * pre-processing: normalization + SentencePiece (spm12k,spm12k) * download original weights: [opus-2020-06-16.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/nld-cat/opus-2020-06-16.zip) * test set translations: [opus-2020-06-16.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/nld-cat/opus-2020-06-16.test.txt) * test set scores: [opus-2020-06-16.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/nld-cat/opus-2020-06-16.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | Tatoeba-test.nld.cat | 42.1 | 0.624 | ### System Info: - hf_name: nld-cat - source_languages: nld - target_languages: cat - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/nld-cat/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['nl', 'ca'] - src_constituents: {'nld'} - tgt_constituents: {'cat'} - src_multilingual: False - tgt_multilingual: False - prepro: normalization + SentencePiece (spm12k,spm12k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/nld-cat/opus-2020-06-16.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/nld-cat/opus-2020-06-16.test.txt - src_alpha3: nld - tgt_alpha3: cat - short_pair: nl-ca - chrF2_score: 0.624 - bleu: 42.1 - brevity_penalty: 0.988 - ref_len: 3942.0 - src_name: Dutch - tgt_name: Catalan - train_date: 2020-06-16 - src_alpha2: nl - tgt_alpha2: ca - prefer_old: False - long_pair: nld-cat - helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535 - transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b - port_machine: brutasse - port_time: 2020-08-21-14:41
Helsinki-NLP/opus-mt-rn-fr
b64d323d3036d4191e74b90b0683ce2c67e96dde
2020-08-21T14:42:49.000Z
[ "pytorch", "marian", "text2text-generation", "rn", "fr", "transformers", "translation", "license:apache-2.0", "autotrain_compatible" ]
translation
false
Helsinki-NLP
null
Helsinki-NLP/opus-mt-rn-fr
2
null
transformers
23,115
--- language: - rn - fr tags: - translation license: apache-2.0 --- ### run-fra * source group: Rundi * target group: French * OPUS readme: [run-fra](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/run-fra/README.md) * model: transformer-align * source language(s): run * target language(s): fra * model: transformer-align * pre-processing: normalization + SentencePiece (spm4k,spm4k) * download original weights: [opus-2020-06-16.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/run-fra/opus-2020-06-16.zip) * test set translations: [opus-2020-06-16.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/run-fra/opus-2020-06-16.test.txt) * test set scores: [opus-2020-06-16.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/run-fra/opus-2020-06-16.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | Tatoeba-test.run.fra | 18.2 | 0.397 | ### System Info: - hf_name: run-fra - source_languages: run - target_languages: fra - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/run-fra/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['rn', 'fr'] - src_constituents: {'run'} - tgt_constituents: {'fra'} - src_multilingual: False - tgt_multilingual: False - prepro: normalization + SentencePiece (spm4k,spm4k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/run-fra/opus-2020-06-16.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/run-fra/opus-2020-06-16.test.txt - src_alpha3: run - tgt_alpha3: fra - short_pair: rn-fr - chrF2_score: 0.397 - bleu: 18.2 - brevity_penalty: 1.0 - ref_len: 7496.0 - src_name: Rundi - tgt_name: French - train_date: 2020-06-16 - src_alpha2: rn - tgt_alpha2: fr - prefer_old: False - long_pair: run-fra - helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535 - transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b - port_machine: brutasse - port_time: 2020-08-21-14:41
Helsinki-NLP/opus-mt-run-es
1915cb3c1f53b0fae04befffc6ea8b5b6c544622
2021-09-10T14:02:39.000Z
[ "pytorch", "marian", "text2text-generation", "run", "es", "transformers", "translation", "license:apache-2.0", "autotrain_compatible" ]
translation
false
Helsinki-NLP
null
Helsinki-NLP/opus-mt-run-es
2
null
transformers
23,116
--- tags: - translation license: apache-2.0 --- ### opus-mt-run-es * source languages: run * target languages: es * OPUS readme: [run-es](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/run-es/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/run-es/opus-2020-01-16.zip) * test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/run-es/opus-2020-01-16.test.txt) * test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/run-es/opus-2020-01-16.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | JW300.run.es | 26.9 | 0.452 |
Helsinki-NLP/opus-mt-sl-uk
de36b5886fd286ae9a56d4536c446d7bb73000e0
2020-08-21T14:42:49.000Z
[ "pytorch", "marian", "text2text-generation", "sl", "uk", "transformers", "translation", "license:apache-2.0", "autotrain_compatible" ]
translation
false
Helsinki-NLP
null
Helsinki-NLP/opus-mt-sl-uk
2
null
transformers
23,117
--- language: - sl - uk tags: - translation license: apache-2.0 --- ### slv-ukr * source group: Slovenian * target group: Ukrainian * OPUS readme: [slv-ukr](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/slv-ukr/README.md) * model: transformer-align * source language(s): slv * target language(s): ukr * model: transformer-align * pre-processing: normalization + SentencePiece (spm32k,spm32k) * download original weights: [opus-2020-06-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/slv-ukr/opus-2020-06-17.zip) * test set translations: [opus-2020-06-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/slv-ukr/opus-2020-06-17.test.txt) * test set scores: [opus-2020-06-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/slv-ukr/opus-2020-06-17.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | Tatoeba-test.slv.ukr | 10.6 | 0.236 | ### System Info: - hf_name: slv-ukr - source_languages: slv - target_languages: ukr - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/slv-ukr/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['sl', 'uk'] - src_constituents: {'slv'} - tgt_constituents: {'ukr'} - src_multilingual: False - tgt_multilingual: False - prepro: normalization + SentencePiece (spm32k,spm32k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/slv-ukr/opus-2020-06-17.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/slv-ukr/opus-2020-06-17.test.txt - src_alpha3: slv - tgt_alpha3: ukr - short_pair: sl-uk - chrF2_score: 0.23600000000000002 - bleu: 10.6 - brevity_penalty: 1.0 - ref_len: 3906.0 - src_name: Slovenian - tgt_name: Ukrainian - train_date: 2020-06-17 - src_alpha2: sl - tgt_alpha2: uk - prefer_old: False - long_pair: slv-ukr - helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535 - transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b - port_machine: brutasse - port_time: 2020-08-21-14:41
Helsinki-NLP/opus-mt-sv-eo
b6d5b9fdcaee1dd54570120e8f724faafa22aca6
2020-08-21T14:42:50.000Z
[ "pytorch", "marian", "text2text-generation", "sv", "eo", "transformers", "translation", "license:apache-2.0", "autotrain_compatible" ]
translation
false
Helsinki-NLP
null
Helsinki-NLP/opus-mt-sv-eo
2
null
transformers
23,118
--- language: - sv - eo tags: - translation license: apache-2.0 --- ### swe-epo * source group: Swedish * target group: Esperanto * OPUS readme: [swe-epo](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/swe-epo/README.md) * model: transformer-align * source language(s): swe * target language(s): epo * model: transformer-align * pre-processing: normalization + SentencePiece (spm4k,spm4k) * download original weights: [opus-2020-06-16.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/swe-epo/opus-2020-06-16.zip) * test set translations: [opus-2020-06-16.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/swe-epo/opus-2020-06-16.test.txt) * test set scores: [opus-2020-06-16.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/swe-epo/opus-2020-06-16.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | Tatoeba-test.swe.epo | 29.7 | 0.498 | ### System Info: - hf_name: swe-epo - source_languages: swe - target_languages: epo - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/swe-epo/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['sv', 'eo'] - src_constituents: {'swe'} - tgt_constituents: {'epo'} - src_multilingual: False - tgt_multilingual: False - prepro: normalization + SentencePiece (spm4k,spm4k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/swe-epo/opus-2020-06-16.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/swe-epo/opus-2020-06-16.test.txt - src_alpha3: swe - tgt_alpha3: epo - short_pair: sv-eo - chrF2_score: 0.498 - bleu: 29.7 - brevity_penalty: 0.958 - ref_len: 10987.0 - src_name: Swedish - tgt_name: Esperanto - train_date: 2020-06-16 - src_alpha2: sv - tgt_alpha2: eo - prefer_old: False - long_pair: swe-epo - helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535 - transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b - port_machine: brutasse - port_time: 2020-08-21-14:41
