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ftopal
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huggingface-models-embeddings

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spacy
<a href="https://github.com/centre-for-humanities-computing/Dacy"><img src="https://centre-for-humanities-computing.github.io/DaCy/_static/icon.png" width="175" height="175" align="right" /></a> # DaCy large DaCy is a Danish language processing framework with state-of-the-art pipelines as well as functionality for analysing Danish pipelines. DaCy's largest pipeline has achieved State-of-the-Art performance on parts-of-speech tagging and dependency parsing for Danish on the Danish Dependency treebank as well as competitive performance on named entity recognition, named entity disambiguation and coreference resolution. To read more check out the [DaCy repository](https://github.com/centre-for-humanities-computing/DaCy) for material on how to use DaCy and reproduce the results. DaCy also contains guides on usage of the package as well as behavioural test for biases and robustness of Danish NLP pipelines. | Feature | Description | | --- | --- | | **Name** | `da_dacy_large_trf` | | **Version** | `0.2.0` | | **spaCy** | `>=3.5.2,<3.6.0` | | **Default Pipeline** | `transformer`, `tagger`, `morphologizer`, `trainable_lemmatizer`, `parser`, `ner`, `coref`, `span_resolver`, `span_cleaner`, `entity_linker` | | **Components** | `transformer`, `tagger`, `morphologizer`, `trainable_lemmatizer`, `parser`, `ner`, `coref`, `span_resolver`, `span_cleaner`, `entity_linker` | | **Vectors** | 0 keys, 0 unique vectors (0 dimensions) | | **Sources** | [UD Danish DDT v2.11](https://github.com/UniversalDependencies/UD_Danish-DDT) (Johannsen, Anders; Martínez Alonso, Héctor; Plank, Barbara)<br />[DaNE](https://huggingface.co/datasets/dane) (Rasmus Hvingelby, Amalie B. Pauli, Maria Barrett, Christina Rosted, Lasse M. Lidegaard, Anders Søgaard)<br />[DaCoref](https://huggingface.co/datasets/alexandrainst/dacoref) (Buch-Kromann, Matthias)<br />[DaNED](https://danlp-alexandra.readthedocs.io/en/stable/docs/datasets.html#daned) (Barrett, M. J., Lam, H., Wu, M., Lacroix, O., Plank, B., & Søgaard, A.)<br />[chcaa/dfm-encoder-large-v1](https://huggingface.co/chcaa/dfm-encoder-large-v1) (The Danish Foundation Models team) | | **License** | `Apache-2.0` | | **Author** | [Kenneth Enevoldsen](https://chcaa.io/#/) | ### Label Scheme <details> <summary>View label scheme (211 labels for 4 components)</summary> | Component | Labels | | --- | --- | | **`tagger`** | `ADJ`, `ADP`, `ADV`, `AUX`, `CCONJ`, `DET`, `INTJ`, `NOUN`, `NUM`, `PART`, `PRON`, `PROPN`, `PUNCT`, `SCONJ`, `SYM`, `VERB`, `X` | | **`morphologizer`** | `AdpType=Prep\|POS=ADP`, `Definite=Ind\|Gender=Com\|Number=Sing\|POS=NOUN`, `Mood=Ind\|POS=AUX\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `POS=PROPN`, `Definite=Ind\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Definite=Def\|Gender=Neut\|Number=Sing\|POS=NOUN`, `POS=SCONJ`, `Definite=Def\|Gender=Com\|Number=Sing\|POS=NOUN`, `Mood=Ind\|POS=VERB\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `POS=ADV`, `Number=Plur\|POS=DET\|PronType=Dem`, `Degree=Pos\|Number=Plur\|POS=ADJ`, `Definite=Ind\|Gender=Com\|Number=Plur\|POS=NOUN`, `POS=PUNCT`, `NumType=Ord\|POS=ADJ`, `POS=CCONJ`, `Definite=Ind\|Gender=Neut\|Number=Plur\|POS=NOUN`, `POS=VERB\|VerbForm=Inf\|Voice=Act`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Degree=Sup\|POS=ADV`, `Degree=Pos\|POS=ADV`, `Gender=Com\|Number=Sing\|POS=DET\|PronType=Ind`, `Number=Plur\|POS=DET\|PronType=Ind`, `POS=ADP`, `POS=ADV\|PartType=Inf`, `Case=Nom\|Gender=Com\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Mood=Ind\|POS=AUX\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Definite=Def\|Degree=Pos\|Number=Sing\|POS=ADJ`, `Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Mood=Ind\|POS=VERB\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `POS=ADP\|PartType=Inf`, `Definite=Ind\|Degree=Pos\|Gender=Com\|Number=Sing\|POS=ADJ`, `NumType=Card\|POS=NUM`, `Degree=Pos\|POS=ADJ`, `Definite=Ind\|Number=Sing\|POS=AUX\|Tense=Past\|VerbForm=Part`, `POS=PART\|PartType=Inf`, `Case=Acc\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Definite=Def\|Gender=Com\|Number=Plur\|POS=NOUN`, `Definite=Ind\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `POS=VERB\|Tense=Pres\|VerbForm=Part`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Gen\|Definite=Def\|Gender=Com\|Number=Sing\|POS=NOUN`, `Definite=Def\|Degree=Sup\|Number=Plur\|POS=ADJ`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `POS=AUX\|VerbForm=Inf\|Voice=Act`, `Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Definite=Ind\|Degree=Cmp\|Number=Sing\|POS=ADJ`, `Degree=Cmp\|POS=ADJ`, `POS=PRON\|PartType=Inf`, `Definite=Ind\|Degree=Pos\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Com\|POS=PRON\|PronType=Ind`, `Number=Plur\|POS=PRON\|PronType=Ind`, `POS=INTJ`, `Gender=Com\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Gen\|Number=Plur\|POS=DET\|PronType=Ind`, `Mood=Ind\|POS=VERB\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Definite=Def\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Degree=Cmp\|POS=ADV`, `Number=Plur\|Number[psor]=Plur\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs\|Style=Form`, `Case=Acc\|Gender=Com\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Gen\|POS=PROPN`, `Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Ind`, `Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Gender=Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Acc\|Gender=Com\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