license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1 class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
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mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 1.0962 | 1.0 | 1012 | 0.7528 | 0.3793 | 0.6109 | 0.4411 | 0.4411 | | 0.7022 | 2.0 | 2024 | 0.6763 | 0.3992 | 0.6557 | 0.4799 | 0.4799 | | 0.6136 | 3.0 | 3036 | 0.6751 | 0.3995 | 0.6597 | 0.4824 | 0.4824 | | 0.5444 | 4.0 | 4048 | 0.6799 | 0.3891 | 0.6817 | 0.4854 | 0.4854 | | 0.4846 | 5.0 | 5060 | 0.7371 | 0.4030 | 0.6701 | 0.4906 | 0.4906 | | 0.4379 | 6.0 | 6072 | 0.7520 | 0.3956 | 0.6788 | 0.4887 | 0.4887 | | 0.404 | 7.0 | 7084 | 0.7788 | 0.3801 | 0.6854 | 0.4800 | 0.4800 | | 8d7c201cfb9bde9eb6a088e784a9c254 |
apache-2.0 | ['automatic-speech-recognition', 'es'] | false | exp_w2v2t_es_no-pretraining_s953 Fine-tuned randomly initialized wav2vec2 model for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool. | 45b36f8dac9d9645b90b6059a73a63b4 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-finetuned-coscan-sex This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the coscan-speech dataset. It achieves the following results on the evaluation set: - Loss: 0.0229 - Accuracy: 0.9965 | 3cabbe11dae354c24e34536a51abb52d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.0034 | 1.0 | 6644 | 0.0229 | 0.9965 | | 1116d4dc3c86c047aaca11007b2764bf |
mit | ['generated_from_trainer'] | false | edos-2023-baseline-roberta-base-label_sexist This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4729 - F1: 0.8048 | 7dbe97cf2d2326b72f5d7b5d17abc50c |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.4114 | 1.14 | 400 | 0.3516 | 0.7954 | | 0.2725 | 2.29 | 800 | 0.4086 | 0.7925 | | 0.2134 | 3.43 | 1200 | 0.4404 | 0.8062 | | 0.1632 | 4.57 | 1600 | 0.4729 | 0.8048 | | 39898724c7a2d7dafedfc28c5a02ac2c |
apache-2.0 | ['generated_from_trainer'] | false | beit-base-patch16-224-pt22k-ft22k-finetunedt This model is a fine-tuned version of [microsoft/beit-base-patch16-224-pt22k-ft22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k-ft22k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0147 - Accuracy: 1.0 | 7daff96ec60a3d2e929334c86b48cedb |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.4714 | 1.0 | 25 | 0.0147 | 1.0 | | 0.0089 | 2.0 | 50 | 0.0008 | 1.0 | | 0.0101 | 3.0 | 75 | 0.0003 | 1.0 | | 0.0021 | 4.0 | 100 | 0.0002 | 1.0 | | 0.0028 | 5.0 | 125 | 0.0001 | 1.0 | | 0.0016 | 6.0 | 150 | 0.0001 | 1.0 | | 0.0044 | 7.0 | 175 | 0.0001 | 1.0 | | 0.0007 | 8.0 | 200 | 0.0001 | 1.0 | | 0.0013 | 9.0 | 225 | 0.0001 | 1.0 | | 0.0004 | 10.0 | 250 | 0.0001 | 1.0 | | b6809ce8bb0a69994aeb1e3e994a9945 |
apache-2.0 | [] | false | Usage ```python from transformers import T5Tokenizer, T5ForConditionalGeneration tokenizer = T5Tokenizer.from_pretrained("Langboat/mengzi-t5-base") model = T5ForConditionalGeneration.from_pretrained("Langboat/mengzi-t5-base") ``` | b4939d550cadb60d8f7b07621d0f17d0 |
apache-2.0 | ['generated_from_trainer'] | false | favs-filtersort-multilabel-classification-bert-base-cased This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the filter_sort dataset. It achieves the following results on the evaluation set: - Loss: 0.3066 - F1: 0.7429 - Roc Auc: 0.8142 - Accuracy: 0.2 | 0daa3de48b2e427bb8f416a486061a07 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:| | 0.7601 | 1.0 | 12 | 0.6966 | 0.2564 | 0.4518 | 0.0 | | 0.6757 | 2.0 | 24 | 0.5629 | 0.6667 | 0.7785 | 0.0 | | 0.5796 | 3.0 | 36 | 0.4652 | 0.6286 | 0.7477 | 0.0 | | 0.5026 | 4.0 | 48 | 0.4161 | 0.6479 | 0.7605 | 0.0 | | 0.4282 | 5.0 | 60 | 0.3830 | 0.6849 | 0.7862 | 0.0 | | 0.4085 | 6.0 | 72 | 0.3658 | 0.7273 | 0.7962 | 0.0 | | 0.3847 | 7.0 | 84 | 0.3538 | 0.7353 | 0.8052 | 0.0 | | 0.3829 | 8.0 | 96 | 0.3457 | 0.6761 | 0.7772 | 0.0 | | 0.3758 | 9.0 | 108 | 0.3409 | 0.6857 | 0.7810 | 0.0 | | 0.3487 | 10.0 | 120 | 0.3327 | 0.7143 | 0.7976 | 0.0 | | 0.3421 | 11.0 | 132 | 0.3268 | 0.6866 | 0.7758 | 0.0 | | 0.3351 | 12.0 | 144 | 0.3183 | 0.7059 | 0.7886 | 0.0 | | 0.3245 | 13.0 | 156 | 0.3149 | 0.7246 | 0.8014 | 0.0 | | 0.3191 | 14.0 | 168 | 0.3087 | 0.7246 | 0.8014 | 0.1 | | 0.3083 | 15.0 | 180 | 0.3066 | 0.7429 | 0.8142 | 0.2 | | 0.3061 | 16.0 | 192 | 0.3062 | 0.7429 | 0.8142 | 0.2 | | 0.2935 | 17.0 | 204 | 0.3017 | 0.7429 | 0.8142 | 0.2 | | 0.2888 | 18.0 | 216 | 0.3009 | 0.7429 | 0.8142 | 0.2 | | 0.297 | 19.0 | 228 | 0.3022 | 0.7429 | 0.8142 | 0.2 | | 0.2868 | 20.0 | 240 | 0.3014 | 0.7429 | 0.8142 | 0.2 | | e517e5d47e4a40a98cb6487e9a5c25a4 |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | UD v2.5 benchmarking pipeline for UD_English-EWT | Feature | Description | | --- | --- | | **Name** | `en_udv25_englishewt_trf` | | **Version** | `0.0.1` | | **spaCy** | `>=3.2.1,<3.3.0` | | **Default Pipeline** | `experimental_char_ner_tokenizer`, `transformer`, `tagger`, `morphologizer`, `parser`, `experimental_edit_tree_lemmatizer` | | **Components** | `experimental_char_ner_tokenizer`, `transformer`, `senter`, `tagger`, `morphologizer`, `parser`, `experimental_edit_tree_lemmatizer` | | **Vectors** | 0 keys, 0 unique vectors (0 dimensions) | | **Sources** | [Universal Dependencies v2.5](https://lindat.mff.cuni.cz/repository/xmlui/handle/11234/1-3105) (Zeman, Daniel; et al.) | | **License** | `CC BY-SA 4.0` | | **Author** | [Explosion](https://explosion.ai) | | 61281f9b183a4cfc2c888746aec5558e |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | Label Scheme <details> <summary>View label scheme (1760 labels for 6 components)</summary> | Component | Labels | | --- | --- | | **`experimental_char_ner_tokenizer`** | `TOKEN` | | **`senter`** | `I`, `S` | | **`tagger`** | `$`, `''`, `,`, `-LRB-`, `-RRB-`, `.`, `:`, `ADD`, `AFX`, `CC`, `CD`, `DT`, `EX`, `FW`, `GW`, `HYPH`, `IN`, `JJ`, `JJR`, `JJS`, `LS`, `MD`, `NFP`, `NN`, `NNP`, `NNPS`, `NNS`, `PDT`, `POS`, `PRP`, `PRP$`, `RB`, `RBR`, `RBS`, `RP`, `SYM`, `TO`, `UH`, `VB`, `VBD`, `VBG`, `VBN`, `VBP`, `VBZ`, `WDT`, `WP`, `WP$`, `WRB`, `XX`, ```` | | **`morphologizer`** | `Number=Sing\|POS=PROPN`, `POS=PUNCT`, `Degree=Pos\|POS=ADJ`, `Number=Plur\|POS=NOUN`, `Mood=Ind\|POS=VERB\|Tense=Past\|VerbForm=Fin`, `Definite=Def\|POS=DET\|PronType=Art`, `Number=Sing\|POS=NOUN`, `POS=ADP`, `Number=Sing\|POS=DET\|PronType=Dem`, `Definite=Ind\|POS=DET\|PronType=Art`, `POS=AUX\|VerbForm=Fin`, `POS=AUX\|VerbForm=Inf`, `POS=VERB\|VerbForm=Ger`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `POS=PART`, `POS=VERB\|VerbForm=Inf`, `POS=SCONJ`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Mood=Ind\|POS=AUX\|Tense=Past\|VerbForm=Fin`, `POS=VERB\|Tense=Past\|VerbForm=Part`, `NumType=Card\|POS=NUM`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `POS=AUX\|VerbForm=Ger`, `POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `POS=ADV`, `Number=Sing\|POS=PRON\|PronType=Dem`, `Number=Plur\|POS=PROPN`, `Degree=Pos\|NumType=Ord\|POS=ADJ`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin`, `Case=Nom\|POS=PRON\|Person=2\|PronType=Prs`, `Mood=Ind\|POS=VERB\|Tense=Pres\|VerbForm=Fin`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `POS=VERB\|Tense=Pres\|VerbForm=Part`, `Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `POS=CCONJ`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Ind\|POS=AUX\|Tense=Pres\|VerbForm=Fin`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `POS=PRON\|PronType=Rel`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `POS=PRON`, `Number=Plur\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `POS=AUX\|Tense=Past\|VerbForm=Part`, `POS=DET`, `Number=Sing\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs`, `Degree=Pos\|POS=ADV`, `Degree=Cmp\|POS=ADV`, `Number=Sing\|POS=PRON`, `Degree=Cmp\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `POS=ADV\|PronType=Dem`, `POS=ADV\|PronType=Int`, `Number=Plur\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Number=Plur\|POS=PRON\|PronType=Dem`, `Mood=Imp\|POS=VERB\|VerbForm=Fin`, `Degree=Sup\|POS=ADJ`, `POS=PRON\|PronType=Int`, `NumType=Mult\|POS=ADV`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `POS=DET\|PronType=Int`, `POS=PRON\|Person=2\|Poss=Yes\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin`, `Number=Plur\|POS=DET\|PronType=Dem`, `POS=PRON\|Poss=Yes\|PronType=Int`, `Case=Acc\|POS=PRON\|Person=2\|PronType=Prs`, `POS=X`, `POS=PRON\|PronType=Dem`, `Number=Sing\|POS=PROPN\|Typo=Yes`, `POS=ADV\|PronType=Rel`, `Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Degree=Sup\|POS=ADV`, `POS=INTJ`, `Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Foreign=Yes\|POS=X`, `POS=SYM`, `Number=Sing\|POS=ADJ`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Past\|VerbForm=Fin`, `Mood=Imp\|POS=AUX\|VerbForm=Fin`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs\|Reflex=Yes`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Abbr=Yes\|POS=CCONJ`, `