Helsinki-NLP/opus-mt-sv-he
804fe4f67cbb373619e4a9a053041e690dda272a
2021-09-10T14:06:50.000Z
[ "pytorch", "marian", "text2text-generation", "sv", "he", "transformers", "translation", "license:apache-2.0", "autotrain_compatible" ]
translation
false
Helsinki-NLP
null
Helsinki-NLP/opus-mt-sv-he
2
null
transformers
23,119
--- tags: - translation license: apache-2.0 --- ### opus-mt-sv-he * source languages: sv * target languages: he * OPUS readme: [sv-he](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sv-he/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-he/opus-2020-01-16.zip) * test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/sv-he/opus-2020-01-16.test.txt) * test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/sv-he/opus-2020-01-16.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | JW300.sv.he | 23.1 | 0.440 |
Helsinki-NLP/opus-mt-sv-lu
6ba0b38cc9116e3d5329b0210438bef031d6762b
2021-09-10T14:07:52.000Z
[ "pytorch", "marian", "text2text-generation", "sv", "lu", "transformers", "translation", "license:apache-2.0", "autotrain_compatible" ]
translation
false
Helsinki-NLP
null
Helsinki-NLP/opus-mt-sv-lu
2
null
transformers
23,120
--- tags: - translation license: apache-2.0 --- ### opus-mt-sv-lu * source languages: sv * target languages: lu * OPUS readme: [sv-lu](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sv-lu/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-lu/opus-2020-01-16.zip) * test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/sv-lu/opus-2020-01-16.test.txt) * test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/sv-lu/opus-2020-01-16.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | JW300.sv.lu | 24.8 | 0.484 |
Helsinki-NLP/opus-mt-sv-mfe
16986d34a7dbb789e3906d2b65c9891354c39d36
2021-09-10T14:08:10.000Z
[ "pytorch", "marian", "text2text-generation", "sv", "mfe", "transformers", "translation", "license:apache-2.0", "autotrain_compatible" ]
translation
false
Helsinki-NLP
null
Helsinki-NLP/opus-mt-sv-mfe
2
null
transformers
23,121
--- tags: - translation license: apache-2.0 --- ### opus-mt-sv-mfe * source languages: sv * target languages: mfe * OPUS readme: [sv-mfe](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sv-mfe/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-21.zip](https://object.pouta.csc.fi/OPUS-MT-models/sv-mfe/opus-2020-01-21.zip) * test set translations: [opus-2020-01-21.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/sv-mfe/opus-2020-01-21.test.txt) * test set scores: [opus-2020-01-21.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/sv-mfe/opus-2020-01-21.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | JW300.sv.mfe | 24.3 | 0.445 |
Helsinki-NLP/opus-mt-sv-run
a5706fa6ebb50fe7f6129e47c30031193841d861
2021-09-10T14:09:05.000Z
[ "pytorch", "marian", "text2text-generation", "sv", "run", "transformers", "translation", "license:apache-2.0", "autotrain_compatible" ]
translation
false
Helsinki-NLP
null
Helsinki-NLP/opus-mt-sv-run
2
null
transformers
23,122
--- tags: - translation license: apache-2.0 --- ### opus-mt-sv-run * source languages: sv * target languages: run * OPUS readme: [sv-run](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sv-run/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-run/opus-2020-01-16.zip) * test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/sv-run/opus-2020-01-16.test.txt) * test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/sv-run/opus-2020-01-16.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | JW300.sv.run | 24.4 | 0.502 |
Helsinki-NLP/opus-mt-sv-tn
9f2fc3a817597f6e20dc48dca76a4d07e22e3e7f
2021-09-10T14:10:00.000Z
[ "pytorch", "marian", "text2text-generation", "sv", "tn", "transformers", "translation", "license:apache-2.0", "autotrain_compatible" ]
translation
false
Helsinki-NLP
null
Helsinki-NLP/opus-mt-sv-tn
2
null
transformers
23,123
--- tags: - translation license: apache-2.0 --- ### opus-mt-sv-tn * source languages: sv * target languages: tn * OPUS readme: [sv-tn](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sv-tn/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-tn/opus-2020-01-16.zip) * test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/sv-tn/opus-2020-01-16.test.txt) * test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/sv-tn/opus-2020-01-16.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | JW300.sv.tn | 36.3 | 0.561 |
Helsinki-NLP/opus-mt-tr-eo
daf22c4ed4a156351412d919b9b9e163c286013d
2020-08-21T14:42:51.000Z
[ "pytorch", "marian", "text2text-generation", "tr", "eo", "transformers", "translation", "license:apache-2.0", "autotrain_compatible" ]
translation
false
Helsinki-NLP
null
Helsinki-NLP/opus-mt-tr-eo
2
null
transformers
23,124
--- language: - tr - eo tags: - translation license: apache-2.0 --- ### tur-epo * source group: Turkish * target group: Esperanto * OPUS readme: [tur-epo](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/tur-epo/README.md) * model: transformer-align * source language(s): tur * target language(s): epo * model: transformer-align * pre-processing: normalization + SentencePiece (spm4k,spm4k) * download original weights: [opus-2020-06-16.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/tur-epo/opus-2020-06-16.zip) * test set translations: [opus-2020-06-16.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/tur-epo/opus-2020-06-16.test.txt) * test set scores: [opus-2020-06-16.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/tur-epo/opus-2020-06-16.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | Tatoeba-test.tur.epo | 17.0 | 0.373 | ### System Info: - hf_name: tur-epo - source_languages: tur - target_languages: epo - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/tur-epo/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['tr', 'eo'] - src_constituents: {'tur'} - tgt_constituents: {'epo'} - src_multilingual: False - tgt_multilingual: False - prepro: normalization + SentencePiece (spm4k,spm4k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/tur-epo/opus-2020-06-16.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/tur-epo/opus-2020-06-16.test.txt - src_alpha3: tur - tgt_alpha3: epo - short_pair: tr-eo - chrF2_score: 0.373 - bleu: 17.0 - brevity_penalty: 0.8809999999999999 - ref_len: 33762.0 - src_name: Turkish - tgt_name: Esperanto - train_date: 2020-06-16 - src_alpha2: tr - tgt_alpha2: eo - prefer_old: False - long_pair: tur-epo - helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535 - transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b - port_machine: brutasse - port_time: 2020-08-21-14:41
Helsinki-NLP/opus-mt-zlw-fiu
420cb4a9da2a9c806ede890080697855915b94a7
2021-06-29T12:40:19.000Z
[ "pytorch", "marian", "text2text-generation", "dsb", "cs", "csb_Latn", "hsb", "pl", "zlw", "hu", "vro", "fi", "liv_Latn", "mdf", "krl", "fkv_Latn", "mhr", "et", "sma", "udm", "vep", "myv", "kpv", "se", "izh", "fiu", "transformers", "translation", "license:apache-2.0", "autotrain_compatible" ]
translation
false
Helsinki-NLP
null
Helsinki-NLP/opus-mt-zlw-fiu
2
null
transformers
23,125