Definite=Def\|Degree=Sup\|POS=ADJ`, `Gender=Neut\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Definite=Def\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part`, `POS=PRON\|PronType=Dem`, `Degree=Pos\|Gender=Com\|Number=Sing\|POS=ADJ`, `Number=Plur\|POS=NUM`, `POS=VERB\|VerbForm=Inf\|Voice=Pass`, `Definite=Def\|Degree=Sup\|Number=Sing\|POS=ADJ`, `Number=Sing\|POS=PRON\|PronType=Int,Rel`, `Case=Nom\|Gender=Com\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Gender=Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Gender=Com\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `POS=PRON`, `Definite=Ind\|Number=Sing\|POS=NOUN`, `Definite=Ind\|Number=Sing\|POS=NUM`, `Case=Gen\|Definite=Ind\|Gender=Com\|Number=Sing\|POS=NOUN`, `Foreign=Yes\|POS=ADV`, `POS=NOUN`, `Case=Gen\|Definite=Def\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Gender=Com\|Number=Plur\|POS=NOUN`, `Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Int,Rel`, `Case=Nom\|Gender=Com\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Gender=Com\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Gen\|Definite=Ind\|Gender=Com\|Number=Plur\|POS=NOUN`, `Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Degree=Sup\|POS=ADJ`, `Degree=Pos\|Number=Sing\|POS=ADJ`, `Mood=Imp\|POS=VERB`, `Case=Nom\|Gender=Com\|POS=PRON\|Person=2\|Polite=Form\|PronType=Prs`, `Case=Acc\|Gender=Com\|POS=PRON\|Person=2\|Polite=Form\|PronType=Prs`, `POS=X`, `Case=Gen\|Definite=Def\|Gender=Com\|Number=Plur\|POS=NOUN`, `Number=Plur\|POS=PRON\|PronType=Dem`, `Case=Acc\|Gender=Com\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Number=Plur\|POS=PRON\|PronType=Int,Rel`, `Gender=Com\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Degree=Cmp\|Number=Plur\|POS=ADJ`, `Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Gender=Com\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs\|Style=Form`, `Case=Nom\|Gender=Com\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Acc\|Gender=Com\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Gender=Com\|POS=PRON\|PronType=Int,Rel`, `Case=Gen\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Gender=Neut\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `POS=VERB\|VerbForm=Ger`, `Gender=Com\|Number=Sing\|POS=PRON\|PronType=Dem`, `Case=Gen\|POS=PRON\|PronType=Int,Rel`, `Mood=Ind\|POS=VERB\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Abbr=Yes\|POS=X`, `Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Gender=Com\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Definite=Ind\|Number=Plur\|POS=NOUN`, `Foreign=Yes\|POS=X`, `Number=Plur\|POS=PRON\|PronType=Rcp`, `Case=Nom\|Gender=Com\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Gen\|Degree=Cmp\|POS=ADJ`, `Case=Gen\|Definite=Def\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Acc\|Gender=Com\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Dem`, `Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs\|Style=Form`, `Gender=Neut\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs\|Style=Form`, `Number=Plur\|Number[psor]=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Number[psor]=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Number=Plur\|POS=PRON\|PronType=Rcp`, `POS=DET\|Person=2\|Polite=Form\|Poss=Yes\|PronType=Prs`, `POS=SYM`, `POS=DET\|PronType=Dem`, `Gender=Com\|Number=Sing\|POS=NUM`, `Number[psor]=Plur\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Definite=Def\|Degree=Abs\|POS=ADJ`, `POS=VERB\|Tense=Pres`, `Definite=Ind\|Gender=Neut\|Number=Sing\|POS=NUM`, `Degree=Abs\|POS=ADV`, `Case=Gen\|Definite=Def\|Degree=Pos\|Number=Sing\|POS=ADJ`, `Gender=Com\|Number=Sing\|POS=PRON\|PronType=Int,Rel`, `POS=VERB\|Tense=Past\|VerbForm=Part`, `Definite=Ind\|Degree=Sup\|Number=Sing\|POS=ADJ`, `Gender=Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Gender=Com\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs`, `Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Number[psor]=Plur\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Definite=Ind\|POS=NOUN`, `Case=Gen\|Gender=Com\|Number=Sing\|POS=DET\|PronType=Ind`, `Definite=Ind\|Gender=Com\|Number=Sing\|POS=NUM`, `Definite=Def\|Number=Plur\|POS=NOUN`, `Case=Gen\|POS=NOUN`, `POS=AUX\|Tense=Pres\|VerbForm=Part` | | **`parser`** | `ROOT`, `acl:relcl`, `advcl`, `advmod`, `advmod:lmod`, `amod`, `appos`, `aux`, `case`, `cc`, `ccomp`, `compound:prt`, `conj`, `cop`, `dep`, `det`, `expl`, `fixed`, `flat`, `iobj`, `list`, `mark`, `nmod`, `nmod:poss`, `nsubj`, `nummod`, `obj`, `obl`, `obl:lmod`, `obl:tmod`, `punct`, `xcomp` | | **`ner`** | `LOC`, `MISC`, `ORG`, `PER` | </details> ### Accuracy | Type | Score | | --- | --- | | `TOKEN_ACC` | 99.92 | | `TOKEN_P` | 99.70 | | `TOKEN_R` | 99.77 | | `TOKEN_F` | 99.74 | | `SENTS_P` | 100.00 | | `SENTS_R` | 100.00 | | `SENTS_F` | 100.00 | | `TAG_ACC` | 99.14 | | `POS_ACC` | 99.08 | | `MORPH_ACC` | 98.80 | | `MORPH_MICRO_P` | 99.45 | | `MORPH_MICRO_R` | 99.32 | | `MORPH_MICRO_F` | 99.39 | | `DEP_UAS` | 92.81 | | `DEP_LAS` | 90.80 | | `ENTS_P` | 88.58 | | `ENTS_R` | 86.20 | | `ENTS_F` | 87.38 | | `LEMMA_ACC` | 95.89 | | `COREF_LEA_F1` | 46.72 | | `COREF_LEA_PRECISION` | 45.91 | | `COREF_LEA_RECALL` | 47.56 | | `NEL_SCORE` | 34.29 | | `NEL_MICRO_P` | 84.00 | | `NEL_MICRO_R` | 21.54 | | `NEL_MICRO_F` | 34.29 | | `NEL_MACRO_P` | 86.71 | | `NEL_MACRO_R` | 24.70 | | `NEL_MACRO_F` | 37.28 | ### Training This model was trained using [spaCy](https://spacy.io) and logged to [Weights & Biases](https://wandb.ai/kenevoldsen/dacy-v0.2.0). You can find all the training logs [here](https://wandb.ai/kenevoldsen/dacy-v0.2.0).