POS=SCONJ\|Typo=Yes`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Number=Sing\|POS=SYM`, `POS=DET\|Typo=Yes`, `Degree=Pos\|POS=PROPN`, `Abbr=Yes\|POS=ADP`, `POS=ADP\|Typo=Yes`, `Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs\|Reflex=Yes`, `POS=PRON\|Person=2\|Poss=Yes\|PronType=Prs\|Typo=Yes`, `Abbr=Yes\|POS=VERB\|Tense=Pres\|VerbForm=Part`, `Abbr=Yes\|POS=PART`, `POS=AUX\|Typo=Yes\|VerbForm=Fin`, `Degree=Pos\|POS=ADJ\|Typo=Yes`, `POS=VERB\|Tense=Past\|Typo=Yes\|VerbForm=Part\|Voice=Pass`, `Number=Sing\|POS=NOUN\|Typo=Yes`, `Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs\|Reflex=Yes`, `Abbr=Yes\|Number=Sing\|POS=NOUN`, `Degree=Pos\|POS=NOUN`, `POS=CCONJ\|Typo=Yes`, `Number=Sing\|POS=X`, `Abbr=Yes\|POS=SCONJ`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs\|Reflex=Yes`, `Mood=Ind\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Fin`, `Mood=Ind\|POS=AUX\|Tense=Pres\|Typo=Yes\|VerbForm=Fin`, `POS=ADV\|Typo=Yes`, `Mood=Ind\|Number=Sing\|POS=AUX\|Tense=Past\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin`, `Number=Sing\|POS=NUM`, `POS=PRON\|Poss=Yes\|PronType=Rel`, `Abbr=Yes\|Mood=Ind\|POS=VERB\|Tense=Pres\|VerbForm=Fin`, `Abbr=Yes\|POS=INTJ`, `Abbr=Yes\|POS=VERB\|VerbForm=Inf`, `Abbr=Yes\|Number=Sing\|POS=PRON`, `Abbr=Yes\|POS=PRON\|Person=2\|Poss=Yes\|PronType=Prs`, `Abbr=Yes\|POS=PRON\|PronType=Int`, `Abbr=Yes\|POS=AUX\|VerbForm=Fin`, `Abbr=Yes\|POS=ADV`, `Abbr=Yes\|Number=Plur\|POS=NOUN`, `Abbr=Yes\|Mood=Ind\|POS=AUX\|Tense=Pres\|Typo=Yes\|VerbForm=Fin`, `POS=ADJ`, `Number=Plur\|POS=NOUN\|Typo=Yes`, `POS=DET\|PronType=Rel\|Typo=Yes`, `POS=PART\|Typo=Yes`, `Abbr=Yes\|POS=DET`, `POS=DET\|PronType=Dem`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs\|Typo=Yes`, `Degree=Pos\|NumType=Ord\|POS=ADV`, `POS=NOUN`, `Number=Plur\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs\|Typo=Yes`, `POS=PRON\|Typo=Yes`, `Number=Plur\|POS=VERB`, `POS=VERB\|Typo=Yes\|VerbForm=Inf`, `Mood=Ind\|POS=VERB\|Tense=Past\|Typo=Yes\|VerbForm=Fin`, `Mood=Imp\|POS=AUX\|VerbForm=Inf`, `Abbr=Yes\|Mood=Imp\|POS=VERB\|VerbForm=Fin`, `Abbr=Yes\|Case=Nom\|POS=PRON\|Person=2\|PronType=Prs`, `POS=VERB\|Tense=Past\|Typo=Yes\|VerbForm=Part`, `Mood=Ind\|POS=AUX\|Tense=Past\|Typo=Yes\|VerbForm=Fin`, `Mood=Ind\|POS=VERB\|Tense=Pres\|Typo=Yes\|VerbForm=Fin`, `Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `POS=VERB\|Typo=Yes\|VerbForm=Ger`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|Typo=Yes\|VerbForm=Fin`, `Abbr=Yes\|POS=PRON`, `Abbr=Yes\|Number=Plur\|POS=NOUN\|Typo=Yes`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Typo=Yes`, `Abbr=Yes\|Case=Acc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs` | | **`parser`** | `ROOT`, `acl`, `acl:relcl`, `advcl`, `advmod`, `amod`, `appos`, `aux`, `aux:pass`, `case`, `cc`, `cc:preconj`, `ccomp`, `compound`, `compound:prt`, `conj`, `cop`, `csubj`, `dep`, `det`, `det:predet`, `discourse`, `expl`, `fixed`, `flat`, `flat:foreign`, `goeswith`, `iobj`, `list`, `mark`, `nmod`, `nmod:npmod`, `nmod:poss`, `nmod:tmod`, `nsubj`, `nsubj:pass`, `nummod`, `obj`, `obl`, `obl:npmod`, `obl:tmod`, `orphan`, `parataxis`, `punct`, `reparandum`, `vocative`, `xcomp` | | **`experimental_edit_tree_lemmatizer`** | `0`, `2`, `4`, `6`, `8`, `10`, `12`, `13`, `15`, `17`, `19`, `21`, `23`, `26`, `28`, `29`, `30`, `32`, `34`, `36`, `39`, `42`, `43`, `45`, `47`, `49`, `51`, `53`, `55`, `57`, `59`, `61`, `62`, `64`, `67`, `69`, `71`, `73`, `75`, `77`, `79`, `81`, `83`, `85`, `87`, `1`, `89`, `90`, `92`, `94`, `95`, `97`, `99`, `101`, `105`, `106`, `108`, `110`, `111`, `112`, `113`, `115`, `117`, `119`, `121`, `122`, `124`, `125`, `126`, `127`, `128`, `129`, `130`, `132`, `133`, `136`, `137`, `138`, `139`, `142`, `143`, `145`, `150`, `153`, `156`, `157`, `159`, `162`, `163`, `164`, `167`, `169`, `171`, `174`, `176`, `177`, `179`, `182`, `184`, `187`, `189`, `191`, `193`, `194`, `197`, `198`, `201`, `203`, `204`, `208`, `210`, `211`, `213`, `214`, `215`, `217`, `220`, `221`, `224`, `225`, `227`, `229`, `231`, `233`, `235`, `236`, `239`, `241`, `242`, `244`, `246`, `247`, `248`, `249`, `250`, `251`, `252`, `254`, `256`, `258`, `259`, `261`, `263`, `264`, `265`, `266`, `269`, `270`, `272`, `273`, `274`, `276`, `277`, `278`, `281`, `283`, `72`, `285`, `287`, `288`, `291`, `292`, `293`, `296`, `297`, `298`, `299`, `300`, `301`, `302`, `303`, `304`, `305`, `306`, `307`, `308`, `309`, `310`, `311`, `315`, `316`, `317`, `318`, `319`, `320`, `322`, `88`, `324`, `327`, `328`, `332`, `336`, `337`, `338`, `340`, `341`, `342`, `343`, `344`, `347`, `349`, `350`, `351`, `352`, `353`, `354`, `356`, `357`, `358`, `360`, `361`, `362`, `363`, `364`, `365`, `366`, `367`, `369`, `373`, `375`, `376`, `377`, `378`, `379`, `144`, `381`, `383`, `384`, `386`, `387`, `389`, `390`, `393`, `394`, `396`, `397`, `398`, `399`, `402`, `405`, `407`, `408`, `410`, `411`, `412`, `413`, `414`, `416`, `418`, `419`, `421`, `422`, `423`, `424`, `426`, `428`, `429`, `430`, `432`, `434`, `436`, `437`, `438`, `441`, `442`, `443`, `444`, `445`, `446`, `447`, `260`, `448`, `452`, `453`, `454`, `455`, `456`, `457`, `458`, 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cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | Accuracy | Type | Score | | --- | --- | | `TOKEN_F` | 99.15 | | `TOKEN_P` | 99.18 | | `TOKEN_R` | 99.11 | | `TOKEN_ACC` | 99.83 | | `SENTS_F` | 90.62 | | `SENTS_P` | 90.99 | | `SENTS_R` | 90.26 | | `TAG_ACC` | 96.36 | | `POS_ACC` | 96.94 | | `MORPH_ACC` | 96.91 | | `DEP_UAS` | 91.90 | | `DEP_LAS` | 89.42 | | `LEMMA_ACC` | 97.36 | | e00955f4287807abac2e5366ed7cbd78 |
apache-2.0 | ['setfit', 'sentence-transformers', 'text-classification'] | false | fathyshalab/domain_transfer_general-massive_cooking-roberta-large-v1-5-4 This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. | e84908186ddae762069e44baa4f51d6c |
mit | ['generated_from_trainer'] | false | training This model is a fine-tuned version of [microsoft/deberta-base](https://huggingface.co/microsoft/deberta-base) on the cynthiachan/FeedRef_10pct dataset. It achieves the following results on the evaluation set: - Loss: 0.0810 - Attackid Precision: 1.0 - Attackid Recall: 1.0 - Attackid F1: 1.0 - Attackid Number: 6 - Cve Precision: 1.0 - Cve Recall: 1.0 - Cve F1: 1.0 - Cve Number: 11 - Defenderthreat Precision: 0.0 - Defenderthreat Recall: 0.0 - Defenderthreat F1: 0.0 - Defenderthreat Number: 2 - Domain Precision: 1.0 - Domain Recall: 0.9565 - Domain F1: 0.9778 - Domain Number: 23 - Email Precision: 1.0 - Email Recall: 1.0 - Email F1: 1.0 - Email Number: 3 - Filepath Precision: 0.8841 - Filepath Recall: 0.8788 - Filepath F1: 0.8815 - Filepath Number: 165 - Hostname Precision: 1.0 - Hostname Recall: 1.0 - Hostname F1: 1.0 - Hostname Number: 12 - Ipv4 Precision: 1.0 - Ipv4 Recall: 1.0 - Ipv4 F1: 1.0 - Ipv4 Number: 12 - Md5 Precision: 0.8333 - Md5 Recall: 0.9615 - Md5 F1: 0.8929 - Md5 Number: 52 - Sha1 Precision: 0.6667 - Sha1 Recall: 0.8571 - Sha1 F1: 0.75 - Sha1 Number: 7 - Sha256 Precision: 0.9565 - Sha256 Recall: 1.0 - Sha256 F1: 0.9778 - Sha256 Number: 44 - Uri Precision: 0.0 - Uri Recall: 0.0 - Uri F1: 0.0 - Uri Number: 1 - Overall Precision: 0.9014 - Overall Recall: 0.9201 - Overall F1: 0.9107 - Overall Accuracy: 0.9851 | e1f688c67636b739ca3b3b295edb33fd |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Attackid Precision | Attackid Recall | Attackid F1 | Attackid Number | Cve Precision | Cve Recall | Cve F1 | Cve Number | Defenderthreat Precision | Defenderthreat Recall | Defenderthreat F1 | Defenderthreat Number | Domain Precision | Domain Recall | Domain F1 | Domain Number | Email Precision | Email Recall | Email F1 | Email Number | Filepath Precision | Filepath Recall | Filepath F1 | Filepath Number | Hostname Precision | Hostname Recall | Hostname F1 | Hostname Number | Ipv4 Precision | Ipv4 Recall | Ipv4 F1 | Ipv4 Number | Md5 Precision | Md5 Recall | Md5 F1 | Md5 Number | Sha1 Precision | Sha1 Recall | Sha1 F1 | Sha1 Number | Sha256 Precision | Sha256 Recall | Sha256 F1 | Sha256 Number | Uri Precision | Uri Recall | Uri F1 | Uri Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |:-------------:|:-----:|:----:|:---------------:|:------------------:|:---------------:|:-----------:|:---------------:|:-------------:|:----------:|:------:|:----------:|:------------------------:|:---------------------:|:-----------------:|:---------------------:|:----------------:|:-------------:|:---------:|:-------------:|:---------------:|:------------:|:--------:|:------------:|:------------------:|:---------------:|:-----------:|:---------------:|:------------------:|:---------------:|:-----------:|:---------------:|:--------------:|:-----------:|:-------:|:-----------:|:-------------:|:----------:|:------:|:----------:|:--------------:|:-----------:|:-------:|:-----------:|:----------------:|:-------------:|:---------:|:-------------:|:-------------:|:----------:|:------:|:----------:|:-----------------:|:--------------:|:----------:|:----------------:| | 