--- language: - dsb - cs - csb_Latn - hsb - pl - zlw - hu - vro - fi - liv_Latn - mdf - krl - fkv_Latn - mhr - et - sma - udm - vep - myv - kpv - se - izh - fiu tags: - translation license: apache-2.0 --- ### zlw-fiu * source language name: West Slavic languages * target language name: Finno-Ugrian languages * OPUS readme: [README.md](https://object.pouta.csc.fi/Tatoeba-MT-models/zlw-fiu/README.md) * model: transformer * source language codes: dsb, cs, csb_Latn, hsb, pl, zlw * target language codes: hu, vro, fi, liv_Latn, mdf, krl, fkv_Latn, mhr, et, sma, udm, vep, myv, kpv, se, izh, fiu * dataset: opus * release date: 2021-02-18 * pre-processing: normalization + SentencePiece (spm32k,spm32k) * download original weights: [opus-2021-02-18.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/zlw-fiu/opus-2021-02-18.zip/zlw-fiu/opus-2021-02-18.zip) * a sentence-initial language token is required in the form of >>id<<(id = valid, usually three-letter target language ID) * Training data: * ces-fin: Tatoeba-train (1000000) * ces-hun: Tatoeba-train (1000000) * pol-est: Tatoeba-train (1000000) * pol-fin: Tatoeba-train (1000000) * pol-hun: Tatoeba-train (1000000) * Validation data: * ces-fin: Tatoeba-dev, 1000 * ces-hun: Tatoeba-dev, 1000 * est-pol: Tatoeba-dev, 1000 * fin-pol: Tatoeba-dev, 1000 * hun-pol: Tatoeba-dev, 1000 * mhr-pol: Tatoeba-dev, 461 * total-size-shuffled: 5426 * devset-selected: top 5000 lines of Tatoeba-dev.src.shuffled! * Test data: * newssyscomb2009.ces-hun: 502/9733 * newstest2009.ces-hun: 2525/54965 * Tatoeba-test.ces-fin: 88/408 * Tatoeba-test.ces-hun: 1911/10336 * Tatoeba-test.multi-multi: 4562/25497 * Tatoeba-test.pol-chm: 5/36 * Tatoeba-test.pol-est: 15/98 * Tatoeba-test.pol-fin: 609/3293 * Tatoeba-test.pol-hun: 1934/11285 * test set translations file: [test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zlw-fiu/opus-2021-02-18.zip/zlw-fiu/opus-2021-02-18.test.txt) * test set scores file: [eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zlw-fiu/opus-2021-02-18.zip/zlw-fiu/opus-2021-02-18.eval.txt) * BLEU-scores |Test set|score| |---|---| |Tatoeba-test.ces-fin|57.2| |Tatoeba-test.ces-hun|42.6| |Tatoeba-test.multi-multi|39.4| |Tatoeba-test.pol-hun|36.6| |Tatoeba-test.pol-fin|36.1| |Tatoeba-test.pol-est|20.9| |newssyscomb2009.ces-hun|13.9| |newstest2009.ces-hun|13.9| |Tatoeba-test.pol-chm|2.0| * chr-F-scores |Test set|score| |---|---| |Tatoeba-test.ces-fin|0.71| |Tatoeba-test.ces-hun|0.637| |Tatoeba-test.multi-multi|0.616| |Tatoeba-test.pol-hun|0.605| |Tatoeba-test.pol-fin|0.592| |newssyscomb2009.ces-hun|0.449| |newstest2009.ces-hun|0.443| |Tatoeba-test.pol-est|0.372| |Tatoeba-test.pol-chm|0.007| ### System Info: * hf_name: zlw-fiu * source_languages: dsb,cs,csb_Latn,hsb,pl,zlw * target_languages: hu,vro,fi,liv_Latn,mdf,krl,fkv_Latn,mhr,et,sma,udm,vep,myv,kpv,se,izh,fiu * opus_readme_url: https://object.pouta.csc.fi/Tatoeba-MT-models/zlw-fiu/opus-2021-02-18.zip/README.md * original_repo: Tatoeba-Challenge * tags: ['translation'] * languages: ['dsb', 'cs', 'csb_Latn', 'hsb', 'pl', 'zlw', 'hu', 'vro', 'fi', 'liv_Latn', 'mdf', 'krl', 'fkv_Latn', 'mhr', 'et', 'sma', 'udm', 'vep', 'myv', 'kpv', 'se', 'izh', 'fiu'] * src_constituents: ['dsb', 'ces', 'csb_Latn', 'hsb', 'pol'] * tgt_constituents: ['hun', 'vro', 'fin', 'liv_Latn', 'mdf', 'krl', 'fkv_Latn', 'mhr', 'est', 'sma', 'udm', 'vep', 'myv', 'kpv', 'sme', 'izh'] * src_multilingual: True * tgt_multilingual: True * helsinki_git_sha: a0966db6db0ae616a28471ff0faf461b36fec07d * transformers_git_sha: 3857f2b4e34912c942694489c2b667d9476e55f5 * port_machine: bungle * port_time: 2021-06-29-15:24
Helsinki-NLP/opus-mt-zne-fi
3d5c68815b2ff67b9681355bdf8f5c318cb863b2
2021-09-11T10:53:11.000Z
[ "pytorch", "marian", "text2text-generation", "zne", "fi", "transformers", "translation", "license:apache-2.0", "autotrain_compatible" ]
translation
false
Helsinki-NLP
null
Helsinki-NLP/opus-mt-zne-fi
2
null
transformers
23,126
--- tags: - translation license: apache-2.0 --- ### opus-mt-zne-fi * source languages: zne * target languages: fi * OPUS readme: [zne-fi](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/zne-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/zne-fi/opus-2020-01-16.zip) * test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/zne-fi/opus-2020-01-16.test.txt) * test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/zne-fi/opus-2020-01-16.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | JW300.zne.fi | 22.8 | 0.432 |
Helsinki-NLP/opus-tatoeba-af-ru
d6b635deae3dd0350db0b6c40d1921a2886a2de4
2021-02-12T13:01:01.000Z
[ "pytorch", "marian", "text2text-generation", "af", "ru", "transformers", "translation", "license:apache-2.0", "autotrain_compatible" ]
translation
false
Helsinki-NLP
null
Helsinki-NLP/opus-tatoeba-af-ru
2
null
transformers
23,127
--- language: - af - ru tags: - translation license: apache-2.0 --- ### af-ru * source group: Afrikaans * target group: Russian * OPUS readme: [afr-rus](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/afr-rus/README.md) * model: transformer-align * source language(s): afr * target language(s): rus * model: transformer-align * pre-processing: normalization + SentencePiece (spm32k,spm32k) * download original weights: [opus-2020-09-10.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/afr-rus/opus-2020-09-10.zip) * test set translations: [opus-2020-09-10.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/afr-rus/opus-2020-09-10.test.txt) * test set scores: [opus-2020-09-10.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/afr-rus/opus-2020-09-10.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | Tatoeba-test.afr.rus | 38.2 | 0.580 | ### System Info: - hf_name: af-ru - source_languages: afr - target_languages: rus - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/afr-rus/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['af', 'ru'] - src_constituents: ('Afrikaans', {'afr'}) - tgt_constituents: ('Russian', {'rus'}) - src_multilingual: False - tgt_multilingual: False - long_pair: afr-rus - prepro: normalization + SentencePiece (spm32k,spm32k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/afr-rus/opus-2020-09-10.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/afr-rus/opus-2020-09-10.test.txt - src_alpha3: afr - tgt_alpha3: rus - chrF2_score: 0.58 - bleu: 38.2 - brevity_penalty: 0.992 - ref_len: 1213 - src_name: Afrikaans - tgt_name: Russian - train_date: 2020-01-01 00:00:00 - src_alpha2: af - tgt_alpha2: ru - prefer_old: False - short_pair: af-ru - helsinki_git_sha: e8c308a96c1bd0b4ca6a8ce174783f93c3e30f25 - transformers_git_sha: 31245775e5772fbded1ac07ed89fbba3b5af0cb9 - port_machine: LM0-400-22516.local - port_time: 2021-02-12-14:52
HeyLucasLeao/byt5-base-pt-product-reviews
55dee99ce5fba70acafe892f53e6bf8a9df335a4
2021-08-25T17:02:21.000Z
[ "pytorch", "t5", "text2text-generation", "arxiv:2105.13626", "transformers", "autotrain_compatible" ]
text2text-generation
false
HeyLucasLeao
null
HeyLucasLeao/byt5-base-pt-product-reviews
2
1
transformers
23,128
Create README.md ## ByT5 Base Portuguese Product Reviews #### Model Description This is a finetuned version from ByT5 Base by Google for Sentimental Analysis from Product Reviews in Portuguese. ##### Paper: https://arxiv.org/abs/2105.13626 #### Training data It was trained from products reviews from a Americanas.com. You can found the data here: https://github.com/HeyLucasLeao/finetuning-byt5-model. #### Training Procedure It was finetuned using the Trainer Class available on the Hugging Face library. For evaluation it was used accuracy, precision, recall and f1 score. ##### Learning Rate: **1e-4** ##### Epochs: **1** ##### Colab for Finetuning: https://drive.google.com/file/d/17TcaN52moq7i7TE2EbcVbwQEQuAIQU63/view?usp=sharing ##### Colab for Metrics: https://colab.research.google.com/drive/1wbTDfOsE45UL8Q3ZD1_FTUmdVOKCcJFf#scrollTo=S4nuLkAFrlZ6 #### Score: ```python Training Set: 'accuracy': 0.9019706922688226, 'f1': 0.9305820610687022, 'precision': 0.9596555965559656, 'recall': 0.9032183375781431 Test Set: 'accuracy': 0.9019409684035312, 'f1': 0.9303758732034697, 'precision': 0.9006660401258529, 'recall': 0.9621126145787866 Validation Set: 'accuracy': 0.9044948078526491, 'f1': 0.9321924443009364, 'precision': 0.9024426549173129, 'recall': 0.9639705531617191 ``` #### Goals My true intention was totally educational, thus making available a this version of the model as a example for future proposes. How to use ``` python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM import torch if torch.cuda.is_available(): device = torch.device('cuda') else: device = torch.device('cpu') print(device) tokenizer = AutoTokenizer.from_pretrained("HeyLucasLeao/byt5-base-pt-product-reviews") model = AutoModelForSeq2SeqLM.from_pretrained("HeyLucasLeao/byt5-base-pt-product-reviews") model.to(device) def classificar_review(review): inputs = tokenizer([review], padding='max_length', truncation=True, max_length=512, return_tensors='pt') input_ids = inputs.input_ids.to(device) attention_mask = inputs.attention_mask.to(device) output = model.generate(input_ids, attention_mask=attention_mask) pred = np.argmax(output.cpu(), axis=1) dici = {0: 'Review Negativo', 1: 'Review Positivo'} return dici[pred.item()] classificar_review(review) ```
Holako/NER_model_holako
b358aa4f389cd368c3c312ccb06c181d8b90df7c