{"language": ["da"], "license": "apache-2.0", "library_name": "spacy", "tags": ["spacy", "dacy", "danish", "token-classification", "pos tagging", "morphological analysis", "lemmatization", "dependency parsing", "named entity recognition", "coreference resolution", "named entity linking", "named entity disambiguation"], "datasets": ["universal_dependencies", "dane", "alexandrainst/dacoref"], "metrics": ["accuracy"], "model-index": [{"name": "da_dacy_large_trf-0.2.0", "results": [{"task": {"type": "token-classification", "name": "NER"}, "dataset": {"name": "DaNE", "type": "dane", "split": "test"}, "metrics": [{"type": "precision", "value": 0.8858195212, "name": "NER Precision"}, {"type": "recall", "value": 0.8620071685, "name": "NER Recall"}, {"type": "f_score", "value": 0.8737511353, "name": "NER F Score"}]}, {"task": {"type": "token-classification", "name": "TAG"}, "dataset": {"name": "UD Danish DDT", "type": "universal_dependencies", "config": "da_ddt", "split": "test"}, "metrics": [{"type": "accuracy", "value": 0.9913668347, "name": "TAG (XPOS) Accuracy"}, {"type": "accuracy", "value": 0.9908174469, "name": "POS (UPOS) Accuracy"}, {"type": "accuracy", "value": 0.9880227568, "name": "Morph (UFeats) Accuracy"}, {"type": "accuracy", "value": 0.9589423796, "name": "Lemma Accuracy"}, {"type": "f_score", "value": 0.9280885781, "name": "Unlabeled Attachment Score (UAS)"}, {"type": "f_score", "value": 0.9079997669, "name": "Labeled Attachment Score (LAS)"}, {"type": "f_score", "value": 1.0, "name": "Sentences F-Score"}]}, {"task": {"type": "coreference-resolution", "name": "coreference-resolution"}, "dataset": {"name": "DaCoref", "type": "alexandrainst/dacoref", "split": "custom"}, "metrics": [{"type": "f_score", "value": 0.4672143289, "name": "LEA"}]}, {"task": {"type": "coreference-resolution", "name": "coreference-resolution"}, "dataset": {"name": "DaNED", "type": "named-entity-linking", "split": "custom"}, "metrics": [{"type": "precision", "value": 0.84, "name": "Named entity Linking Precision"}, {"type": "recall", "value": 0.2153846154, "name": "Named entity Linking Recall"}, {"type": "f_score", "value": 0.3428571429, "name": "Named entity Linking F Score"}]}]}]}
token-classification
chcaa/da_dacy_large_trf
[ "spacy", "dacy", "danish", "token-classification", "pos tagging", "morphological analysis", "lemmatization", "dependency parsing", "named entity recognition", "coreference resolution", "named entity linking", "named entity disambiguation", "da", "dataset:universal_dependencies", "dataset:dane", "dataset:alexandrainst/dacoref", "license:apache-2.0", "model-index", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[ "da" ]
TAGS #spacy #dacy #danish #token-classification #pos tagging #morphological analysis #lemmatization #dependency parsing #named entity recognition #coreference resolution #named entity linking #named entity disambiguation #da #dataset-universal_dependencies #dataset-dane #dataset-alexandrainst/dacoref #license-apache-2.0 #model-index #region-us
<a href="URL src="URL width="175" height="175" align="right" /> DaCy large ========== DaCy is a Danish language processing framework with state-of-the-art pipelines as well as functionality for analysing Danish pipelines. DaCy's largest pipeline has achieved State-of-the-Art performance on parts-of-speech tagging and dependency parsing for Danish on the Danish Dependency treebank as well as competitive performance on named entity recognition, named entity disambiguation and coreference resolution. To read more check out the DaCy repository for material on how to use DaCy and reproduce the results. DaCy also contains guides on usage of the package as well as behavioural test for biases and robustness of Danish NLP pipelines. ### Label Scheme View label scheme (211 labels for 4 components) ### Accuracy ### Training This model was trained using spaCy and logged to Weights & Biases. You can find all the training logs here.