0.3797 | 0.37 | 500 | 0.1998 | 0.0 | 0.0 | 0.0 | 6 | 0.0 | 0.0 | 0.0 | 11 | 0.0 | 0.0 | 0.0 | 2 | 0.0286 | 0.0435 | 0.0345 | 23 | 0.0 | 0.0 | 0.0 | 3 | 0.5108 | 0.7152 | 0.5960 | 165 | 0.1774 | 0.9167 | 0.2973 | 12 | 0.4 | 0.5 | 0.4444 | 12 | 0.3194 | 0.4423 | 0.3710 | 52 | 0.0 | 0.0 | 0.0 | 7 | 0.4588 | 0.8864 | 0.6047 | 44 | 0.0 | 0.0 | 0.0 | 1 | 0.3875 | 0.5858 | 0.4664 | 0.9593 | | 0.1713 | 0.75 | 1000 | 0.1619 | 0.6 | 0.5 | 0.5455 | 6 | 0.5 | 0.6364 | 0.56 | 11 | 0.0 | 0.0 | 0.0 | 2 | 0.6957 | 0.6957 | 0.6957 | 23 | 0.0 | 0.0 | 0.0 | 3 | 0.6879 | 0.6545 | 0.6708 | 165 | 0.5217 | 1.0 | 0.6857 | 12 | 0.5714 | 1.0 | 0.7273 | 12 | 0.6667 | 0.8846 | 0.7603 | 52 | 0.0 | 0.0 | 0.0 | 7 | 0.7692 | 0.9091 | 0.8333 | 44 | 0.0 | 0.0 | 0.0 | 1 | 0.6685 | 0.7219 | 0.6942 | 0.9664 | | 0.1152 | 1.12 | 1500 | 0.1096 | 0.8333 | 0.8333 | 0.8333 | 6 | 1.0 | 1.0 | 1.0 | 11 | 0.0 | 0.0 | 0.0 | 2 | 0.7826 | 0.7826 | 0.7826 | 23 | 1.0 | 1.0 | 1.0 | 3 | 0.7202 | 0.8424 | 0.7765 | 165 | 1.0 | 1.0 | 1.0 | 12 | 0.4444 | 1.0 | 0.6154 | 12 | 0.6944 | 0.9615 | 0.8065 | 52 | 0.0 | 0.0 | 0.0 | 7 | 0.8723 | 0.9318 | 0.9011 | 44 | 0.0 | 0.0 | 0.0 | 1 | 0.7312 | 0.8609 | 0.7908 | 0.9751 | | 0.1089 | 1.5 | 2000 | 0.1243 | 1.0 | 1.0 | 1.0 | 6 | 0.9167 | 1.0 | 0.9565 | 11 | 0.0 | 0.0 | 0.0 | 2 | 0.9048 | 0.8261 | 0.8636 | 23 | 1.0 | 1.0 | 1.0 | 3 | 0.8011 | 0.8788 | 0.8382 | 165 | 0.6667 | 1.0 | 0.8 | 12 | 0.9091 | 0.8333 | 0.8696 | 12 | 0.7812 | 0.9615 | 0.8621 | 52 | 0.0 | 0.0 | 0.0 | 7 | 0.7857 | 1.0 | 0.88 | 44 | 0.0 | 0.0 | 0.0 | 1 | 0.8065 | 0.8876 | 0.8451 | 0.9750 | | 0.0947 | 1.87 | 2500 | 0.0913 | 0.75 | 1.0 | 0.8571 | 6 | 1.0 | 1.0 | 1.0 | 11 | 0.0 | 0.0 | 0.0 | 2 | 0.8462 | 0.9565 | 0.8980 | 23 | 0.3333 | 0.6667 | 0.4444 | 3 | 0.8035 | 0.8424 | 0.8225 | 165 | 0.6 | 1.0 | 0.7500 | 12 | 1.0 | 1.0 | 1.0 | 12 | 0.7969 | 0.9808 | 0.8793 | 52 | 0.0 | 0.0 | 0.0 | 7 | 0.8302 | 1.0 | 0.9072 | 44 | 0.0 | 0.0 | 0.0 | 1 | 0.7952 | 0.8846 | 0.8375 | 0.9792 | | 0.0629 | 2.25 | 3000 | 0.0940 | 1.0 | 0.8333 | 0.9091 | 6 | 1.0 | 1.0 | 1.0 | 11 | 0.0 | 0.0 | 0.0 | 2 | 0.9565 | 0.9565 | 0.9565 | 23 | 1.0 | 1.0 | 1.0 | 3 | 0.8671 | 0.8303 | 0.8483 | 165 | 1.0 | 1.0 | 1.0 | 12 | 1.0 | 1.0 | 1.0 | 12 | 0.9273 | 0.9808 | 0.9533 | 52 | 0.25 | 0.1429 | 0.1818 | 7 | 0.8776 | 0.9773 | 0.9247 | 44 | 0.0 | 0.0 | 0.0 | 1 | 0.8946 | 0.8787 | 0.8866 | 0.9825 | | 0.0442 | 2.62 | 3500 | 0.1012 | 1.0 | 1.0 | 1.0 | 6 | 0.9167 | 1.0 | 0.9565 | 11 | 0.0 | 0.0 | 0.0 | 2 | 0.9091 | 0.8696 | 0.8889 | 23 | 0.75 | 1.0 | 0.8571 | 3 | 0.8182 | 0.8727 | 0.8446 | 165 | 1.0 | 1.0 | 1.0 | 12 | 1.0 | 1.0 | 1.0 | 12 | 0.92 | 0.8846 | 0.9020 | 52 | 0.5 | 1.0 | 0.6667 | 7 | 0.9565 | 1.0 | 0.9778 | 44 | 0.0 | 0.0 | 0.0 | 1 | 0.8616 | 0.9024 | 0.8815 | 0.9818 | | 0.0401 | 3.0 | 4000 | 0.0810 | 1.0 | 1.0 | 1.0 | 6 | 1.0 | 1.0 | 1.0 | 11 | 0.0 | 0.0 | 0.0 | 2 | 1.0 | 0.9565 | 0.9778 | 23 | 1.0 | 1.0 | 1.0 | 3 | 0.8841 | 0.8788 | 0.8815 | 165 | 1.0 | 1.0 | 1.0 | 12 | 1.0 | 1.0 | 1.0 | 12 | 0.8333 | 0.9615 | 0.8929 | 52 | 0.6667 | 0.8571 | 0.75 | 7 | 0.9565 | 1.0 | 0.9778 | 44 | 0.0 | 0.0 | 0.0 | 1 | 0.9014 | 0.9201 | 0.9107 | 0.9851 | | bc497eca89ab6baac8ac9ed877e5519c |
creativeml-openrail-m | ['coreml', 'stable-diffusion', 'text-to-image'] | false | Seek.art MEGA is a general use "anything" model that significantly improves on 1.5 across dozens of styles. Created by Coreco at [seek.art](https://seek.art/) This model was trained on nearly 10k high-quality public domain digital artworks with the goal of improving output quality across the board. We find the model to be highly flexible in its ability to mix various styles, subjects, and details. We recommend resolutions above 640px in one or both dimensions for best results. You can try this model and several others for free at [seek.art](https://seek.art/). We also recommend an inference tool supporting prompt weighting and high resolution optimization / fixing for best results. We suggest [InvokeAI](https://github.com/invoke-ai/InvokeAI) as a sensibly licensed and fully featured open-source inference tool. | cfbac0ff022042c9af780ac315961fe7 |
creativeml-openrail-m | ['coreml', 'stable-diffusion', 'text-to-image'] | false | Examples <img src="https://huggingface.co/coreco/seek.art_MEGA/resolve/main/examples.png" style="max-width: 800px;" width="100%"/> The above example images including the prompts and all relevant settings are available [here](https://seek.art/explore/search?collection=6112a64d-bd8b-4043-8d96-88c7cfa65c43). Additionally, search thousands of high quality prompts on [seek.art](https://seek.art/) for free. | 36952d6c8c00eade67297cc3c8af2fd4 |
creativeml-openrail-m | ['coreml', 'stable-diffusion', 'text-to-image'] | false | Use Restrictions You agree not to use the Model or Derivatives of the Model: - for the commercial purpose of hosted content generation (inference) without the express written permission of seek.art. Model output for personal use carries no such commercial restriction. - In any way that violates any applicable national, federal, state, local or international law or regulation; - For the purpose of exploiting, harming or attempting to exploit or harm minors in any way; - To generate or disseminate verifiably false information and/or content with the purpose of harming others; - To generate or disseminate personal identifiable information that can be used to harm an individual; - To defame, disparage or otherwise harass others; - For fully automated decision making that adversely impacts an individual’s legal rights or otherwise creates or modifies a binding, enforceable obligation; - For any use intended to or which has the effect of discriminating against or harming individuals or groups based on online or offline social behavior or known or predicted personal or personality characteristics; - To exploit any of the vulnerabilities of a specific group of persons based on their age, social, physical or mental characteristics, in order to materially distort the behavior of a person pertaining to that group in a manner that causes or is likely to cause that person or another person physical or psychological harm; - For any use intended to or which has the effect of discriminating against individuals or groups based on legally protected characteristics or categories; - To provide medical advice and medical results interpretation; - To generate or disseminate information for the purpose to be used for administration of justice, law enforcement, immigration or asylum processes, such as predicting an individual will commit fraud/crime commitment (e.g. by text profiling, drawing causal relationships between assertions made in documents, indiscriminate and arbitrarily-targeted use). | 99bf23039f6a4adc1c6eeefafdfb9b48 |
apache-2.0 | ['generated_from_trainer'] | false | finetuning-sentiment-model-3000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.3186 - Accuracy: 0.87 - F1: 0.8770 | 6ab7a559704280833d85db28e9d8e1ad |
apache-2.0 | ['generated_from_trainer'] | false | tiny-mlm-glue-cola-custom-tokenizer This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the None dataset. It achieves the following results on the evaluation set: - Loss: nan | 35fdcc857a46fd63e14ce3e97adad7d3 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 7.2575 | 0.47 | 500 | 6.4792 | | 6.4145 | 0.94 | 1000 | 6.4699 | | 6.2252 | 1.4 | 1500 | 6.5489 | | 6.0413 | 1.87 | 2000 | 6.3427 | | 5.8394 | 2.34 | 2500 | 6.2134 | | 5.825 | 2.81 | 3000 | nan | | 5.8071 | 3.27 | 3500 | 6.1627 | | 5.6601 | 3.74 | 4000 | 6.0835 | | 5.686 | 4.21 | 4500 | 6.0319 | | 5.6029 | 4.68 | 5000 | 5.9500 | | 5.5236 | 5.14 | 5500 | 5.9621 | | 5.586 | 5.61 | 6000 | 5.8955 | | 5.5582 | 6.08 | 6500 | 6.0435 | | 5.412 | 6.55 | 7000 | 6.0175 | | 5.397 | 7.02 | 7500 | nan | | 571ee2d5fad1590493f176cfb32540bc |
apache-2.0 | ['generated_from_trainer'] | false | recipe-lr8e06-wd0.1-bs32 This model is a fine-tuned version of [paola-md/recipe-distilroberta-Is](https://huggingface.co/paola-md/recipe-distilroberta-Is) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2752 - Rmse: 0.5246 - Mse: 0.2752 - Mae: 0.4184 | bf1db6ce786e714aa85791eab0b52059 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rmse | Mse | Mae | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:| | 0.2769 | 1.0 | 623 | 0.2773 | 0.5266 | 0.2773 | 0.4297 | | 0.2745 | 2.0 | 1246 | 0.2739 | 0.5233 | 0.2739 | 0.4144 | | 0.2733 | 3.0 | 1869 | 0.2752 | 0.5246 | 0.2752 | 0.4215 | | 0.2722 | 4.0 | 2492 | 0.2744 | 0.5238 | 0.2744 | 0.4058 | | 0.2714 | 5.0 | 3115 | 0.2758 | 0.5252 | 0.2758 | 0.4233 | | 0.2705 | 6.0 | 3738 | 0.2752 | 0.5246 | 0.2752 | 0.4184 | | 9eeba128cb74a443d75c5a73d4628b10 |