2022-02-23T09:07:06.000Z
[ "pytorch", "xlm-roberta", "token-classification", "transformers", "autotrain_compatible" ]
token-classification
false
Holako
null
Holako/NER_model_holako
2
null
transformers
23,129
#### How to use You can use this model with Transformers *pipeline* for NER. ```python from transformers import AutoTokenizer, AutoModelForTokenClassification from transformers import pipeline tokenizer = AutoTokenizer.from_pretrained("Holako/NER_model_holako") model = AutoModelForTokenClassification.from_pretrained("Holako/NER_model_holako") nlp = pipeline("ner", model=model, tokenizer=tokenizer) example = "اسمي احمد" ner_results = nlp(example) print(ner_results) ``` #### Limitations and bias This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. ======= #### Limitations and bias This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. ## Training data Language|Dataset -|- Arabic | [ANERcorp](https://camel.abudhabi.nyu.edu/anercorp/)
HungChau/bert_concept_extraction
df0c5b53cd8673d99a95a2cdf6b2b19fc0dfdcb1
2021-09-03T19:23:40.000Z
[ "pytorch", "distilbert", "token-classification", "transformers", "autotrain_compatible" ]
token-classification
false
HungChau
null
HungChau/bert_concept_extraction
2
null
transformers
23,130
Entry not found
HungChau/bert_concept_extraction_iir_from_kp20k_v1.1
baa092caeb8f51807aa45d681bf933331b08fe0a
2021-10-06T14:38:09.000Z
[ "pytorch", "distilbert", "token-classification", "transformers", "autotrain_compatible" ]
token-classification
false
HungChau
null
HungChau/bert_concept_extraction_iir_from_kp20k_v1.1
2
null
transformers
23,131
Entry not found
HungChau/bert_concept_extraction_kp20k_from_iir_v1.1
1ad0900d465ec5dcd808f816da5824868b8b4d22
2021-10-06T15:51:21.000Z
[ "pytorch", "distilbert", "token-classification", "transformers", "autotrain_compatible" ]
token-classification
false
HungChau
null
HungChau/bert_concept_extraction_kp20k_from_iir_v1.1
2
null
transformers
23,132
Entry not found
HungChau/distilbert-base-cased-concept-extraction-iir-v1.3
d92fb1748d98591764f6fb393b26ce2c1c74df94
2021-11-17T01:30:24.000Z
[ "pytorch", "distilbert", "token-classification", "transformers", "autotrain_compatible" ]
token-classification
false
HungChau
null
HungChau/distilbert-base-cased-concept-extraction-iir-v1.3
2
null
transformers
23,133
Entry not found
HungChau/distilbert-base-cased-concept-extraction-kp20k-v1.2-concept-extraction-wikipedia-v1.2
da0c9ebdfbef63d9cbb2dc9ece2380b0f5dbfbf9
2021-11-18T19:35:39.000Z
[ "pytorch", "distilbert", "token-classification", "transformers", "autotrain_compatible" ]
token-classification
false
HungChau
null
HungChau/distilbert-base-cased-concept-extraction-kp20k-v1.2-concept-extraction-wikipedia-v1.2
2
null
transformers
23,134
Entry not found
HungChau/distilbert-base-cased-concept-extraction-kp20k-v1.2
644bfaad5ba5509e4988c10462a10d359ca6f926
2021-11-16T09:53:17.000Z
[ "pytorch", "distilbert", "token-classification", "transformers", "autotrain_compatible" ]
token-classification
false
HungChau
null
HungChau/distilbert-base-cased-concept-extraction-kp20k-v1.2
2
null
transformers
23,135
Entry not found
HungChau/distilbert-base-uncased-concept-extraction-iir-v1.0-concept-extraction-kp20k-v1.0
87483822b51ac82aa8c147bea6c92176c19e945e
2021-09-25T01:32:32.000Z
[ "pytorch", "distilbert", "token-classification", "transformers", "autotrain_compatible" ]
token-classification
false
HungChau
null
HungChau/distilbert-base-uncased-concept-extraction-iir-v1.0-concept-extraction-kp20k-v1.0
2
null
transformers
23,136
Entry not found
HungChau/distilbert-base-uncased-concept-extraction-iir-v1.2-concept-extraction-kp20k-v1.2
232a1b6d684a28986fb9124eb6eb12522c742ab8
2021-11-18T12:33:39.000Z
[ "pytorch", "distilbert", "token-classification", "transformers", "autotrain_compatible" ]
token-classification
false
HungChau
null
HungChau/distilbert-base-uncased-concept-extraction-iir-v1.2-concept-extraction-kp20k-v1.2
2
null
transformers
23,137
Entry not found
HungChau/distilbert-base-uncased-concept-extraction-iir-v1.2
ad4169f713d8c55f8b1b82bee7986e9fb6ccddd8
2021-11-16T00:43:30.000Z
[ "pytorch", "distilbert", "token-classification", "transformers", "autotrain_compatible" ]
token-classification
false
HungChau
null
HungChau/distilbert-base-uncased-concept-extraction-iir-v1.2
2
null
transformers
23,138
Entry not found
HungChau/distilbert-base-uncased-concept-extraction-kp20k-v1.0-concept-extraction-iir-v1.0
3bf7ff93daf202942fdec598d76c4b1ba36dedc3
2021-09-24T15:32:30.000Z
[ "pytorch", "distilbert", "token-classification", "transformers", "autotrain_compatible" ]
token-classification
false
HungChau
null
HungChau/distilbert-base-uncased-concept-extraction-kp20k-v1.0-concept-extraction-iir-v1.0
2
null
transformers
23,139
Entry not found
HungChau/distilbert-base-uncased-concept-extraction-kp20k-v1.0-concept-extraction-wikipedia-v1.0
b70df2e5ea843bfd3539b6b8b02fff3eb7a274d8
2021-11-01T21:05:49.000Z
[ "pytorch", "distilbert", "token-classification", "transformers", "autotrain_compatible" ]
token-classification
false
HungChau
null
HungChau/distilbert-base-uncased-concept-extraction-kp20k-v1.0-concept-extraction-wikipedia-v1.0
2
null
transformers
23,140
Entry not found
HungChau/distilbert-base-uncased-concept-extraction-kp20k-v1.0
07244c172ce4c7c968c5b627199e51f314a7a4b3
2021-09-24T02:40:06.000Z
[ "pytorch", "distilbert", "token-classification", "transformers", "autotrain_compatible" ]
token-classification
false
HungChau
null
HungChau/distilbert-base-uncased-concept-extraction-kp20k-v1.0
2
null
transformers
23,141
Entry not found
HungChau/distilbert-base-uncased-concept-extraction-wikipedia-v1.0-concept-extraction-iir-v1.0
6c2ce6230ddc9ae97f2d9cea5e29b4bd3415407a
2021-11-02T23:34:28.000Z
[ "pytorch", "distilbert", "token-classification", "transformers", "autotrain_compatible" ]
token-classification
false
HungChau
null
HungChau/distilbert-base-uncased-concept-extraction-wikipedia-v1.0-concept-extraction-iir-v1.0
2
null
transformers
23,142
Entry not found
HungChau/distilbert-base-uncased-concept-extraction-wikipedia-v1.0-concept-extraction-iir-v1.3
36766502f8a059c9646b7b1eff57b9ad4d0225bd
2021-11-18T03:56:11.000Z
[ "pytorch", "distilbert", "token-classification", "transformers", "autotrain_compatible" ]
token-classification
false
HungChau
null
HungChau/distilbert-base-uncased-concept-extraction-wikipedia-v1.0-concept-extraction-iir-v1.3
2
null
transformers
23,143
Entry not found
HungChau/distilbert-base-uncased-concept-extraction-wikipedia-v1.0-concept-extraction-kp20k-v1.0
59c48bdb0c1739b12d3c71d0f85f7ea81888d8eb
2021-11-03T04:13:16.000Z
[ "pytorch", "distilbert", "token-classification", "transformers", "autotrain_compatible" ]
token-classification
false
HungChau
null
HungChau/distilbert-base-uncased-concept-extraction-wikipedia-v1.0-concept-extraction-kp20k-v1.0
2
null
transformers
23,144
Entry not found
HypNyx/DialoGPT-small-Thanos
14d0b4172cdf015bdf96d3aeda61b8e15dc9ff04
2021-09-02T15:18:59.000Z
[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]
conversational
false
HypNyx
null
HypNyx/DialoGPT-small-Thanos
2
null
transformers
23,145
--- tags: - conversational --- #Thanos DialoGPT Model
IMJONEZZ/SlovenBERTcina
141147e6796f8455ea9546b0df84fb7ed516c5fd
2021-07-29T05:26:25.000Z
[ "pytorch", "roberta", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
IMJONEZZ
null
IMJONEZZ/SlovenBERTcina
2
1
transformers
23,146
#Slovak RoBERTA Masked Language Model ###83Mil Parameters in small model Medium and Large models coming soon! RoBERTA pretrained tokenizer vocab and merges included. --- ##Training params: - **Dataset**: 8GB Slovak Monolingual dataset including ParaCrawl (monolingual), OSCAR, and several gigs of my own findings and cleaning. - **Preprocessing**: Tokenized with a pretrained ByteLevelBPETokenizer trained on the same dataset. Uncased, with s, pad, /s, unk, and mask special tokens. - **Evaluation results**: - Mnoho ľudí tu<mask> * žije. * žijú. * je. * trpí. - Ako sa<mask> * máte * máš * má * hovorí - Plážová sezóna pod Zoborom patrí medzi<mask> obdobia. * ročné * najkrajšie * najobľúbenejšie * najnáročnejšie - **Limitations**: The current model is fairly small, although it works very well. This model is meant to be finetuned on downstream tasks e.g. Part-of-Speech tagging, Question Answering, anything in GLUE or SUPERGLUE. - **Credit**: If you use this or any of my models in research or professional work, please credit me - Christopher Brousseau in said work.