[ "### Label Scheme\n\n\n\nView label scheme (211 labels for 4 components)", "### Accuracy", "### Training\n\n\nThis model was trained using spaCy and logged to Weights & Biases. You can find all the training logs here." ]
[ "TAGS\n#spacy #dacy #danish #token-classification #pos tagging #morphological analysis #lemmatization #dependency parsing #named entity recognition #coreference resolution #named entity linking #named entity disambiguation #da #dataset-universal_dependencies #dataset-dane #dataset-alexandrainst/dacoref #license-apache-2.0 #model-index #region-us \n", "### Label Scheme\n\n\n\nView label scheme (211 labels for 4 components)", "### Accuracy", "### Training\n\n\nThis model was trained using spaCy and logged to Weights & Biases. You can find all the training logs here." ]
[ 107, 17, 5, 33 ]
[ "passage: TAGS\n#spacy #dacy #danish #token-classification #pos tagging #morphological analysis #lemmatization #dependency parsing #named entity recognition #coreference resolution #named entity linking #named entity disambiguation #da #dataset-universal_dependencies #dataset-dane #dataset-alexandrainst/dacoref #license-apache-2.0 #model-index #region-us \n### Label Scheme\n\n\n\nView label scheme (211 labels for 4 components)### Accuracy### Training\n\n\nThis model was trained using spaCy and logged to Weights & Biases. You can find all the training logs here." ]
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spacy
<a href="https://github.com/centre-for-humanities-computing/Dacy"><img src="https://centre-for-humanities-computing.github.io/DaCy/_static/icon.png" width="175" height="175" align="right" /></a> # DaCy medium DaCy is a Danish language processing framework with state-of-the-art pipelines as well as functionality for analysing Danish pipelines. DaCy's largest pipeline has achieved State-of-the-Art performance on parts-of-speech tagging and dependency parsing for Danish on the Danish Dependency treebank as well as competitive performance on named entity recognition, named entity disambiguation and coreference resolution. To read more check out the [DaCy repository](https://github.com/centre-for-humanities-computing/DaCy) for material on how to use DaCy and reproduce the results. DaCy also contains guides on usage of the package as well as behavioural test for biases and robustness of Danish NLP pipelines. | Feature | Description | | --- | --- | | **Name** | `da_dacy_medium_trf` | | **Version** | `0.2.0` | | **spaCy** | `>=3.5.2,<3.6.0` | | **Default Pipeline** | `transformer`, `tagger`, `morphologizer`, `trainable_lemmatizer`, `parser`, `ner`, `coref`, `span_resolver`, `span_cleaner`, `entity_linker` | | **Components** | `transformer`, `tagger`, `morphologizer`, `trainable_lemmatizer`, `parser`, `ner`, `coref`, `span_resolver`, `span_cleaner`, `entity_linker` | | **Vectors** | 0 keys, 0 unique vectors (0 dimensions) | | **Sources** | [UD Danish DDT v2.11](https://github.com/UniversalDependencies/UD_Danish-DDT) (Johannsen, Anders; Martínez Alonso, Héctor; Plank, Barbara)<br />[DaNE](https://huggingface.co/datasets/dane) (Rasmus Hvingelby, Amalie B. Pauli, Maria Barrett, Christina Rosted, Lasse M. Lidegaard, Anders Søgaard)<br />[DaCoref](https://huggingface.co/datasets/alexandrainst/dacoref) (Buch-Kromann, Matthias)<br />[DaNED](https://danlp-alexandra.readthedocs.io/en/stable/docs/datasets.html#daned) (Barrett, M. J., Lam, H., Wu, M., Lacroix, O., Plank, B., & Søgaard, A.)<br />[vesteinn/DanskBERT](https://huggingface.co/vesteinn/DanskBERT) (Vésteinn Snæbjarnarson) | | **License** | `Apache-2.0` | | **Author** | [Kenneth Enevoldsen](https://chcaa.io/#/) | ### Label Scheme <details> <summary>View label scheme (211 labels for 4 components)</summary> | Component | Labels | | --- | --- | | **`tagger`** | `ADJ`, `ADP`, `ADV`, `AUX`, `CCONJ`, `DET`, `INTJ`, `NOUN`, `NUM`, `PART`, `PRON`, `PROPN`, `PUNCT`, `SCONJ`, `SYM`, `VERB`, `X` | | **`morphologizer`** | `AdpType=Prep\|POS=ADP`, `Definite=Ind\|Gender=Com\|Number=Sing\|POS=NOUN`, `Mood=Ind\|POS=AUX\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `POS=PROPN`, `Definite=Ind\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Definite=Def\|Gender=Neut\|Number=Sing\|POS=NOUN`, `POS=SCONJ`, `Definite=Def\|Gender=Com\|Number=Sing\|POS=NOUN`, `Mood=Ind\|POS=VERB\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `POS=ADV`, `Number=Plur\|POS=DET\|PronType=Dem`, `Degree=Pos\|Number=Plur\|POS=ADJ`, `Definite=Ind\|Gender=Com\|Number=Plur\|POS=NOUN`, `POS=PUNCT`, `NumType=Ord\|POS=ADJ`, `POS=CCONJ`, `Definite=Ind\|Gender=Neut\|Number=Plur\|POS=NOUN`, `POS=VERB\|VerbForm=Inf\|Voice=Act`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Degree=Sup\|POS=ADV`, `Degree=Pos\|POS=ADV`, `Gender=Com\|Number=Sing\|POS=DET\|PronType=Ind`, `Number=Plur\|POS=DET\|PronType=Ind`, `POS=ADP`, `POS=ADV\|PartType=Inf`, `Case=Nom\|Gender=Com\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Mood=Ind\|POS=AUX\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Definite=Def\|Degree=Pos\|Number=Sing\|POS=ADJ`, `Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Mood=Ind\|POS=VERB\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `POS=ADP\|PartType=Inf`, `Definite=Ind\|Degree=Pos\|Gender=Com\|Number=Sing\|POS=ADJ`, `NumType=Card\|POS=NUM`, `Degree=Pos\|POS=ADJ`, `Definite=Ind\|Number=Sing\|POS=AUX\|Tense=Past\|VerbForm=Part`, `POS=PART\|PartType=Inf`, `Case=Acc\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Definite=Def\|Gender=Com\|Number=Plur\|POS=NOUN`, `Definite=Ind\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `POS=VERB\|Tense=Pres\|VerbForm=Part`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Gen\|Definite=Def\|Gender=Com\|Number=Sing\|POS=NOUN`, `Definite=Def\|Degree=Sup\|Number=Plur\|POS=ADJ`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `POS=AUX\|VerbForm=Inf\|Voice=Act`, `Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Definite=Ind\|Degree=Cmp\|Number=Sing\|POS=ADJ`, `Degree=Cmp\|POS=ADJ`, `POS=PRON\|PartType=Inf`, `Definite=Ind\|Degree=Pos\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Com\|POS=PRON\|PronType=Ind`, `Number=Plur\|POS=PRON\|PronType=Ind`, `POS=INTJ`, `Gender=Com\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