mit | [] | false | Basic use ```python import cv2 import numpy as np import onnxruntime as rt from huggingface_hub import hf_hub_download tagger_model_path = hf_hub_download(repo_id="skytnt/deepdanbooru_onnx", filename="deepdanbooru.onnx") tagger_model = rt.InferenceSession(tagger_model_path, providers=['CUDAExecutionProvider', 'CPUExecutionProvider']) tagger_model_meta = tagger_model.get_modelmeta().custom_metadata_map tagger_tags = eval(tagger_model_meta['tags']) def tagger_predict(image, score_threshold): s = 512 h, w = image.shape[:-1] h, w = (s, int(s * w / h)) if h > w else (int(s * h / w), s) ph, pw = s - h, s - w image = cv2.resize(image, (w, h), interpolation=cv2.INTER_AREA) image = cv2.copyMakeBorder(image, ph // 2, ph - ph // 2, pw // 2, pw - pw // 2, cv2.BORDER_REPLICATE) image = image.astype(np.float32) / 255 image = img_new[np.newaxis, :] probs = tagger_model.run(None, {"input_1": image})[0][0] probs = probs.astype(np.float32) res = [] for prob, label in zip(probs.tolist(), tagger_tags): if prob < score_threshold: continue res.append(label) return res img = cv2.imread("test.jpg") img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) tags = tagger_predict(img, 0.5) print(tags) ``` | 5a7ad9900be699424bbad62b29fbf6c1 |
mit | [] | false | Multi-gpu batch process ```python import cv2 import torch import os import numpy as np import onnxruntime as rt from huggingface_hub import hf_hub_download from torch.utils.data import DataLoader, Dataset from PIL import Image from tqdm import tqdm from threading import Thread class MyDataset(Dataset): def __init__(self, image_list): self.image_list = image_list def __len__(self): length = len(self.image_list) return length def __getitem__(self, index): image = Image.open(self.image_list[index]).convert("RGB") image = np.asarray(image) s = 512 h, w = image.shape[:-1] h, w = (s, int(s * w / h)) if h > w else (int(s * h / w), s) ph, pw = s - h, s - w image = cv2.resize(image, (w, h), interpolation=cv2.INTER_AREA) image = cv2.copyMakeBorder(image, ph // 2, ph - ph // 2, pw // 2, pw - pw // 2, cv2.BORDER_REPLICATE) image = image.astype(np.float32) / 255 image = torch.from_numpy(image) idx = torch.tensor([index], dtype=torch.int32) return image, idx def get_images(path): def file_ext(fname): return os.path.splitext(fname)[1].lower() all_files = { os.path.relpath(os.path.join(root, fname), path) for root, _dirs, files in os.walk(path) for fname in files } all_images = sorted( os.path.join(path, fname) for fname in all_files if file_ext(fname) in [".png", ".jpg", ".jpeg"] ) print(len(all_images)) return all_images def process(all_images, batch_size=8, score_threshold=0.35): predictions = {} def work_fn(images, device_id): dataset = MyDataset(images) dataloader = DataLoader( dataset, batch_size=batch_size, shuffle=False, persistent_workers=True, num_workers=4, pin_memory=True, ) for data in tqdm(dataloader): image, idxs = data image = image.numpy() probs = tagger_model[device_id].run(None, {"input_1": image})[0] probs = probs.astype(np.float32) bs = probs.shape[0] for i in range(bs): tags = [] for prob, label in zip(probs[i].tolist(), tagger_tags): if prob > score_threshold: tags.append((label, prob)) predictions[images[idxs[i].item()]] = tags gpu_num = len(tagger_model) image_num = (len(all_images) // gpu_num) + 1 ts = [Thread(target=work_fn, args=(all_images[i * image_num:(i + 1) * image_num], i)) for i in range(gpu_num)] for t in ts: t.start() for t in ts: t.join() return predictions gpu_num = 4 batch_size = 8 tagger_model_path = hf_hub_download(repo_id="skytnt/deepdanbooru_onnx", filename="deepdanbooru.onnx") tagger_model = [ rt.InferenceSession(tagger_model_path, providers=['CUDAExecutionProvider'], provider_options=[{'device_id': i}]) for i in range(gpu_num)] tagger_model_meta = tagger_model[0].get_modelmeta().custom_metadata_map tagger_tags = eval(tagger_model_meta['tags']) all_images = get_images("./data") predictions = process(all_images, batch_size) ``` | 3c195da5f31fbc70bf70d5cde7ab7077 |
cc-by-4.0 | ['generated_from_trainer'] | false | bert-large-uncased-whole-word-masking-squad2-with-ner-Pwhatisthe-conll2003-with-neg-with-repeat This model is a fine-tuned version of [deepset/bert-large-uncased-whole-word-masking-squad2](https://huggingface.co/deepset/bert-large-uncased-whole-word-masking-squad2) on the squad_v2 and the conll2003 datasets. | e8c814fbe080c367ccaecf4134cf489a |
mit | ['generated_from_keras_callback'] | false | Deep98/Cardinal__Catholicism_-clustered This model is a fine-tuned version of [nandysoham16/11-clustered_aug](https://huggingface.co/nandysoham16/11-clustered_aug) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.3075 - Train End Logits Accuracy: 0.8958 - Train Start Logits Accuracy: 0.9306 - Validation Loss: 1.3105 - Validation End Logits Accuracy: 0.75 - Validation Start Logits Accuracy: 0.5 - Epoch: 0 | 7517080fd13eeff1d9cfbe9a990e711c |
mit | ['generated_from_keras_callback'] | false | Training results | Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:| | 0.3075 | 0.8958 | 0.9306 | 1.3105 | 0.75 | 0.5 | 0 | | 2fc57774eec76c9db241f4f0cbe13eb9 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.4795 | 1.28 | 100 | 2.2135 | | 2.0935 | 2.56 | 200 | 2.1722 | | 1.9961 | 3.84 | 300 | 2.1639 | | 1.9455 | 5.13 | 400 | 2.1605 | | 1.9083 | 6.41 | 500 | 2.1609 | | f467718346daa905d424b92185770e5b |
apache-2.0 | ['translation'] | false | alv-eng * source group: Atlantic-Congo languages * target group: English * OPUS readme: [alv-eng](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/alv-eng/README.md) * model: transformer * source language(s): ewe fuc fuv ibo kin lin lug nya run sag sna swh toi_Latn tso umb wol xho yor zul * target language(s): eng * model: transformer * pre-processing: normalization + SentencePiece (spm32k,spm32k) * download original weights: [opus2m-2020-07-31.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/alv-eng/opus2m-2020-07-31.zip) * test set translations: [opus2m-2020-07-31.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/alv-eng/opus2m-2020-07-31.test.txt) * test set scores: [opus2m-2020-07-31.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/alv-eng/opus2m-2020-07-31.eval.txt) | 70a90f4759a4aa49d42e52652e02a42b |
apache-2.0 | ['translation'] | false | Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | Tatoeba-test.ewe-eng.ewe.eng | 6.3 | 0.328 | | Tatoeba-test.ful-eng.ful.eng | 0.4 | 0.108 | | Tatoeba-test.ibo-eng.ibo.eng | 4.5 | 0.196 | | Tatoeba-test.kin-eng.kin.eng | 30.7 | 0.511 | | Tatoeba-test.lin-eng.lin.eng | 2.8 | 0.213 | | Tatoeba-test.lug-eng.lug.eng | 3.4 | 0.140 | | Tatoeba-test.multi.eng | 20.9 | 0.376 | | Tatoeba-test.nya-eng.nya.eng | 38.7 | 0.492 | | Tatoeba-test.run-eng.run.eng | 24.5 | 0.417 | | Tatoeba-test.sag-eng.sag.eng | 5.5 | 0.177 | | Tatoeba-test.sna-eng.sna.eng | 26.9 | 0.412 | | Tatoeba-test.swa-eng.swa.eng | 4.9 | 0.196 | | Tatoeba-test.toi-eng.toi.eng | 3.9 | 0.147 | | Tatoeba-test.tso-eng.tso.eng | 76.7 | 0.957 | | Tatoeba-test.umb-eng.umb.eng | 4.0 | 0.195 | | Tatoeba-test.wol-eng.wol.eng | 3.7 | 0.170 | | Tatoeba-test.xho-eng.xho.eng | 38.9 | 0.556 | | Tatoeba-test.yor-eng.yor.eng | 25.1 | 0.412 | | Tatoeba-test.zul-eng.zul.eng | 46.1 | 0.623 | | c1a18ef877178e69130079b93c2e5714 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: alv-eng - source_languages: alv - target_languages: eng - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/alv-eng/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['sn', 'rw', 'wo', 'ig', 'sg', 'ee', 'zu', 'lg', 'ts', 'ln', 'ny', 'yo', 'rn', 'xh', 'alv', 'en'] - src_constituents: {'sna', 'kin', 'wol', 'ibo', 'swh', 'sag', 'ewe', 'zul', 'fuc', 'lug', 'tso', 'lin', 'nya', 'yor', 'run', 'xho', 'fuv', 'toi_Latn', 'umb'} - tgt_constituents: {'eng'} - src_multilingual: True - tgt_multilingual: False - prepro: normalization + SentencePiece (spm32k,spm32k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/alv-eng/opus2m-2020-07-31.