Ife/ES-CA
7a49d9fa6cc5cb22fb4e0b709da20d856c90557b
2021-09-16T02:54:09.000Z
[ "pytorch", "marian", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
Ife
null
Ife/ES-CA
2
null
transformers
23,147
Entry not found
Ifenna/dbert-3epoch
5ea0711ed83eca3d06b6606e14e576fe5951fece
2021-07-24T23:48:06.000Z
[ "pytorch", "distilbert", "question-answering", "transformers", "autotrain_compatible" ]
question-answering
false
Ifenna
null
Ifenna/dbert-3epoch
2
null
transformers
23,148
A distilbert model fine-tuned for question answering.
Ilyabarigou/Genesis-harrybotter
b6805e16df88ec4e417c21ffc0819ed73afd782a
2021-09-02T16:37:18.000Z
[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]
conversational
false
Ilyabarigou
null
Ilyabarigou/Genesis-harrybotter
2
null
transformers
23,149
--- tags: - conversational --- # Harry Botter Model
InfoCoV/Cro-CoV-BERTic
9e9fe8f5e4158beb723b3ddfc243129bd4e55aba
2022-02-11T14:20:05.000Z
[ "pytorch", "tensorboard", "electra", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
InfoCoV
null
InfoCoV/Cro-CoV-BERTic
2
null
transformers
23,150
Entry not found
Iskaj/300m_cv8.0_nl_base
0ecba7407414dd88fd41c7ef947cbb2ff9c09579
2022-02-04T11:38:50.000Z
[ "pytorch", "wav2vec2", "automatic-speech-recognition", "transformers" ]
automatic-speech-recognition
false
Iskaj
null
Iskaj/300m_cv8.0_nl_base
2
null
transformers
23,151
Entry not found
Iskaj/newnew
a4e2a5607f3a15fa9e0549e9d3a6a137e8d4bd25
2022-02-02T20:02:08.000Z
[ "pytorch", "wav2vec2", "automatic-speech-recognition", "nl", "dataset:common_voice", "transformers", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "license:apache-2.0", "model-index" ]
automatic-speech-recognition
false
Iskaj
null
Iskaj/newnew
2
null
transformers
23,152
--- language: - nl license: apache-2.0 tags: - automatic-speech-recognition - mozilla-foundation/common_voice_8_0 - generated_from_trainer datasets: - common_voice model-index: - name: newnew results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # newnew This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - NL dataset. It achieves the following results on the evaluation set: - Loss: 11.4375 - Wer: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 4000 - num_epochs: 50.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.2+cu102 - Datasets 1.18.3.dev0 - Tokenizers 0.11.0
Iskaj/w2v-xlsr-dutch-lm-added
ad70d5cf7b6cfecf7d6c3b126ebb934d3e01b9c3
2022-01-27T15:58:50.000Z
[ "pytorch", "jax", "wav2vec2", "automatic-speech-recognition", "transformers" ]
automatic-speech-recognition
false
Iskaj
null
Iskaj/w2v-xlsr-dutch-lm-added
2
null
transformers
23,153
Copy of "facebook/wav2vec2-large-xlsr-53-dutch"
Iskaj/xlsr_300m_CV_8.0_50_EP_new_params_nl
6b324dc6c213528ff5f14a71976adf2c1529fa01
2022-03-23T18:34:30.000Z
[ "pytorch", "wav2vec2", "automatic-speech-recognition", "nl", "dataset:mozilla-foundation/common_voice_8_0", "transformers", "hf-asr-leaderboard", "model_for_talk", "mozilla-foundation/common_voice_8_0", "robust-speech-event", "license:apache-2.0", "model-index" ]
automatic-speech-recognition
false
Iskaj
null
Iskaj/xlsr_300m_CV_8.0_50_EP_new_params_nl
2
null
transformers
23,154
--- language: - nl license: apache-2.0 tags: - automatic-speech-recognition - hf-asr-leaderboard - model_for_talk - mozilla-foundation/common_voice_8_0 - nl - robust-speech-event datasets: - mozilla-foundation/common_voice_8_0 model-index: - name: XLS-R-300M - Dutch results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 8 NL type: mozilla-foundation/common_voice_8_0 args: nl metrics: - name: Test WER type: wer value: 35.44 - name: Test CER type: cer value: 19.57 - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Robust Speech Event - Dev Data type: speech-recognition-community-v2/dev_data args: nl metrics: - name: Test WER type: wer value: 37.17 - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Robust Speech Event - Test Data type: speech-recognition-community-v2/eval_data args: nl metrics: - name: Test WER type: wer value: 38.73 --- # xlsr_300m_CV_8.0_50_EP_new_params_nl
Istiaque190515/harry_potter
4dd47fe77b010c7fb9f218baf3ed612d45fd2f91
2021-09-18T15:56:56.000Z
[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]
conversational
false
Istiaque190515
null
Istiaque190515/harry_potter
2
null
transformers
23,155
--- tags: - conversational --- #harry_potter
Itcast/cnc_output
ae06d81c9eec3b21305b2e74743515ee5c0fd14f
2020-01-01T15:20:04.000Z
[ "pytorch", "transformers" ]
null
false
Itcast
null
Itcast/cnc_output
2
null
transformers
23,156
Entry not found
Jacobo/axiothea
7049fecffde2af7481fcb78cc22149be9f0be59d
2021-11-15T20:07:05.000Z
[ "pytorch", "roberta", "fill-mask", "grc", "transformers", "generated_from_trainer", "model-index", "autotrain_compatible" ]
fill-mask
false
Jacobo
null
Jacobo/axiothea
2
null
transformers
23,157
--- tags: - generated_from_trainer language: - grc model-index: - name: dioBERTo results: [] widget: - text: "Πλάτων ὁ Περικτιόνης <mask> γένος ἀνέφερεν εἰς Σόλωνα." - text: "ὁ Κριτίας ἀπέβλεψε <mask> τὴν θύραν." - text: "Ὦ φίλε Κλεινία, καλῶς μὲν <mask>." --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # axiothea This is an experimental roberta model trained with an ancient Greek corpus of about 900 MB, which was scrapped from the web and post-processed. Duplicate texts and editorial punctuation were removed. The training dataset will be soon available in the Huggingface datasets hub. Training a model of ancient Greek is challenging given that it is a low resource language from which 50% of the register has only survived in fragmentary texts. The model is provided by the Diogenet project at the University of California, San Diego. It achieves the following results on the evaluation set: - Loss: 3.3351 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-------:|:---------------:| | 4.7013 | 1.0 | 341422 | 4.8813 | | 4.2866 | 2.0 | 682844 | 4.4422 | | 4.0496 | 3.0 | 1024266 | 4.2132 | | 3.8503 | 4.0 | 1365688 | 4.0246 | | 3.6917 | 5.0 | 1707110 | 3.8756 | | 3.4917 | 6.0 | 2048532 | 3.7381 | | 3.3907 | 7.0 | 2389954 | 3.6107 | | 3.2876 | 8.0 | 2731376 | 3.5044 | | 3.1994 | 9.0 | 3072798 | 3.3980 | | 3.0806 | 10.0 | 3414220 | 3.3095 | ### Framework versions - Transformers 4.13.0.dev0 - Pytorch 1.10.0+cu102 - Datasets 1.14.0 - Tokenizers 0.10.3
Jainil30/wav2vec2-base-csa-10-rev3
5e12be3f25d6a2fcff2683e04cbe32727195e492
2022-01-12T14:55:33.000Z
[ "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
automatic-speech-recognition
false
Jainil30
null
Jainil30/wav2vec2-base-csa-10-rev3
2
null
transformers
23,158
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-base-csa-10-rev3 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-base-csa-10-rev3 This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.5869 - Wer: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:---:| | 18.7934 | 25.0 | 200 | 3.5869 | 1.0 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.13.3 - Tokenizers 0.10.3
Jeevesh8/DA-LF
9cc47271d709ac03588ab2eb66a8743cf4b1be64
2021-11-12T10:02:01.000Z
[ "pytorch", "longformer", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
Jeevesh8
null
Jeevesh8/DA-LF
2
null
transformers
23,159
Entry not found
Jeevesh8/sMLM-256-LF
7d56118efe8fe3d676f5ccfffe6d4e3ec33c05af
2021-11-12T09:57:06.000Z
[ "pytorch", "longformer", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
Jeevesh8
null
Jeevesh8/sMLM-256-LF
2
null
transformers
23,160
Entry not found
Jeevesh8/sMLM-LF
59439ddcbaeb99504e04685a08dce4d4d19f25fc
2021-11-12T09:02:58.000Z
[ "pytorch", "longformer", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
Jeevesh8
null
Jeevesh8/sMLM-LF
2
null
transformers
23,161
Entry not found
Jeska/BertjeWDialDataALLQonly08
c72a016efecb2f8a66d7b7eca20a72eca610bfbe
2021-12-11T22:48:56.000Z
[ "pytorch", "tensorboard", "bert", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
Jeska
null
Jeska/BertjeWDialDataALLQonly08
2
null
transformers
23,162
Entry not found
Jeska/BertjeWDialDataALLQonly128
7bd0ab5415020eb7408103010eeae50b017aa8ae
2021-12-07T18:57:42.000Z
[ "pytorch", "tensorboard", "bert", "fill-mask", "transformers", "generated_from_trainer", "model-index", "autotrain_compatible" ]
fill-mask
false
Jeska
null
Jeska/BertjeWDialDataALLQonly128
2
null
transformers
23,163