Gen\|Number=Plur\|POS=DET\|PronType=Ind`, `Mood=Ind\|POS=VERB\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Definite=Def\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Degree=Cmp\|POS=ADV`, `Number=Plur\|Number[psor]=Plur\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs\|Style=Form`, `Case=Acc\|Gender=Com\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Gen\|POS=PROPN`, `Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Ind`, `Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Gender=Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Acc\|Gender=Com\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Definite=Def\|Degree=Sup\|POS=ADJ`, `Gender=Neut\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Definite=Def\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part`, `POS=PRON\|PronType=Dem`, `Degree=Pos\|Gender=Com\|Number=Sing\|POS=ADJ`, `Number=Plur\|POS=NUM`, `POS=VERB\|VerbForm=Inf\|Voice=Pass`, `Definite=Def\|Degree=Sup\|Number=Sing\|POS=ADJ`, `Number=Sing\|POS=PRON\|PronType=Int,Rel`, `Case=Nom\|Gender=Com\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Gender=Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Gender=Com\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `POS=PRON`, `Definite=Ind\|Number=Sing\|POS=NOUN`, `Definite=Ind\|Number=Sing\|POS=NUM`, `Case=Gen\|Definite=Ind\|Gender=Com\|Number=Sing\|POS=NOUN`, `Foreign=Yes\|POS=ADV`, `POS=NOUN`, `Case=Gen\|Definite=Def\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Gender=Com\|Number=Plur\|POS=NOUN`, `Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Int,Rel`, `Case=Nom\|Gender=Com\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Gender=Com\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Gen\|Definite=Ind\|Gender=Com\|Number=Plur\|POS=NOUN`, `Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Degree=Sup\|POS=ADJ`, `Degree=Pos\|Number=Sing\|POS=ADJ`, `Mood=Imp\|POS=VERB`, `Case=Nom\|Gender=Com\|POS=PRON\|Person=2\|Polite=Form\|PronType=Prs`, `Case=Acc\|Gender=Com\|POS=PRON\|Person=2\|Polite=Form\|PronType=Prs`, `POS=X`, `Case=Gen\|Definite=Def\|Gender=Com\|Number=Plur\|POS=NOUN`, `Number=Plur\|POS=PRON\|PronType=Dem`, `Case=Acc\|Gender=Com\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Number=Plur\|POS=PRON\|PronType=Int,Rel`, `Gender=Com\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Degree=Cmp\|Number=Plur\|POS=ADJ`, `Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Gender=Com\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs\|Style=Form`, `Case=Nom\|Gender=Com\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Acc\|Gender=Com\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Gender=Com\|POS=PRON\|PronType=Int,Rel`, `Case=Gen\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Gender=Neut\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `POS=VERB\|VerbForm=Ger`, `Gender=Com\|Number=Sing\|POS=PRON\|PronType=Dem`, `Case=Gen\|POS=PRON\|PronType=Int,Rel`, `Mood=Ind\|POS=VERB\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Abbr=Yes\|POS=X`, `Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Gender=Com\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Definite=Ind\|Number=Plur\|POS=NOUN`, `Foreign=Yes\|POS=X`, `Number=Plur\|POS=PRON\|PronType=Rcp`, `Case=Nom\|Gender=Com\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Gen\|Degree=Cmp\|POS=ADJ`, `Case=Gen\|Definite=Def\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Acc\|Gender=Com\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Dem`, `Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs\|Style=Form`, `Gender=Neut\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs\|Style=Form`, `Number=Plur\|Number[psor]=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Number[psor]=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Number=Plur\|POS=PRON\|PronType=Rcp`, `POS=DET\|Person=2\|Polite=Form\|Poss=Yes\|PronType=Prs`, `POS=SYM`, `POS=DET\|PronType=Dem`, `Gender=Com\|Number=Sing\|POS=NUM`, `Number[psor]=Plur\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Definite=Def\|Degree=Abs\|POS=ADJ`, `POS=VERB\|Tense=Pres`, `Definite=Ind\|Gender=Neut\|Number=Sing\|POS=NUM`, `Degree=Abs\|POS=ADV`, `Case=Gen\|Definite=Def\|Degree=Pos\|Number=Sing\|POS=ADJ`, `Gender=Com\|Number=Sing\|POS=PRON\|PronType=Int,Rel`, `POS=VERB\|Tense=Past\|VerbForm=Part`, `Definite=Ind\|Degree=Sup\|Number=Sing\|POS=ADJ`, `Gender=Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Gender=Com\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs`, `Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Number[psor]=Plur\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Definite=Ind\|POS=NOUN`, `Case=Gen\|Gender=Com\|Number=Sing\|POS=DET\|PronType=Ind`, `Definite=Ind\|Gender=Com\|Number=Sing\|POS=NUM`, `Definite=Def\|Number=Plur\|POS=NOUN`, `Case=Gen\|POS=NOUN`, `POS=AUX\|Tense=Pres\|VerbForm=Part` | | **`parser`** | `ROOT`, `acl:relcl`, `advcl`, `advmod`, `advmod:lmod`, `amod`, `appos`, `aux`, `case`, `cc`, `ccomp`, `compound:prt`, `conj`, `cop`, `dep`, `det`, `expl`, `fixed`, `flat`, `iobj`, `list`, `mark`, `nmod`, `nmod:poss`, `nsubj`, `nummod`, `obj`, `obl`, `obl:lmod`, `obl:tmod`, `punct`, `xcomp` | | **`ner`** | `LOC`, `MISC`, `ORG`, `PER` | </details> ### Accuracy | Type | Score | | --- | --- | | `TOKEN_ACC` | 99.92 | | `TOKEN_P` | 99.70 | | `TOKEN_R` | 99.77 | | `TOKEN_F` | 99.74 | | `SENTS_P` | 98.42 | | `SENTS_R` | 99.29 | | `SENTS_F` | 98.85 | | `TAG_ACC` | 98.47 | | `POS_ACC` | 98.57 | | `MORPH_ACC` | 98.14 | | `MORPH_MICRO_P` | 99.10 | | `MORPH_MICRO_R` | 98.77 | | `MORPH_MICRO_F` | 98.93 | | `DEP_UAS` | 90.84 | | `DEP_LAS` | 88.33 | | `ENTS_P` | 87.08 | | `ENTS_R` | 84.59 | | `ENTS_F` | 85.82 | | `LEMMA_ACC` | 94.20 | | `COREF_LEA_F1` | 41.18 | | `COREF_LEA_PRECISION` | 48.89 | | `COREF_LEA_RECALL` | 35.58 | | `NEL_SCORE` | 80.12 | | `NEL_MICRO_P` | 99.23 | | `NEL_MICRO_R` | 67.19 | | `NEL_MICRO_F` | 80.12 | | `NEL_MACRO_P` | 99.39 | | `NEL_MACRO_R` | 65.99 | | `NEL_MACRO_F` | 78.15 | ### Training This model was trained using [spaCy](https://spacy.io) and logged to [Weights & Biases](https://wandb.ai/kenevoldsen/dacy-v0.2.0). You can find all the training logs [here](https://wandb.ai/kenevoldsen/dacy-v0.2.0).