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/alv-eng/opus2m-2020-07-31.test.txt - src_alpha3: alv - tgt_alpha3: eng - short_pair: alv-en - chrF2_score: 0.376 - bleu: 20.9 - brevity_penalty: 1.0 - ref_len: 15208.0 - src_name: Atlantic-Congo languages - tgt_name: English - train_date: 2020-07-31 - src_alpha2: alv - tgt_alpha2: en - prefer_old: False - long_pair: alv-eng - helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535 - transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b - port_machine: brutasse - port_time: 2020-08-21-14:41 | 3e90861073d525f5d4a901585256010e |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer', 'robust-speech-event', 'hf-asr-leaderboard'] | false | XLS-R-300M - Maltese 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 - MT dataset. It achieves the following results on the evaluation set: - Loss: 0.1895 - Wer: 0.1984 | 4b1b9ee22d1a325107510bbbe70a190b |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer', 'robust-speech-event', 'hf-asr-leaderboard'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7.5e-05 - train_batch_size: 32 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 60.0 - mixed_precision_training: Native AMP | bf172781988ee67a582da6686aa6d150 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer', 'robust-speech-event', 'hf-asr-leaderboard'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.4219 | 3.6 | 400 | 3.3127 | 1.0 | | 3.0399 | 7.21 | 800 | 3.0330 | 1.0 | | 1.5756 | 10.81 | 1200 | 0.6108 | 0.5724 | | 1.0995 | 14.41 | 1600 | 0.3091 | 0.3154 | | 0.9639 | 18.02 | 2000 | 0.2596 | 0.2841 | | 0.9032 | 21.62 | 2400 | 0.2270 | 0.2514 | | 0.8145 | 25.23 | 2800 | 0.2172 | 0.2483 | | 0.7845 | 28.83 | 3200 | 0.2084 | 0.2333 | | 0.7694 | 32.43 | 3600 | 0.1974 | 0.2234 | | 0.7333 | 36.04 | 4000 | 0.2020 | 0.2185 | | 0.693 | 39.64 | 4400 | 0.1947 | 0.2148 | | 0.6802 | 43.24 | 4800 | 0.1960 | 0.2102 | | 0.667 | 46.85 | 5200 | 0.1904 | 0.2072 | | 0.6486 | 50.45 | 5600 | 0.1881 | 0.2009 | | 0.6339 | 54.05 | 6000 | 0.1877 | 0.1989 | | 0.6254 | 57.66 | 6400 | 0.1893 | 0.2003 | | 93e83430d51c802070ff70141786f7c7 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer', 'robust-speech-event', 'hf-asr-leaderboard'] | false | Evaluation Commands 1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test` ```bash python eval.py --model_id anuragshas/wav2vec2-xls-r-300m-mt-cv8-with-lm --dataset mozilla-foundation/common_voice_8_0 --config mt --split test ``` | 2e45083d411f27c17b66f9e6358173bb |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer', 'robust-speech-event', 'hf-asr-leaderboard'] | false | Inference With LM ```python import torch from datasets import load_dataset from transformers import AutoModelForCTC, AutoProcessor import torchaudio.functional as F model_id = "anuragshas/wav2vec2-xls-r-300m-mt-cv8-with-lm" sample_iter = iter(load_dataset("mozilla-foundation/common_voice_8_0", "mt", split="test", streaming=True, use_auth_token=True)) sample = next(sample_iter) resampled_audio = F.resample(torch.tensor(sample["audio"]["array"]), 48_000, 16_000).numpy() model = AutoModelForCTC.from_pretrained(model_id) processor = AutoProcessor.from_pretrained(model_id) input_values = processor(resampled_audio, return_tensors="pt").input_values with torch.no_grad(): logits = model(input_values).logits transcription = processor.batch_decode(logits.numpy()).text | 8be2e1c13bb8cf9547713e045df93636 |
apache-2.0 | [] | false | [Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) for **Closed Book Question Answering**. The model was pre-trained using T5's denoising objective on [C4](https://huggingface.co/datasets/c4), subsequently additionally pre-trained using [REALM](https://arxiv.org/pdf/2002.08909.pdf)'s salient span masking objective on [Wikipedia](https://huggingface.co/datasets/wikipedia), and finally fine-tuned on [Web Questions (WQ)](https://huggingface.co/datasets/web_questions). **Note**: The model was fine-tuned on 90% of the train splits of [Web Questions (WQ)](https://huggingface.co/datasets/web_questions) for 20k steps and validated on the held-out 10% of the train split. Other community Checkpoints: [here](https://huggingface.co/models?search=ssm) Paper: [How Much Knowledge Can You Pack Into the Parameters of a Language Model?](https://arxiv.org/abs/1910.10683.pdf) Authors: *Adam Roberts, Colin Raffel, Noam Shazeer* | d53e78c4fca89315351c12e19c5f85cb |
apache-2.0 | [] | false | Results on Web Questions - Test Set |Id | link | Exact Match | |---|---|---| |**T5-11b**|**https://huggingface.co/google/t5-11b-ssm-wqo**|**40.8**| |T5-xxl|https://huggingface.co/google/t5-xxl-ssm-wqo|42.8| | 4da0b998a87b6a35139c63787ea5706c |
apache-2.0 | [] | false | Usage The model can be used as follows for **closed book question answering**: ```python from transformers import AutoModelForSeq2SeqLM, AutoTokenizer t5_qa_model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-11b-ssm-wqo") t5_tok = AutoTokenizer.from_pretrained("google/t5-11b-ssm-wqo") input_ids = t5_tok("When was Franklin D. Roosevelt born?", return_tensors="pt").input_ids gen_output = t5_qa_model.generate(input_ids)[0] print(t5_tok.decode(gen_output, skip_special_tokens=True)) ``` | 57b99b1f760a283352e9ff59fbd3fc4e |
apache-2.0 | ['generated_from_trainer'] | false | t5-base-fine-tuned-for-Punctuation-Restoration This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1097 | 3915932ee702e96cdf8b0162a2910dec |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Whisper Base Yue This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the Common Voice 11.0 yue dataset. It achieves the following results on the evaluation set: - Loss: 0.3671 - Wer: 69.5864 | 1d001d6268ececb049b2eac726460b05 |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - 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: 200 - training_steps: 1000 - mixed_precision_training: Native AMP | 12572fafd413de9304b09512ce34db1e |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0998 | 2.78 | 500 | 0.3500 | 71.4517 | | 0.0085 | 5.56 | 1000 | 0.3671 | 69.5864 | | 74f65a675e07800ae4e391c4d0e5a19a |
apache-2.0 | ['translation'] | false | opus-mt-lt-de * source languages: lt * target languages: de * OPUS readme: [lt-de](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/lt-de/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/lt-de/opus-2020-01-21.zip) * test set translations: [opus-2020-01-21.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/lt-de/opus-2020-01-21.test.txt) * test set scores: [opus-2020-01-21.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/lt-de/opus-2020-01-21.eval.txt) | 218a7c0a60567760459bd8485a0e3bc6 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_model_fine_tuned_unlabeled_all This model is a fine-tuned version of [nouman-10/distilbert_model_fine_tuned_unlabeled_all](https://huggingface.co/nouman-10/distilbert_model_fine_tuned_unlabeled_all) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1708 - Accuracy: 0.95 | 3bce88dba3602ecb989d69239e42cd7b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.1329 | 1.0 | 875 | 0.1708 | 0.95 | | 291e9e4cd6514d9b0b29e64f12143044 |
mit | ['music'] | false | Model description TunesFormer is a Transformer-based melody generation system trained on 285,449 melodies with musical forms (represented by control codes), where all scores are represented in ABC notation. It was introduced in the paper [TunesFormer: Forming Tunes with Control Codes](https://arxiv.org/abs/2301.02884) by Wu et al. The code is released in [this repository](https://github.com/sander-wood/tunesformer), and the dataset is released in [huggingface](https://huggingface.co/datasets/sander-wood/abc_cc). By utilizing specific symbols commonly found in ABC notation to indicate section boundaries, TunesFormer can understand and generate melodies with given musical forms based on control codes. The checkpoint released here is TunesFormer-GP (Global Placement), where all the control codes are placed at the beginning of the ABC notation. | a532ef858970707c70dbbb4a3da92596 |
mit | ['music'] | false | Intended uses & limitations You can use this model for melody generation conditioned on musical forms. All scores generated by this model can be written on one stave (for vocal solo or instrumental solo) in standard classical notation, and are in a variety of styles, e.g., blues, classical, folk, jazz, pop, and world music. The generated tunes are in ABC notation, and can be converted to sheet music or audio using [this website](https://ldzhangyx.github.io/abc/), or [this software](https://sourceforge.net/projects/easyabc/). TunesFormer supports the generation of up to 8 sections, and up to 32 bars per section. In addition, although TunesFormer mostly generates music correctly according to the control codes, due to the random nature of sampling, the musical structure generated by the model occasionally does not match that specified by the control codes when more than 6 sections are generated, or when more than 17 bars are generated for a single section. For more information, please check [our paper](https://arxiv.org/abs/2301.02884). | 1fcaab7caefeb0607f34524ca88b8764 |