--- tags: - generated_from_trainer model-index: - name: BertjeWDialDataALLQonly128 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # BertjeWDialDataALLQonly128 This model is a fine-tuned version of [GroNLP/bert-base-dutch-cased](https://huggingface.co/GroNLP/bert-base-dutch-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.0364 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 12.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 2.2326 | 1.0 | 871 | 2.1509 | | 2.1375 | 2.0 | 1742 | 2.1089 | | 2.0442 | 3.0 | 2613 | 2.0655 | | 2.0116 | 4.0 | 3484 | 2.0433 | | 1.9346 | 5.0 | 4355 | 2.0134 | | 1.9056 | 6.0 | 5226 | 1.9956 | | 1.8295 | 7.0 | 6097 | 2.0287 | | 1.8204 | 8.0 | 6968 | 2.0173 | | 1.7928 | 9.0 | 7839 | 2.0251 | | 1.7357 | 10.0 | 8710 | 2.0148 | | 1.7318 | 11.0 | 9581 | 1.9274 | | 1.7311 | 12.0 | 10452 | 1.9314 | ### Framework versions - Transformers 4.13.0.dev0 - Pytorch 1.10.0 - Datasets 1.16.1 - Tokenizers 0.10.3
Jeska/BertjeWDialDataQA20k
10c69c7ab9c4eff8366675cbd2d7f4fe45803478
2021-11-29T15:35:11.000Z
[ "pytorch", "tensorboard", "bert", "fill-mask", "transformers", "generated_from_trainer", "model-index", "autotrain_compatible" ]
fill-mask
false
Jeska
null
Jeska/BertjeWDialDataQA20k
2
null
transformers
23,164
--- tags: - generated_from_trainer model-index: - name: BertjeWDialDataQA20k results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # BertjeWDialDataQA20k This model is a fine-tuned version of [GroNLP/bert-base-dutch-cased](https://huggingface.co/GroNLP/bert-base-dutch-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.9208 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.1713 | 1.0 | 1542 | 2.0098 | | 2.0736 | 2.0 | 3084 | 1.9853 | | 2.0543 | 3.0 | 4626 | 2.0134 | ### Framework versions - Transformers 4.13.0.dev0 - Pytorch 1.10.0 - Datasets 1.16.1 - Tokenizers 0.10.3
LysandreJik/dummy-model
288dc58c209692e41b0c177a0bed30cfd9c25f2c
2021-06-30T17:38:18.000Z
[ "pytorch", "camembert", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
LysandreJik
null
LysandreJik/dummy-model
2
null
transformers
23,165
Entry not found
LysandreJik/local_dir_1
d137267fc732600d7dd89603145bad8dc4b7a277
2021-09-06T19:43:31.000Z
[ "pytorch", "bert", "feature-extraction", "transformers" ]
feature-extraction
false
LysandreJik
null
LysandreJik/local_dir_1
2
null
transformers
23,166
Entry not found
Jipski/gpt2-Flo-BasBoettcher-Chefkoch
9758144b2ab061bd553e13f010688d1f9c34423b
2021-12-06T21:45:45.000Z
[ "pytorch", "gpt2", "text-generation", "transformers" ]
text-generation
false
Jipski
null
Jipski/gpt2-Flo-BasBoettcher-Chefkoch
2
null
transformers
23,167
Entry not found
Jipski/gpt2-FloSolo
14f0da89815f4d29bee32fdb6f464c58abd15b2e
2021-12-06T21:39:08.000Z
[ "pytorch", "gpt2", "text-generation", "transformers" ]
text-generation
false
Jipski
null
Jipski/gpt2-FloSolo
2
null
transformers
23,168
Entry not found
JonatanGk/roberta-base-bne-finetuned-sqac
4e9333ce737c6e93db0f1db7e061fee91cd7c7ca
2021-10-21T21:06:47.000Z
[ "pytorch", "tensorboard", "roberta", "question-answering", "dataset:sqac", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]
question-answering
false
JonatanGk
null
JonatanGk/roberta-base-bne-finetuned-sqac
2
1
transformers
23,169
--- license: apache-2.0 tags: - generated_from_trainer datasets: - sqac model-index: - name: roberta-base-bne-finetuned-sqac results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # roberta-base-bne-finetuned-sqac This model is a fine-tuned version of [PlanTL-GOB-ES/roberta-base-bne](https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne) on the sqac dataset. It achieves the following results on the evaluation set: - Loss: 1.2066 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### 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: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.9924 | 1.0 | 1196 | 0.8670 | | 0.474 | 2.0 | 2392 | 0.8923 | | 0.1637 | 3.0 | 3588 | 1.2066 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.9.0+cu111 - Datasets 1.14.0 - Tokenizers 0.10.3
JonathanSum/dummy-model
2952560bb043ffc13418c1d8a823a207deeaecf1
2021-07-31T17:14:35.000Z
[ "pytorch", "camembert", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
JonathanSum
null
JonathanSum/dummy-model
2
null
transformers
23,170
Entry not found
Jung/t5-large-finetuned
b54775482e553ef1593c3d6b2d79f1b9a4e3bbe9
2021-06-23T02:35:40.000Z
[ "pytorch", "jax", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
Jung
null
Jung/t5-large-finetuned
2
null
transformers
23,171
Entry not found
Junmai/klue-roberta-large-copa-finetuned-v1
08836da8cd57b73066034775b65630261de1992a
2021-12-08T06:02:06.000Z
[ "pytorch", "roberta", "multiple-choice", "transformers" ]
multiple-choice
false
Junmai
null
Junmai/klue-roberta-large-copa-finetuned-v1
2
null
transformers
23,172
Entry not found
Junmai/pretrained-klue-roberta-v1
23a7a88a644b5ba8247a5c7efdfbe260cf148405
2021-12-08T04:49:00.000Z
[ "pytorch", "roberta", "multiple-choice", "transformers" ]
multiple-choice
false
Junmai
null
Junmai/pretrained-klue-roberta-v1
2
null
transformers
23,173
Entry not found
Kaledmgo/DialoGPT-small-donajulia
05cf431a81c5e359a804b942d7ef3c97154579db
2021-09-01T02:05:03.000Z
[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]
conversational
false
Kaledmgo
null
Kaledmgo/DialoGPT-small-donajulia
2
null
transformers
23,174
--- tags: - conversational --- # Dona Julia DialoGPT Model
Kalindu/SinBerto
c892311d6c8a1ef7d9c81e871a62e9e064fe1224
2021-06-17T16:37:19.000Z
[ "pytorch", "roberta", "fill-mask", "si", "arxiv:1907.11692", "transformers", "SinBERTo", "Sinhala", "autotrain_compatible" ]
fill-mask
false
Kalindu
null
Kalindu/SinBerto
2
null
transformers
23,175
--- language: si tags: - SinBERTo - Sinhala - roberta --- ### Overview SinBerto is a small language model trained on a small news corpus. SinBerto is trained on Sinhala Language which is a low resource language compared to other languages. ### Model Specifications. model : [Roberta](https://arxiv.org/abs/1907.11692) vocab_size=52_000, max_position_embeddings=514, num_attention_heads=12, num_hidden_layers=6, type_vocab_size=1 ### How to use from the Transformers Library from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Kalindu/SinBerto") model = AutoModelForMaskedLM.from_pretrained("Kalindu/SinBerto") ### OR Clone the model repo git lfs install git clone https://huggingface.co/Kalindu/SinBerto
KekLord/DialoGPT-small-rick3
8222541f0bc65bf5c08cc69737d19a819dc2373e
2021-11-02T06:00:30.000Z
[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]
conversational
false
KekLord
null
KekLord/DialoGPT-small-rick3
2
null
transformers
23,176
--- tags: - conversational --- # Rick3 DialoGPT Model
KheireddineDaouadi/SIMCSEARA
add7207d6e857e40a94e40aa40ad0b4fd19d0f43
2022-02-14T22:38:53.000Z
[ "pytorch", "bert", "feature-extraction", "transformers" ]
feature-extraction
false
KheireddineDaouadi
null
KheireddineDaouadi/SIMCSEARA
2
null
transformers
23,177
Entry not found
Kshaunish/DialoGPT-small-rick
0fe7c5853833b89bbd67501efdcf0890b9f3c9f1
2021-08-31T10:40:50.000Z
[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]
conversational
false
Kshaunish
null
Kshaunish/DialoGPT-small-rick
2
null
transformers
23,178
--- tags: - conversational --- # Rick Sanchez DialoGPT Model
Kush/DialoGPT-small-harrypotter
074ad2c75ed96f7d53af5155f415eee44b2cccb7
2021-10-17T12:56:00.000Z
[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]
conversational
false
Kush
null
Kush/DialoGPT-small-harrypotter
2
null
transformers
23,179
--- tags: - conversational --- # Harry Potter DialoGPT Model
Kyuyoung11/haremotions-v4
f6655579c93e075fa666c31efd6dbe75a56691e5
2021-08-15T06:05:41.000Z
[ "pytorch", "electra", "transformers" ]
null
false
Kyuyoung11
null
Kyuyoung11/haremotions-v4
2
null
transformers
23,180
Entry not found
Lara/opus-mt-en-de-finetuned-en-to-de
cc0193314bd7d174956bb177820de0a418a7f7d6
2021-10-31T21:33:03.000Z
[ "pytorch", "tensorboard", "marian", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
Lara
null
Lara/opus-mt-en-de-finetuned-en-to-de
2
null
transformers
23,181
Entry not found
LegolasTheElf/Wav2Vec2_xls_r_300m_hi_cv7_part2
1bd6f900ef4a9ff43258302b78b1c5af480898a2
2022-02-08T07:22:10.000Z
[ "pytorch", "wav2vec2", "automatic-speech-recognition", "transformers" ]
automatic-speech-recognition
false
LegolasTheElf
null
LegolasTheElf/Wav2Vec2_xls_r_300m_hi_cv7_part2
2
null