{"language": ["da"], "license": "apache-2.0", "library_name": "spacy", "tags": ["spacy", "dacy", "danish", "token-classification", "pos tagging", "morphological analysis", "lemmatization", "dependency parsing", "named entity recognition", "coreference resolution", "named entity linking", "named entity disambiguation"], "datasets": ["universal_dependencies", "dane", "alexandrainst/dacoref"], "metrics": ["accuracy"], "model-index": [{"name": "da_dacy_medium_trf-0.2.0", "results": [{"task": {"type": "token-classification", "name": "NER"}, "dataset": {"name": "DaNE", "type": "dane", "split": "test"}, "metrics": [{"type": "precision", "value": 0.8708487085, "name": "NER Precision"}, {"type": "recall", "value": 0.8458781362, "name": "NER Recall"}, {"type": "f_score", "value": 0.8581818182, "name": "NER F Score"}]}, {"task": {"type": "token-classification", "name": "TAG"}, "dataset": {"name": "UD Danish DDT", "type": "universal_dependencies", "config": "da_ddt", "split": "test"}, "metrics": [{"type": "accuracy", "value": 0.9847290149, "name": "TAG (XPOS) Accuracy"}, {"type": "accuracy", "value": 0.985677928, "name": "POS (UPOS) Accuracy"}, {"type": "accuracy", "value": 0.9814371257, "name": "Morph (UFeats) Accuracy"}, {"type": "accuracy", "value": 0.9419805438, "name": "Lemma Accuracy"}, {"type": "f_score", "value": 0.9083920564, "name": "Unlabeled Attachment Score (UAS)"}, {"type": "f_score", "value": 0.883349834, "name": "Labeled Attachment Score (LAS)"}, {"type": "f_score", "value": 0.9885462555, "name": "Sentences F-Score"}]}, {"task": {"type": "coreference-resolution", "name": "coreference-resolution"}, "dataset": {"name": "DaCoref", "type": "alexandrainst/dacoref", "split": "custom"}, "metrics": [{"type": "f_score", "value": 0.4118366346, "name": "LEA"}]}, {"task": {"type": "coreference-resolution", "name": "coreference-resolution"}, "dataset": {"name": "DaNED", "type": "named-entity-linking", "split": "custom"}, "metrics": [{"type": "precision", "value": 0.9923076923, "name": "Named entity Linking Precision"}, {"type": "recall", "value": 0.671875, "name": "Named entity Linking Recall"}, {"type": "f_score", "value": 0.801242236, "name": "Named entity Linking F Score"}]}]}]}
token-classification
chcaa/da_dacy_medium_trf
[ "spacy", "dacy", "danish", "token-classification", "pos tagging", "morphological analysis", "lemmatization", "dependency parsing", "named entity recognition", "coreference resolution", "named entity linking", "named entity disambiguation", "da", "dataset:universal_dependencies", "dataset:dane", "dataset:alexandrainst/dacoref", "license:apache-2.0", "model-index", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[ "da" ]
TAGS #spacy #dacy #danish #token-classification #pos tagging #morphological analysis #lemmatization #dependency parsing #named entity recognition #coreference resolution #named entity linking #named entity disambiguation #da #dataset-universal_dependencies #dataset-dane #dataset-alexandrainst/dacoref #license-apache-2.0 #model-index #region-us
<a href="URL src="URL width="175" height="175" align="right" /> DaCy medium =========== DaCy is a Danish language processing framework with state-of-the-art pipelines as well as functionality for analysing Danish pipelines. DaCy's largest pipeline has achieved State-of-the-Art performance on parts-of-speech tagging and dependency parsing for Danish on the Danish Dependency treebank as well as competitive performance on named entity recognition, named entity disambiguation and coreference resolution. To read more check out the DaCy repository for material on how to use DaCy and reproduce the results. DaCy also contains guides on usage of the package as well as behavioural test for biases and robustness of Danish NLP pipelines. ### Label Scheme View label scheme (211 labels for 4 components) ### Accuracy ### Training This model was trained using spaCy and logged to Weights & Biases. You can find all the training logs here.