mit | ['music'] | false | How to use 1. Install dependencies for the code released in [this repository](https://github.com/sander-wood/tunesformer): ``` torch 1.9.1+cu111 samplings 0.1.7 transformers 4.18.0 ``` 2. Set the `control_codes` and `prompt` in the script `run_inference.py` for conditional music generation. ``` control_codes = "[SECS_3][BARS_4][SIM_6][BARS_4][SIM_10][SIM_6][BARS_4]" prompt = """L:1/4 M:4/4 K:C "C" C C G G |"F" A A"C" G2 |"G" F F"C" E E |"G" D D"C" C2 ||""" ``` For TunesFormer, the input is a concatenation of `control_codes` and `prompt`. Both `control_codes` and `prompt` are optional. However, if you need to set the prompt, you must set the control codes. 3. Run the script `run_inference.py`. When running a script for the first time, the downloaded files will be cached for future reuse. ``` python run_inference.py -num_tunes 3 -max_length 1024 -top_p 0.9 -temperature 1.0 -seed 1 ``` 4. Enjoy tunes in the folder `output_tunes`! If you want to convert these ABC tunes to sheet music or audio, please refer to `Intended uses & limitations`. ``` X:1 L:1/4 M:4/4 K:C "C" C C G G |"F" A A"C" G2 |"G" F F"C" E E |"G" D D"C" C2 ||"C" G G"F" A A |"G" G G"C" E2 | "G" F F"C" E E |"G" D D"C" C2 ||"C" C C G G |"F" A A"C" G2 |"G" F F"C" E E |"G" D D"C" C2 |] X:2 L:1/4 M:4/4 K:C "C" C C G G |"F" A A"C" G2 |"G" F F"C" E E |"G" D D"C" C2 ||"C" E E"F" F F |"C" G G"F" A2 | "G7" F F"C" E E |"G" D D"C" C2 ||"C" C C G G |"F" A A"C" G2 |"G" F F"C" E E |"G" D D"C" C2 |] X:3 L:1/4 M:4/4 K:C "C" C C G G |"F" A A"C" G2 |"G" F F"C" E E |"G" D D"C" C2 ||"C" G G"F" A A |"C" G G"F" F2 | "C" E E"G" D D |"G" D D"C" C2 ||"C" C C G G |"F" A A"C" G2 |"G" F F"C" E E |"G" D D"C" C2 |] ``` | 692c3c65c37920508412b8de02403398 |
mit | ['music'] | false | Usage ``` optional arguments: -h, --help show this help message and exit -num_tunes NUM_TUNES the number of independently computed returned tunes -max_length MAX_LENGTH integer to define the maximum length in tokens of each tune -top_p TOP_P float to define the tokens that are within the sample operation of text generation -temperature TEMPERATURE the temperature of the sampling operation -seed SEED seed for randomstate ``` | 1bce271f305012f521c419f09540afe9 |
mit | ['music'] | false | BibTeX entry and citation info ```bibtex @misc{https://doi.org/10.48550/arxiv.2301.02884, doi = {10.48550/ARXIV.2301.02884}, url = {https://arxiv.org/abs/2301.02884}, author = {Wu, Shangda and Sun, Maosong}, keywords = {Sound (cs.SD), Audio and Speech Processing (eess.AS), FOS: Computer and information sciences, FOS: Computer and information sciences, FOS: Electrical engineering, electronic engineering, information engineering, FOS: Electrical engineering, electronic engineering, information engineering}, title = {TunesFormer: Forming Tunes with Control Codes}, publisher = {arXiv}, year = {2023}, copyright = {Creative Commons Attribution 4.0 International} } ``` | fc9df816fbc5b857c71460b30517c0cd |
apache-2.0 | ['generated_from_trainer'] | false | summarise_v2 This model is a fine-tuned version of [allenai/led-base-16384](https://huggingface.co/allenai/led-base-16384) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.3235 - Rouge2 Precision: 0.018 - Rouge2 Recall: 0.0916 - Rouge2 Fmeasure: 0.0292 | 959d60496a83d2b61654a6fde00edc3c |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure | |:-------------:|:-----:|:----:|:---------------:|:----------------:|:-------------:|:---------------:| | 3.1721 | 0.08 | 10 | 2.7742 | 0.0107 | 0.0671 | 0.0178 | | 3.0802 | 0.16 | 20 | 2.7914 | 0.0111 | 0.0878 | 0.019 | | 3.0795 | 0.24 | 30 | 2.6954 | 0.0094 | 0.076 | 0.0157 | | 2.5806 | 0.32 | 40 | 2.6587 | 0.0028 | 0.0271 | 0.0046 | | 2.6553 | 0.4 | 50 | 2.5958 | 0.0084 | 0.0566 | 0.0143 | | 2.689 | 0.48 | 60 | 2.4857 | 0.0089 | 0.0733 | 0.015 | | 2.6642 | 0.56 | 70 | 2.4205 | 0.0069 | 0.0478 | 0.0116 | | 2.3768 | 0.64 | 80 | 2.3754 | 0.0127 | 0.0795 | 0.0215 | | 2.1949 | 0.72 | 90 | 2.3752 | 0.0155 | 0.1013 | 0.0258 | | 2.3257 | 0.8 | 100 | 2.3509 | 0.0155 | 0.1011 | 0.0261 | | 2.4053 | 0.88 | 110 | 2.3261 | 0.015 | 0.0901 | 0.0246 | | 2.9896 | 0.96 | 120 | 2.3235 | 0.018 | 0.0916 | 0.0292 | | d74b89aab792305e535d3979b3f6af07 |
apache-2.0 | ['translation', 'generated_from_trainer'] | false | marian-finetuned-kde4-en-to-fr This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-it](https://huggingface.co/Helsinki-NLP/opus-mt-en-it) on the kde4 dataset. It achieves the following results on the evaluation set: - eval_loss: 1.2473 - eval_bleu: 41.4902 - eval_runtime: 1405.0341 - eval_samples_per_second: 15.699 - eval_steps_per_second: 0.246 - step: 0 | 4861981d7281e2e5d51ff90505ae94e7 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | mk-walkcycle Dreambooth model trained by spooncats with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Or you can run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb) Sample pictures of this concept:  | 5a183d25d02a7576bfa7667c0a842c70 |
mit | ['text-classification', 'pytorch', 'transformers'] | false | Multi2ConvAI-Corona: finetuned Bert for English
This model was developed in the [Multi2ConvAI](https://multi2conv.ai) project:
- domain: Corona (more details about our use cases: ([en](https://multi2convai/en/blog/use-cases), [de](https://multi2convai/en/blog/use-cases)))
- language: English (en)
- model type: finetuned Bert
| d2c7a625b9cb82071c553a87ecd8f1ab |
mit | ['text-classification', 'pytorch', 'transformers'] | false | Run with Huggingface Transformers
````python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("inovex/multi2convai-logistics-en-bert")
model = AutoModelForSequenceClassification.from_pretrained("inovex/multi2convai-logistics-en-bert")
````
| 4752cb4598c44ddcf134f8b2609e8cfa |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | ka-rina Dreambooth model trained by cdefghijkl with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Or you can run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb) Sample pictures of this concept: | 2a2b89fbbb213fb5f8295d8b7c95e393 |
apache-2.0 | [] | false | Graphcore/gpt2-medium-ipu Optimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on Graphcore’s IPUs - a completely new kind of massively parallel processor to accelerate machine intelligence. Learn more about how to take train Transformer models faster with IPUs at [hf.co/hardware/graphcore](https://huggingface.co/hardware/graphcore). Through HuggingFace Optimum, Graphcore released ready-to-use IPU-trained model checkpoints and IPU configuration files to make it easy to train models with maximum efficiency in the IPU. Optimum shortens the development lifecycle of your AI models by letting you plug-and-play any public dataset and allows a seamless integration to our State-of-the-art hardware giving you a quicker time-to-value for your AI project. | dd7a1cb3fc8d8ce164e119a9141b6736 |
apache-2.0 | [] | false | Model description GPT2 is a large transformer-based language model. It is built using transformer decoder blocks. BERT, on the other hand, uses transformer encoder blocks. It adds Layer normalisation to the input of each sub-block, similar to a pre-activation residual networks and an additional layer normalisation. Paper link : [Language Models are Unsupervised Multitask Learners](https://d4mucfpksywv.cloudfront.net/better-language-models/language-models.pdf) | b4321bf3c011daad61a5f5a7c85ac4d5 |
apache-2.0 | [] | false | Intended uses & limitations This model contains just the `IPUConfig` files for running the [HuggingFace/gpt2-medium](https://huggingface.co/gpt2-medium) model on Graphcore IPUs. **This model contains no model weights, only an IPUConfig.** | fff87196f9047ebf37eaca5c7a42266e |
apache-2.0 | ['zero-shot-classification', 'nli', 'pytorch'] | false | Zero-shot SELECTRA: A zero-shot classifier based on SELECTRA *Zero-shot SELECTRA* is a [SELECTRA model](https://huggingface.co/Recognai/selectra_small) fine-tuned on the Spanish portion of the [XNLI dataset](https://huggingface.co/datasets/xnli). You can use it with Hugging Face's [Zero-shot pipeline](https://huggingface.co/transformers/master/main_classes/pipelines.html | ccedcf3c587c11b08532ebf7d03a3226 |
apache-2.0 | ['zero-shot-classification', 'nli', 'pytorch'] | false | transformers.ZeroShotClassificationPipeline) to make [zero-shot classifications](https://joeddav.github.io/blog/2020/05/29/ZSL.html). In comparison to our previous zero-shot classifier [based on BETO](https://huggingface.co/Recognai/bert-base-spanish-wwm-cased-xnli), zero-shot SELECTRA is **much more lightweight**. As shown in the *Metrics* section, the *small* version (5 times fewer parameters) performs slightly worse, while the *medium* version (3 times fewer parameters) **outperforms** the BETO based zero-shot classifier. | 6976af4baaa671bb06ef0669243d827a |
apache-2.0 | ['zero-shot-classification', 'nli', 'pytorch'] | false | Usage ```python from transformers import pipeline classifier = pipeline("zero-shot-classification", model="Recognai/zeroshot_selectra_medium") classifier( "El autor se perfila, a los 50 años de su muerte, como uno de los grandes de su siglo", candidate_labels=["cultura", "sociedad", "economia", "salud", "deportes"], hypothesis_template="Este ejemplo es {}." ) """Output {'sequence': 'El autor se perfila, a los 50 años de su muerte, como uno de los grandes de su siglo', 'labels': ['sociedad', 'cultura', 'salud', 'economia', 'deportes'], 'scores': [0.3711881935596466, 0.25650349259376526, 0.17355826497077942, 0.1641489565372467, 0.03460107371211052]} """ ``` The `hypothesis_template` parameter is important and should be in Spanish. **In the widget on the right, this parameter is set to its default value: "This example is {}.", so different results are expected.** | 4fa4674d29778507206a9ae567421aad |