transformers
23,182
Entry not found
LegolasTheElf/Wav2Vec2_xls_r_openslr_Hi_V2
3f3402b2d657a5db1864d69451679faa63413fdb
2022-02-04T07:53:30.000Z
[ "pytorch", "wav2vec2", "automatic-speech-recognition", "hi", "transformers", "Harveenchadha/indic-voice", "generated_from_trainer", "license:apache-2.0", "model-index" ]
automatic-speech-recognition
false
LegolasTheElf
null
LegolasTheElf/Wav2Vec2_xls_r_openslr_Hi_V2
2
null
transformers
23,183
--- license: apache-2.0 language: - hi tags: - automatic-speech-recognition - Harveenchadha/indic-voice - generated_from_trainer model-index: - name: Wav2Vec2_xls_r_openslr_Hi_V2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Wav2Vec2_xls_r_openslr_Hi_V2 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the [Harveenchadha/indic-voice](https://huggingface.co/datasets/Harveenchadha/indic-voice) dataset. It achieves the following results on the evaluation set: - Loss: 0.3184 - Wer: 0.3104 - Cer: 0.0958 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 200 - num_epochs: 12 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Cer | Validation Loss | Wer | |:-------------:|:-----:|:----:|:------:|:---------------:|:------:| | 7.1097 | 0.48 | 300 | 0.9965 | 3.3989 | 1.0 | | 3.0235 | 0.96 | 600 | 0.3163 | 1.3183 | 0.7977 | | 1.1419 | 1.44 | 900 | 0.1913 | 0.6416 | 0.5543 | | 0.8242 | 1.92 | 1200 | 0.1608 | 0.5063 | 0.4804 | | 0.6876 | 2.56 | 1600 | 0.1387 | 0.4401 | 0.4280 | | 0.5868 | 3.21 | 2000 | 0.1249 | 0.3940 | 0.3907 | | 0.5285 | 3.85 | 2400 | 0.1200 | 0.3661 | 0.3763 | | 0.5 | 4.49 | 2800 | 0.3528 | 0.3610 | 0.1136 | | 0.4538 | 5.13 | 3200 | 0.3403 | 0.3485 | 0.1086 | | 0.4165 | 5.77 | 3600 | 0.3335 | 0.3439 | 0.1062 | | 0.3989 | 6.41 | 4000 | 0.3264 | 0.3340 | 0.1036 | | 0.3679 | 7.05 | 4400 | 0.3256 | 0.3287 | 0.1013 | | 0.3517 | 7.69 | 4800 | 0.3212 | 0.3223 | 0.1002 | | 0.3357 | 8.33 | 5200 | 0.3173 | 0.3196 | 0.0986 | | 0.3225 | 8.97 | 5600 | 0.3142 | 0.3177 | 0.0985 | | 0.3057 | 9.62 | 6000 | 0.3199 | 0.3156 | 0.0975 | | 0.2972 | 10.26 | 6400 | 0.3139 | 0.3128 | 0.0967 | | 0.2881 | 10.9 | 6800 | 0.3184 | 0.3107 | 0.0957 | | 0.2791 | 11.54 | 7200 | 0.3184 | 0.3104 | 0.0958 | ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.2+cu102 - Datasets 1.18.2.dev0 - Tokenizers 0.11.0
Leisa/marian-finetuned-kde4-en-to-fr-accelerate
21fddb4fcac181c27db0876a21091fa65e7ab307
2021-11-21T07:07:31.000Z
[ "pytorch", "marian", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
Leisa
null
Leisa/marian-finetuned-kde4-en-to-fr-accelerate
2
null
transformers
23,184
Entry not found
Leisa/marian-finetuned-kde4-en-to-fr
6dcac6c22fdb5f58747f4d3a3b74d8b8358126bb
2021-11-21T05:25:45.000Z
[ "pytorch", "marian", "text2text-generation", "dataset:kde4", "transformers", "translation", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]
translation
false
Leisa
null
Leisa/marian-finetuned-kde4-en-to-fr
2
null
transformers
23,185
--- license: apache-2.0 tags: - translation - generated_from_trainer datasets: - kde4 metrics: - bleu model-index: - name: marian-finetuned-kde4-en-to-fr results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: kde4 type: kde4 args: en-fr metrics: - name: Bleu type: bleu value: 52.94538305859332 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # marian-finetuned-kde4-en-to-fr This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) on the kde4 dataset. It achieves the following results on the evaluation set: - Loss: 0.8558 - Bleu: 52.9454 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.12.5 - Pytorch 1.10.0 - Datasets 1.15.1 - Tokenizers 0.10.3
LeoCordoba/beto2beto
b88c1ce17ce488d2780d8a3366039156ec7d97ea
2021-09-08T16:31:21.000Z
[ "pytorch", "encoder-decoder", "text2text-generation", "es", "dataset:LeoCordoba/CC-NEWS-ES", "transformers", "text-generation", "spanish", "beto", "license:apache-2.0", "model-index", "autotrain_compatible" ]
text-generation
false
LeoCordoba
null
LeoCordoba/beto2beto
2
null
transformers
23,186
--- language: es tags: - text-generation - spanish - encoder-decoder - beto license: apache-2.0 datasets: - LeoCordoba/CC-NEWS-ES model-index: - name: beto2beto --- ## beto2beto Usage example here: https://colab.research.google.com/drive/18a2ZfF1e_Kyyydlv8INQIkJbv294xcAm?usp=sharing Entrenado por 3 epochs sobre CC-NEWS-ES (2019), aproximadamente 68.000 steps. Encoder max length: 40•Decoder max length: 128 ## Hyperparameters ## Usage ## Results | key | value | | --- | ----- | | test_loss | 2.65148806571960452 |
Leostronkest/DialoGPT-small-michael
f81beb6097039ac3a925da96c436ccff064e42c5
2022-02-14T23:39:02.000Z
[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]
conversational
false
Leostronkest
null
Leostronkest/DialoGPT-small-michael
2
null
transformers
23,187
--- tags: - conversational --- # Michael DialoGPT Model
Li/roberta-base-squad2
58f0e7bb52d2163ed10244f131d5d0bf486e42a4
2021-09-26T04:58:13.000Z
[ "pytorch", "roberta", "question-answering", "transformers", "autotrain_compatible" ]
question-answering
false
Li
null
Li/roberta-base-squad2
2
null
transformers
23,188
[roberta-base](https://huggingface.co/roberta-base) fine-tuned on the [SQuAD2](https://rajpurkar.github.io/SQuAD-explorer) dataset for 2 epochs. The fine-tuning process was performed on a single NVIDIA Tesla T4 GPU (15GB). The hyperparameters are: ``` max_seq_length=512 per_device_train_batch_size=8 gradient_accumulation_steps=4 total train batch size (w. parallel, distributed & accumulation) = 32 learning_rate=3e-5 ``` ## Evaluation results ``` "eval_exact": 80.33352985766024, "eval_f1": 83.38322909593009, "eval_HasAns_exact": 77.81713900134953, "eval_HasAns_f1": 83.925283241562, "eval_HasAns_total": 5928, "eval_NoAns_exact": 82.84272497897393, "eval_NoAns_f1": 82.84272497897393, "eval_NoAns_total": 5945, "eval_best_exact": 80.33352985766024, "eval_best_exact_thresh": 0.0, "eval_best_f1": 83.38322909593005, "eval_best_f1_thresh": 0.0, "eval_samples": 11955, "eval_total": 11873, ``` ## More information Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable. SQuAD2.0 combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions written adversarially by crowdworkers to look similar to answerable ones. To do well on SQuAD2.0, systems must not only answer questions when possible, but also determine when no answer is supported by the paragraph and abstain from answering. (https://rajpurkar.github.io/SQuAD-explorer/)
LiqiangXiao/ConvSearch_QU
a1eb64901d799990a62a27019b839a2408e3a0dd
2022-01-20T06:32:35.000Z
[ "pytorch", "bart", "text2text-generation", "arxiv:2109.05460", "transformers", "autotrain_compatible" ]
text2text-generation
false
LiqiangXiao
null
LiqiangXiao/ConvSearch_QU
2
4
transformers
23,189
## End-to-end Conversational search model A end-to-end system of conversational search system for online shopping. It was introduced in [this paper](https://arxiv.org/abs/2109.05460) published on conference EMNLP. ## Model description ConvSearch is an end-to-end conversational search system that deeply combines the dialog and search system to improve the search performance. In particular, the Product Search module leverages both structured product attributes and unstructured product text (e.g. profile), where the product text may contain phrases matching with utterances when schema is incomplete or when a product attribute value is missing. Putting together, our system has the advantage of both reduced error accumulation along individual modules, and enhanced robustness against product schema/knowledge gaps. ## Intended uses & limitations You can use the raw model to understand the dialog between consumer and server. The concatenated dialogs can be parsed into intents (e.g. inform, request, buy, et al.) and attributes of products. You can also fine-tune this model on similar down-stream tasks, such as a dialog system for shopping in your scenario or customer service system. Since our model is seq-to-seq, any dialog system that can be reformed to seq-to-seq can be implemented based on this model. ## How to use You can use this model directly with: from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("LiqiangXiao/ConvSearch_QU") model = AutoModelForSeq2SeqLM.from_pretrained("LiqiangXiao/ConvSearch_QU") ## Training data ConvSearch was pretrained on a dialog corpus with 49,999 dialogs/942,766 turns.