[ "### Label Scheme\n\n\n\nView label scheme (211 labels for 4 components)", "### Accuracy", "### Training\n\n\nThis model was trained using spaCy and logged to Weights & Biases. You can find all the training logs here." ]
[ "TAGS\n#spacy #dacy #danish #token-classification #pos tagging #morphological analysis #lemmatization #dependency parsing #named entity recognition #coreference resolution #named entity linking #named entity disambiguation #da #dataset-universal_dependencies #dataset-dane #dataset-alexandrainst/dacoref #license-apache-2.0 #model-index #region-us \n", "### Label Scheme\n\n\n\nView label scheme (211 labels for 4 components)", "### Accuracy", "### Training\n\n\nThis model was trained using spaCy and logged to Weights & Biases. You can find all the training logs here." ]
[ 107, 17, 5, 33 ]
[ "passage: TAGS\n#spacy #dacy #danish #token-classification #pos tagging #morphological analysis #lemmatization #dependency parsing #named entity recognition #coreference resolution #named entity linking #named entity disambiguation #da #dataset-universal_dependencies #dataset-dane #dataset-alexandrainst/dacoref #license-apache-2.0 #model-index #region-us \n### Label Scheme\n\n\n\nView label scheme (211 labels for 4 components)### Accuracy### Training\n\n\nThis model was trained using spaCy and logged to Weights & Biases. You can find all the training logs here." ]
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spacy
<a href="https://github.com/centre-for-humanities-computing/Dacy"><img src="https://centre-for-humanities-computing.github.io/DaCy/_static/icon.png" width="175" height="175" align="right" /></a> # DaCy small DaCy is a Danish language processing framework with state-of-the-art pipelines as well as functionality for analysing Danish pipelines. DaCy's largest pipeline has achieved State-of-the-Art performance on parts-of-speech tagging and dependency parsing for Danish on the Danish Dependency treebank as well as competitive performance on named entity recognition, named entity disambiguation and coreference resolution. To read more check out the [DaCy repository](https://github.com/centre-for-humanities-computing/DaCy) for material on how to use DaCy and reproduce the results. DaCy also contains guides on usage of the package as well as behavioural test for biases and robustness of Danish NLP pipelines. | Feature | Description | | --- | --- | | **Name** | `da_dacy_small_trf` | | **Version** | `0.2.0` | | **spaCy** | `>=3.5.2,<3.6.0` | | **Default Pipeline** | `transformer`, `tagger`, `morphologizer`, `trainable_lemmatizer`, `parser`, `ner`, `coref`, `span_resolver`, `span_cleaner`, `entity_linker` | | **Components** | `transformer`, `tagger`, `morphologizer`, `trainable_lemmatizer`, `parser`, `ner`, `coref`, `span_resolver`, `span_cleaner`, `entity_linker` | | **Vectors** | 0 keys, 0 unique vectors (0 dimensions) | | **Sources** | [UD Danish DDT v2.11](https://github.com/UniversalDependencies/UD_Danish-DDT) (Johannsen, Anders; Martínez Alonso, Héctor; Plank, Barbara)<br />[DaNE](https://huggingface.co/datasets/dane) (Rasmus Hvingelby, Amalie B. Pauli, Maria Barrett, Christina Rosted, Lasse M. Lidegaard, Anders Søgaard)<br />[DaCoref](https://huggingface.co/datasets/alexandrainst/dacoref) (Buch-Kromann, Matthias)<br />[DaNED](https://danlp-alexandra.readthedocs.io/en/stable/docs/datasets.html#daned) (Barrett, M. J., Lam, H., Wu, M., Lacroix, O., Plank, B., & Søgaard, A.)<br />[jonfd/electra-small-nordic](https://huggingface.co/jonfd/electra-small-nordic) (Jón Friðrik Daðason) | | **License** | `Apache-2.0` | | **Author** | [Kenneth Enevoldsen](https://chcaa.io/#/) | ### Label Scheme <details> <summary>View label scheme (211 labels for 4 components)</summary> | Component | Labels | | --- | --- | | **`tagger`** | `ADJ`, `ADP`, `ADV`, `AUX`, `CCONJ`, `DET`, `INTJ`, `NOUN`, `NUM`, `PART`, `PRON`, `PROPN`, `PUNCT`, `SCONJ`, `SYM`, `VERB`, `X` | | **`morphologizer`** | `AdpType=Prep\|POS=ADP`, `Definite=Ind\|Gender=Com\|Number=Sing\|POS=NOUN`, `Mood=Ind\|POS=AUX\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `POS=PROPN`, `Definite=Ind\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Definite=Def\|Gender=Neut\|Number=Sing\|POS=NOUN`, `POS=SCONJ`, `Definite=Def\|Gender=Com\|Number=Sing\|POS=NOUN`, `Mood=Ind\|POS=VERB\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `POS=ADV`, `Number=Plur\|POS=DET\|PronType=Dem`, `Degree=Pos\|Number=Plur\|POS=ADJ`, `Definite=Ind\|Gender=Com\|Number=Plur\|POS=NOUN`, `POS=PUNCT`, `NumType=Ord\|POS=ADJ`, `POS=CCONJ`, `Definite=Ind\|Gender=Neut\|Number=Plur\|POS=NOUN`, `POS=VERB\|VerbForm=Inf\|Voice=Act`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Degree=Sup\|POS=ADV`, `Degree=Pos\|POS=ADV`, `Gender=Com\|Number=Sing\|POS=DET\|PronType=Ind`, `Number=Plur\|POS=DET\|PronType=Ind`, `POS=ADP`, `POS=ADV\|PartType=Inf`, `Case=Nom\|Gender=Com\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Mood=Ind\|POS=AUX\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Definite=Def\|Degree=Pos\|Number=Sing\|POS=ADJ`, `Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Mood=Ind\|POS=VERB\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `POS=ADP\|PartType=Inf`, `Definite=Ind\|Degree=Pos\|Gender=Com\|Number=Sing\|POS=ADJ`, `NumType=Card\|POS=NUM`, `Degree=Pos\|POS=ADJ`, `Definite=Ind\|Number=Sing\|POS=AUX\|Tense=Past\|VerbForm=Part`, `POS=PART\|PartType=Inf`, `Case=Acc\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Definite=Def\|Gender=Com\|Number=Plur\|POS=NOUN`, `Definite=Ind\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `POS=VERB\|Tense=Pres\|VerbForm=Part`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Gen\|Definite=Def\|Gender=Com\|Number=Sing\|POS=NOUN`, `Definite=Def\|Degree=Sup\|Number=Plur\|POS=ADJ`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `POS=AUX\|VerbForm=Inf\|Voice=Act`, `Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Definite=Ind