apache-2.0 | ['zero-shot-classification', 'nli', 'pytorch'] | false | Metrics | Model | Params | XNLI (acc) | \*MLSUM (acc) | | --- | --- | --- | --- | | [zs BETO](https://huggingface.co/Recognai/bert-base-spanish-wwm-cased-xnli) | 110M | 0.799 | 0.530 | | [zs SELECTRA medium](https://huggingface.co/Recognai/zeroshot_selectra_medium) | 41M | **0.807** | **0.589** | | zs SELECTRA small | **22M** | 0.795 | 0.446 | \*evaluated with zero-shot learning (ZSL) - **XNLI**: The stated accuracy refers to the test portion of the [XNLI dataset](https://huggingface.co/datasets/xnli), after finetuning the model on the training portion. - **MLSUM**: For this accuracy we take the test set of the [MLSUM dataset](https://huggingface.co/datasets/mlsum) and classify the summaries of 5 selected labels. For details, check out our [evaluation notebook](https://github.com/recognai/selectra/blob/main/zero-shot_classifier/evaluation.ipynb) | 7697252457742a23fef3c982554f336b |
apache-2.0 | ['zero-shot-classification', 'nli', 'pytorch'] | false | Authors - David Fidalgo ([GitHub](https://github.com/dcfidalgo)) - Daniel Vila ([GitHub](https://github.com/dvsrepo)) - Francisco Aranda ([GitHub](https://github.com/frascuchon)) - Javier Lopez ([GitHub](https://github.com/javispp)) | 516a899c9abba7470c035def205f1fb9 |
apache-2.0 | ['generated_from_trainer'] | false |  This model is a fine-tuned version of [allenai/led-base-16384](https://huggingface.co/allenai/led-base-16384) on the multi_news dataset. It achieves the following results on the evaluation set: - Loss: 2.3650 - Rouge1 Precision: 0.4673 - Rouge1 Recall: 0.4135 - Rouge1 Fmeasure: 0.4263 - Rouge2 Precision: 0.1579 - Rouge2 Recall: 0.1426 - Rouge2 Fmeasure: 0.1458 - Rougel Precision: 0.2245 - Rougel Recall: 0.2008 - Rougel Fmeasure: 0.2061 - Rougelsum Precision: 0.2245 - Rougelsum Recall: 0.2008 - Rougelsum Fmeasure: 0.2061 | 583cfe83dd8b92a10ad1b5cdc4a40bc5 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 - mixed_precision_training: Native AMP | aa5b933f541001e09fd9c5625a46af06 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 Precision | Rouge1 Recall | Rouge1 Fmeasure | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure | Rougel Precision | Rougel Recall | Rougel Fmeasure | Rougelsum Precision | Rougelsum Recall | Rougelsum Fmeasure | |:-------------:|:-----:|:----:|:---------------:|:----------------:|:-------------:|:---------------:|:----------------:|:-------------:|:---------------:|:----------------:|:-------------:|:---------------:|:-------------------:|:----------------:|:------------------:| | 2.8095 | 0.16 | 10 | 2.5393 | 0.287 | 0.5358 | 0.3674 | 0.1023 | 0.1917 | 0.1311 | 0.1374 | 0.2615 | 0.1771 | 0.1374 | 0.2615 | 0.1771 | | 2.6056 | 0.32 | 20 | 2.4752 | 0.5005 | 0.3264 | 0.3811 | 0.1663 | 0.1054 | 0.1249 | 0.2582 | 0.1667 | 0.1957 | 0.2582 | 0.1667 | 0.1957 | | 2.5943 | 0.48 | 30 | 2.4422 | 0.4615 | 0.3833 | 0.4047 | 0.1473 | 0.1273 | 0.1321 | 0.2242 | 0.1885 | 0.1981 | 0.2242 | 0.1885 | 0.1981 | | 2.4842 | 0.64 | 40 | 2.4186 | 0.4675 | 0.3829 | 0.4081 | 0.1581 | 0.1294 | 0.1384 | 0.2286 | 0.187 | 0.1995 | 0.2286 | 0.187 | 0.1995 | | 2.4454 | 0.8 | 50 | 2.3990 | 0.467 | 0.408 | 0.4222 | 0.1633 | 0.1429 | 0.1477 | 0.2294 | 0.2008 | 0.2076 | 0.2294 | 0.2008 | 0.2076 | | 2.3622 | 0.96 | 60 | 2.3857 | 0.4567 | 0.3898 | 0.41 | 0.1433 | 0.1233 | 0.1295 | 0.2205 | 0.1876 | 0.1976 | 0.2205 | 0.1876 | 0.1976 | | 2.4034 | 1.13 | 70 | 2.3835 | 0.4515 | 0.4304 | 0.4294 | 0.1526 | 0.1479 | 0.1459 | 0.2183 | 0.209 | 0.2078 | 0.2183 | 0.209 | 0.2078 | | 2.2612 | 1.29 | 80 | 2.3804 | 0.455 | 0.4193 | 0.4236 | 0.1518 | 0.1429 | 0.1427 | 0.2177 | 0.2025 | 0.2037 | 0.2177 | 0.2025 | 0.2037 | | 2.2563 | 1.45 | 90 | 2.3768 | 0.4821 | 0.391 | 0.4196 | 0.1652 | 0.1357 | 0.144 | 0.2385 | 0.1929 | 0.2069 | 0.2385 | 0.1929 | 0.2069 | | 2.243 | 1.61 | 100 | 2.3768 | 0.4546 | 0.4093 | 0.4161 | 0.1552 | 0.1402 | 0.1422 | 0.2248 | 0.2016 | 0.2052 | 0.2248 | 0.2016 | 0.2052 | | 2.2505 | 1.77 | 110 | 2.3670 | 0.4625 | 0.4189 | 0.4262 | 0.1606 | 0.1485 | 0.1493 | 0.2301 | 0.2098 | 0.2119 | 0.2301 | 0.2098 | 0.2119 | | 2.2453 | 1.93 | 120 | 2.3650 | 0.4673 | 0.4135 | 0.4263 | 0.1579 | 0.1426 | 0.1458 | 0.2245 | 0.2008 | 0.2061 | 0.2245 | 0.2008 | 0.2061 | | b79fcfb9e0bd6a04d76bdbcec22f73a0 |
mit | ['generated_from_keras_callback'] | false | xenergy/gpt2-indo This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 2.3370 - Validation Loss: 1.8387 - Epoch: 0 | a8eb58da60be2d5b96edd9cba7ec3ef2 |
mit | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 5e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 39182, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000} - training_precision: mixed_float16 | cf62bd1d11aa23930d4378bee1259754 |
apache-2.0 | [] | false | DistilBERT base multilingual model Spanish subset (cased) This model is the Spanish extract of `distilbert-base-multilingual-cased` (https://huggingface.co/distilbert-base-multilingual-cased), a distilled version of the [BERT base multilingual model](bert-base-multilingual-cased). This model is cased: it does make a difference between english and English. It uses the extraction method proposed by Geotrend described in https://github.com/Geotrend-research/smaller-transformers. The resulting model has the same architecture as DistilmBERT: 6 layers, 768 dimension and 12 heads, with a total of **63M parameters** (compared to 134M parameters for DistilmBERT). The goal of this model is to reduce even further the size of the `distilbert-base-multilingual` multilingual model by selecting only most frequent tokens for Spanish, reducing the size of the embedding layer. For more details visit the paper from the Geotrend team: Load What You Need: Smaller Versions of Multilingual BERT. | 94d3507a701ae7975be28fa2793c98fa |
mit | ['generated_from_trainer'] | false | bert-base-german-cased-finetuned-200labels This model is a fine-tuned version of [ogimgio/bert-base-german-cased-finetuned-7labels](https://huggingface.co/ogimgio/bert-base-german-cased-finetuned-7labels) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0744 - Micro f1: 0.0894 - Macro f1: 0.0538 | bc3688ea91084750f1be603eb08229f7 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-06 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - num_epochs: 50 | bb86234d42e8ec360b3e1fbbf41c9d16 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Micro f1 | Macro f1 | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.8041 | 1.0 | 1380 | 0.7312 | 0.0422 | 0.0413 | | 0.605 | 2.0 | 2760 | 0.5440 | 0.0436 | 0.0423 | | 0.4455 | 3.0 | 4140 | 0.4026 | 0.0478 | 0.0449 | | 0.3317 | 4.0 | 5520 | 0.3070 | 0.0574 | 0.0516 | | 0.2553 | 5.0 | 6900 | 0.2432 | 0.0682 | 0.0599 | | 0.2041 | 6.0 | 8280 | 0.1982 | 0.0759 | 0.0657 | | 0.167 | 7.0 | 9660 | 0.1653 | 0.0798 | 0.0677 | | 0.1403 | 8.0 | 11040 | 0.1417 | 0.0839 | 0.0693 | | 0.1222 | 9.0 | 12420 | 0.1249 | 0.0865 | 0.0695 | | 0.109 | 10.0 | 13800 | 0.1132 | 0.0880 | 0.0684 | | 0.0999 | 11.0 | 15180 | 0.1052 | 0.0874 | 0.0661 | | 0.0941 | 12.0 | 16560 | 0.0994 | 0.0878 | 0.0655 | | 0.089 | 13.0 | 17940 | 0.0951 | 0.0876 | 0.0640 | | 0.0854 | 14.0 | 19320 | 0.0917 | 0.0888 | 0.0638 | | 0.0831 | 15.0 | 20700 | 0.0890 | 0.0889 | 0.0626 | | 0.0804 | 16.0 | 22080 | 0.0869 | 0.0890 | 0.0616 | | 0.0788 | 17.0 | 23460 | 0.0851 | 0.0890 | 0.0606 | | 0.077 | 18.0 | 24840 | 0.0835 | 0.0894 | 0.0599 | | 0.0759 | 19.0 | 26220 | 0.0822 | 0.0894 | 0.0593 | | 0.0745 | 20.0 | 27600 | 0.0811 | 0.0896 | 0.0588 | | 0.0735 | 21.0 | 28980 | 0.0800 | 0.0890 | 0.0573 | | 0.0728 | 22.0 | 30360 | 0.0791 | 0.0888 | 0.0564 | | 0.0716 | 23.0 | 31740 | 0.0783 | 0.0895 | 0.0559 | | 0.0709 | 24.0 | 33120 | 0.0775 | 0.0900 | 0.0556 | | 0.0698 | 25.0 | 34500 | 0.0768 | 0.0896 | 0.0550 | | 0.0694 | 26.0 | 35880 | 0.0761 | 0.0897 | 0.0548 | | 0.069 | 27.0 | 37260 | 0.0755 | 0.0892 | 0.0545 | | 0.0684 | 28.0 | 38640 | 0.0750 | 0.0893 | 0.0541 | | 0.0679 | 29.0 | 40020 | 0.0744 | 0.0894 | 0.0538 | | 395419d467d03bceaed0470ff1dcb43c |