LucasS/bertLargeABSA
794c6fcb52378513ab5825acf62522cf2a257fb8
2021-09-02T19:53:31.000Z
[ "pytorch", "bert", "question-answering", "transformers", "autotrain_compatible" ]
question-answering
false
LucasS
null
LucasS/bertLargeABSA
2
null
transformers
23,190
Entry not found
Lurka/DialoGPT-medium-kon
31a5c99e2627b77862879eefd09f952a07777d45
2021-10-07T14:27:01.000Z
[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]
conversational
false
Lurka
null
Lurka/DialoGPT-medium-kon
2
null
transformers
23,191
--- tags: - conversational --- # Yui DialoGPT Model
Luxiere/DialoGPT-medium-tyrion
c517b785a7107e52e18b2e375de7ded4a554e42d
2021-10-20T17:05:16.000Z
[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]
conversational
false
Luxiere
null
Luxiere/DialoGPT-medium-tyrion
2
null
transformers
23,192
--- tags: - conversational --- # Tyrion DialoGPT Model
MM98/ft-bz
732d368f43eb78160231edda7e5ca3a99f3a9478
2022-01-05T17:34:34.000Z
[ "pytorch", "gpt2", "text-generation", "transformers" ]
text-generation
false
MM98
null
MM98/ft-bz
2
null
transformers
23,193
Entry not found
MMG/bert-base-spanish-wwm-cased-finetuned-squad2-es
810a1e6f6616930d8e36d48635958a67f28bc6df
2021-12-22T13:11:46.000Z
[ "pytorch", "tensorboard", "bert", "question-answering", "es", "dataset:squad_es", "transformers", "generated_from_trainer", "model-index", "autotrain_compatible" ]
question-answering
false
MMG
null
MMG/bert-base-spanish-wwm-cased-finetuned-squad2-es
2
null
transformers
23,194
--- tags: - generated_from_trainer datasets: - squad_es model-index: - name: bert-base-spanish-wwm-cased-finetuned-squad2-es results: [] language: - es --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-spanish-wwm-cased-finetuned-squad2-es This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-cased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased) on the squad_es dataset. It achieves the following results on the evaluation set: - Loss: 1.2841 {'exact': 62.53162421993591, 'f1': 69.33421368741254} ### Framework versions - Transformers 4.14.1 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3
MYX4567/distilbert-base-uncased-finetuned-squad
4595ea48a20995baf6439e07546c1281e02b6878
2021-07-28T08:07:15.000Z
[ "pytorch", "tensorboard", "distilbert", "question-answering", "dataset:squad", "transformers", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible" ]
question-answering
false
MYX4567
null
MYX4567/distilbert-base-uncased-finetuned-squad
2
null
transformers
23,195
--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad model_index: - name: distilbert-base-uncased-finetuned-squad results: - task: name: Question Answering type: question-answering dataset: name: squad type: squad args: plain_text --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # 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: 1.1520 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### 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: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.2177 | 1.0 | 5533 | 1.1565 | | 0.9472 | 2.0 | 11066 | 1.1174 | | 0.7634 | 3.0 | 16599 | 1.1520 | ### Framework versions - Transformers 4.9.1 - Pytorch 1.9.0+cu102 - Datasets 1.10.2 - Tokenizers 0.10.3
MaggieXM/deberta-base-finetuned-squad
6b69c4e16675e02dafc757785ca06411f5c72655
2022-02-04T09:41:38.000Z
[ "pytorch", "tensorboard", "deberta", "question-answering", "dataset:squad", "transformers", "generated_from_trainer", "license:mit", "model-index", "autotrain_compatible" ]
question-answering
false
MaggieXM
null
MaggieXM/deberta-base-finetuned-squad
2
null
transformers
23,196
--- license: mit tags: - generated_from_trainer datasets: - squad model-index: - name: deberta-base-finetuned-squad results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # deberta-base-finetuned-squad This model is a fine-tuned version of [microsoft/deberta-base](https://huggingface.co/microsoft/deberta-base) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 0.0001 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 0.0 | 2 | 5.3843 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0
MagmaCubes1133/DialoGPT-large-rick
b0c6dfd9609cb452d7dd8b919f2904b1935dafd0
2021-10-04T16:56:23.000Z
[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]
conversational
false
MagmaCubes1133
null
MagmaCubes1133/DialoGPT-large-rick
2
null
transformers
23,197
--- tags: conversational --- #Rick Sanchez DialoGPT Model
Mahalakshmi/wav2vec2-large-xlsr-53-demo-colab
630cbab60bf1e4bd68c19f84c64c94fd36d12b28
2022-03-24T11:53:08.000Z
[ "pytorch", "ne", "dataset:openslr", "automatic-speech-recognition", "robust-speech-event", "hf-asr-leaderboard", "license:apache-2.0", "model-index" ]
automatic-speech-recognition
false
Mahalakshmi
null
Mahalakshmi/wav2vec2-large-xlsr-53-demo-colab
2
null
null
23,198
--- language: - ne license: apache-2.0 tags: - automatic-speech-recognition - robust-speech-event - hf-asr-leaderboard datasets: - openslr model-index: - name: wav2vec2-large-xlsr-53-tamil results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: openslr type: openslr args: ne metrics: - name: Test WER type: wer value: 25.02 --- #xlsr-large-53-tamil
Mahalakshmi/wav2vec2-xls-r-300m-demo-colab
ebd6415b568196ef320db1174b67feac0229753a
2022-02-06T13:51:42.000Z
[ "pytorch", "wav2vec2", "automatic-speech-recognition", "dataset:common_voice", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
automatic-speech-recognition
false
Mahalakshmi
null
Mahalakshmi/wav2vec2-xls-r-300m-demo-colab
2
null
transformers
23,199
--- license: apache-2.0 tags: - generated_from_trainer datasets: - common_voice model-index: - name: wav2vec2-xls-r-300m-demo-colab results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-xls-r-300m-demo-colab This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. It achieves the following results on the evaluation set: - eval_loss: 0.9475 - eval_wer: 1.0377 - eval_runtime: 70.5646 - eval_samples_per_second: 25.239 - eval_steps_per_second: 3.16 - epoch: 21.05 - step: 2000 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 300 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.2+cu102 - Datasets 1.18.2.dev0 - Tokenizers 0.11.0