apache-2.0 | ['tapex', 'table-question-answering'] | false | TAPEX-large model fine-tuned on WikiSQL. This model was proposed in [TAPEX: Table Pre-training via Learning a Neural SQL Executor](https://arxiv.org/abs/2107.07653) by Qian Liu, Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weizhu Chen, Jian-Guang Lou. Original repo can be found [here](https://github.com/microsoft/Table-Pretraining). To load it and run inference, you can do the following: ``` from transformers import BartTokenizer, BartForConditionalGeneration import pandas as pd tokenizer = BartTokenizer.from_pretrained("nielsr/tapex-large-finetuned-wikisql") model = BartForConditionalGeneration.from_pretrained("nielsr/tapex-large-finetuned-wikisql") | dbeeae73b0ecd3bb987f1a75da5d08e1 |
apache-2.0 | ['tapex', 'table-question-answering'] | false | define the linearizer based on this code: https://github.com/microsoft/Table-Pretraining/blob/main/tapex/processor/table_linearize.py linearizer = IndexedRowTableLinearize() linear_table = linearizer.process_table(table_dict) | 7f17565bf056a092b4a3c20118959320 |
apache-2.0 | ['translation'] | false | opus-mt-en-ti * source languages: en * target languages: ti * OPUS readme: [en-ti](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-ti/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/en-ti/opus-2020-01-08.zip) * test set translations: [opus-2020-01-08.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/en-ti/opus-2020-01-08.test.txt) * test set scores: [opus-2020-01-08.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/en-ti/opus-2020-01-08.eval.txt) | baf14978f443d3c922d5764339d59d89 |
apache-2.0 | ['generated_from_keras_callback'] | false | Imene/vit-base-patch16-224-wi2 This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.3098 - Train Accuracy: 0.9821 - Train Top-5-accuracy: 0.9971 - Validation Loss: 3.0737 - Validation Accuracy: 0.2491 - Validation Top-5-accuracy: 0.4476 - Epoch: 9 | 0db9cca5a50850ef67e4a08a817530a2 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 0.0003, 'decay_steps': 1750, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.001}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000} - training_precision: mixed_float16 | f641c81ee013a5d760d2ad5f12841050 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Train Accuracy | Train Top-5-accuracy | Validation Loss | Validation Accuracy | Validation Top-5-accuracy | Epoch | |:----------:|:--------------:|:--------------------:|:---------------:|:-------------------:|:-------------------------:|:-----:| | 4.4859 | 0.0195 | 0.0579 | 4.2995 | 0.0368 | 0.0865 | 0 | | 4.1729 | 0.0355 | 0.0987 | 4.0916 | 0.0472 | 0.1266 | 1 | | 3.9541 | 0.0666 | 0.1641 | 3.8050 | 0.0781 | 0.2035 | 2 | | 3.5823 | 0.1247 | 0.2615 | 3.4015 | 0.1429 | 0.2950 | 3 | | 3.0156 | 0.1913 | 0.3987 | 3.0598 | 0.1880 | 0.3916 | 4 | | 2.4618 | 0.3077 | 0.5572 | 2.9869 | 0.2056 | 0.4129 | 5 | | 1.8979 | 0.4541 | 0.7165 | 2.9507 | 0.2298 | 0.4425 | 6 | | 1.2075 | 0.6914 | 0.8886 | 3.0106 | 0.2394 | 0.4425 | 7 | | 0.6026 | 0.9097 | 0.9810 | 3.0739 | 0.2428 | 0.4413 | 8 | | 0.3098 | 0.9821 | 0.9971 | 3.0737 | 0.2491 | 0.4476 | 9 | | 326dd20a79b70b132a6f969444539c90 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Large v2 Italian This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the mozilla-foundation/common_voice_11_0 it dataset. It achieves the following results on the evaluation set: - Loss: 0.1332 - Wer: 4.5576 | 3cdaebb28f1682ebf60252e5bb190e4f |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 16 - seed: 42 - distributed_type: multi-GPU - gradient_accumulation_steps: 2 - 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: 500 - training_steps: 6000 | 2ef0c3b1896ffc951e4d2e4f12149eb3 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.1684 | 0.17 | 1000 | 0.1620 | 6.4620 | | 0.1174 | 0.33 | 2000 | 0.1418 | 5.5663 | | 0.069 | 1.1 | 3000 | 0.1400 | 5.2865 | | 0.0649 | 1.27 | 4000 | 0.1315 | 4.8932 | | 0.0334 | 2.04 | 5000 | 0.1368 | 4.6845 | | 0.037 | 2.21 | 6000 | 0.1332 | 4.5576 | | e6437d3e71087e0df5bdd2ab9d2aad17 |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-uncased.CEBaB_confounding.price_food_ambiance_negative.absa.5-class.seed_44 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. | 9b6e9285cfcdc8169a9a67492e7499a3 |
apache-2.0 | ['generated_from_trainer'] | false | amazon_sentiment_sample_of_1900_with_summary This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1062 - Accuracy: 0.9581 - F1: 0.9579 | 2343cd1c226b692e83887b3ca837e87b |
apache-2.0 | ['t5', 'text2text-generation', 'seq2seq'] | false | Description [megagonlabs/t5-base-japanese-web](https://huggingface.co/megagonlabs/t5-base-japanese-web) is a T5 (Text-to-Text Transfer Transformer) model pre-trained on Japanese web texts. Training codes are [available on GitHub](https://github.com/megagonlabs/t5-japanese). The vocabulary size of this model is 32K. [8K version is also available](https://huggingface.co/megagonlabs/t5-base-japanese-web-8k). | 927df8a0a2d3aa336da9fb6c705383a1 |
apache-2.0 | ['t5', 'text2text-generation', 'seq2seq'] | false | Corpora We used following corpora for pre-training. - Japanese in [mC4/3.0.1](https://huggingface.co/datasets/mc4) (We used [Tensorflow native format](https://github.com/allenai/allennlp/discussions/5056)) - 87,425,304 pages - 782 GB in TFRecord format - [Japanese](https://www.tensorflow.org/datasets/catalog/wiki40b | b9cedd1813dacd85a21cb0bbb5a2e39c |
apache-2.0 | ['t5', 'text2text-generation', 'seq2seq'] | false | Tokenizer We used Japanese Wikipedia to train [SentencePiece](https://github.com/google/sentencepiece). - Vocabulary size: 32,000 - [Byte-fallback](https://github.com/google/sentencepiece/releases/tag/v0.1.9): Enabled | 800eec45d1cad2077606ab5f34c9157d |
apache-2.0 | ['t5', 'text2text-generation', 'seq2seq'] | false | Parameters - T5 model: [models/t5.1.1.base.gin](https://github.com/google-research/text-to-text-transfer-transformer/blob/main/t5/models/gin/models/t5.1.1.base.gin) - Training steps: 1,000,000 It took about 126 hours with TPU v3-8 | 096e5e8b9330f430fa914a6d15eff080 |
apache-2.0 | ['t5', 'text2text-generation', 'seq2seq'] | false | Related models - [日本語T5事前学習済みモデル (sonoisa/t5-base-japanese)](https://huggingface.co/sonoisa/t5-base-japanese) - [日本語T5事前学習済みモデル (sonoisa/t5-base-japanese-mC4-Wikipedia)](https://huggingface.co/sonoisa/t5-base-japanese-mC4-Wikipedia) | d9d2cc4029519bb199d67087bf43f676 |
apache-2.0 | ['t5', 'text2text-generation', 'seq2seq'] | false | Citations - mC4 Contains information from `mC4` which is made available under the [ODC Attribution License](https://opendatacommons.org/licenses/by/1-0/). ```bibtex @article{2019t5, author = {Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu}, title = {Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer}, journal = {arXiv e-prints}, year = {2019}, archivePrefix = {arXiv}, eprint = {1910.10683}, } ``` - wiki40b ```bibtex @inproceedings{49029, title = {Wiki-40B: Multilingual Language Model Dataset}, author = {Mandy Guo and Zihang Dai and Denny Vrandecic and Rami Al-Rfou}, year = {2020}, booktitle = {LREC 2020} } ``` | 522184b88ecaaf474b36820d590d44cf |
mit | ['generated_from_keras_callback'] | false | roberta-base-finetuned-unlabeled_all This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 2.3581 - Validation Loss: 2.1388 - Epoch: 0 | 4e790851d667b143fd86e58dc26a655b |
mit | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 4659, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 | 62debe2b91af3186d1a518d9d6d7e67f |
apache-2.0 | ['translation'] | false | opus-mt-ts-es * source languages: ts * target languages: es * OPUS readme: [ts-es](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/ts-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/ts-es/opus-2020-01-16.zip) * test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/ts-es/opus-2020-01-16.test.txt) * test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/ts-es/opus-2020-01-16.eval.txt) | ca4d1c50ffedb69c1eb73ec5e40c8798 |
unknown | [] | false | Ella lo dejó como lo dejaban todas, en defensa propia, la dependencia emocional que la había sujetado tantas veces, finalmente se vio superada por su instinto de supervivencia. - Estes una puta, me quieres dejar por ese que te follas cuando discutimos. - Amorcito, yo no estoy con nadie más que contigo y no puedes tratarme así, sólo yo entiendo que lo haces por tu trastorno. - Yo no tengo ningún trastorno, eres tú la que me enfermas con tu indecisión, ya sabes que eres el amor de mi vida y que haría cualquier cosa, cualquier cosa, por no perderte, te aviso. | 1fd11fad20b412448aaadf50e4905181 |
apache-2.0 | ['translation'] | false | opus-mt-nso-sv * source languages: nso * target languages: sv * OPUS readme: [nso-sv](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/nso-sv/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/nso-sv/opus-2020-01-16.zip) * test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/nso-sv/opus-2020-01-16.test.txt) * test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/nso-sv/opus-2020-01-16.eval.txt) | 43e0d56edf77bc9de5e789b3dc148992 |
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