\|Degree=Cmp\|Number=Sing\|POS=ADJ`, `Degree=Cmp\|POS=ADJ`, `POS=PRON\|PartType=Inf`, `Definite=Ind\|Degree=Pos\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Com\|POS=PRON\|PronType=Ind`, `Number=Plur\|POS=PRON\|PronType=Ind`, `POS=INTJ`, `Gender=Com\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Gen\|Number=Plur\|POS=DET\|PronType=Ind`, `Mood=Ind\|POS=VERB\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Definite=Def\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Degree=Cmp\|POS=ADV`, `Number=Plur\|Number[psor]=Plur\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs\|Style=Form`, `Case=Acc\|Gender=Com\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Gen\|POS=PROPN`, `Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Ind`, `Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Gender=Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Acc\|Gender=Com\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Definite=Def\|Degree=Sup\|POS=ADJ`, `Gender=Neut\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Definite=Def\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part`, `POS=PRON\|PronType=Dem`, `Degree=Pos\|Gender=Com\|Number=Sing\|POS=ADJ`, `Number=Plur\|POS=NUM`, `POS=VERB\|VerbForm=Inf\|Voice=Pass`, `Definite=Def\|Degree=Sup\|Number=Sing\|POS=ADJ`, `Number=Sing\|POS=PRON\|PronType=Int,Rel`, `Case=Nom\|Gender=Com\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Gender=Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Gender=Com\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `POS=PRON`, `Definite=Ind\|Number=Sing\|POS=NOUN`, `Definite=Ind\|Number=Sing\|POS=NUM`, `Case=Gen\|Definite=Ind\|Gender=Com\|Number=Sing\|POS=NOUN`, `Foreign=Yes\|POS=ADV`, `POS=NOUN`, `Case=Gen\|Definite=Def\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Gender=Com\|Number=Plur\|POS=NOUN`, `Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Int,Rel`, `Case=Nom\|Gender=Com\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Gender=Com\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Gen\|Definite=Ind\|Gender=Com\|Number=Plur\|POS=NOUN`, `Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Degree=Sup\|POS=ADJ`, `Degree=Pos\|Number=Sing\|POS=ADJ`, `Mood=Imp\|POS=VERB`, `Case=Nom\|Gender=Com\|POS=PRON\|Person=2\|Polite=Form\|PronType=Prs`, `Case=Acc\|Gender=Com\|POS=PRON\|Person=2\|Polite=Form\|PronType=Prs`, `POS=X`, `Case=Gen\|Definite=Def\|Gender=Com\|Number=Plur\|POS=NOUN`, `Number=Plur\|POS=PRON\|PronType=Dem`, `Case=Acc\|Gender=Com\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Number=Plur\|POS=PRON\|PronType=Int,Rel`, `Gender=Com\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Degree=Cmp\|Number=Plur\|POS=ADJ`, `Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Gender=Com\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs\|Style=Form`, `Case=Nom\|Gender=Com\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Acc\|Gender=Com\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Gender=Com\|POS=PRON\|PronType=Int,Rel`, `Case=Gen\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Gender=Neut\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `POS=VERB\|VerbForm=Ger`, `Gender=Com\|Number=Sing\|POS=PRON\|PronType=Dem`, `Case=Gen\|POS=PRON\|PronType=Int,Rel`, `Mood=Ind\|POS=VERB\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Abbr=Yes\|POS=X`, `Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Gender=Com\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Definite=Ind\|Number=Plur\|POS=NOUN`, `Foreign=Yes\|POS=X`, `Number=Plur\|POS=PRON\|PronType=Rcp`, `Case=Nom\|Gender=Com\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Gen\|Degree=Cmp\|POS=ADJ`, `Case=Gen\|Definite=Def\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Acc\|Gender=Com\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Dem`, `Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs\|Style=Form`, `Gender=Neut\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs\|Style=Form`, `Number=Plur\|Number[psor]=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Number[psor]=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Number=Plur\|POS=PRON\|PronType=Rcp`, `POS=DET\|Person=2\|Polite=Form\|Poss=Yes\|PronType=Prs`, `POS=SYM`, `POS=DET\|PronType=Dem`, `Gender=Com\|Number=Sing\|POS=NUM`, `Number[psor]=Plur\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Definite=Def\|Degree=Abs\|POS=ADJ`, `POS=VERB\|Tense=Pres`, `Definite=Ind\|Gender=Neut\|Number=Sing\|POS=NUM`, `Degree=Abs\|POS=ADV`, `Case=Gen\|Definite=Def\|Degree=Pos\|Number=Sing\|POS=ADJ`, `Gender=Com\|Number=Sing\|POS=PRON\|PronType=Int,Rel`, `POS=VERB\|Tense=Past\|VerbForm=Part`, `Definite=Ind\|Degree=Sup\|Number=Sing\|POS=ADJ`, `Gender=Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Gender=Com\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs`, `Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Number[psor]=Plur\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Definite=Ind\|POS=NOUN`, `Case=Gen\|Gender=Com\|Number=Sing\|POS=DET\|PronType=Ind`, `Definite=Ind\|Gender=Com\|Number=Sing\|POS=NUM`, `Definite=Def\|Number=Plur\|POS=NOUN`, `Case=Gen\|POS=NOUN`, `POS=AUX\|Tense=Pres\|VerbForm=Part` | | **`parser`** | `ROOT`, `acl:relcl`, `advcl`, `advmod`, `advmod:lmod`, `amod`, `appos`, `aux`, `case`, `cc`, `ccomp`, `compound:prt`, `conj`, `cop`, `dep`, `det`, `expl`, `fixed`, `flat`, `iobj`, `list`, `mark`, `nmod`, `nmod:poss`, `nsubj`, `nummod`, `obj`, `obl`, `obl:lmod`, `obl:tmod`, `punct`, `xcomp` | | **`ner`** | `LOC`, `MISC`, `ORG`, `PER` | </details> ### Accuracy | Type | Score | | --- | --- | | `TOKEN_ACC` | 99.92 | | `TOKEN_P` | 99.70 | | `TOKEN_R` | 99.77 | | `TOKEN_F` | 99.74 | | `SENTS_P` | 92.96 | | `SENTS_R` | 95.75 | | `SENTS_F` | 94.33 | | `TAG_ACC` | 98.47 | | `POS_ACC` | 98.42 | | `MORPH_ACC` | 97.73 | | `MORPH_MICRO_P` | 98.94 | | `MORPH_MICRO_R` | 98.33 | | `MORPH_MICRO_F` | 98.64 | | `DEP_UAS` | 89.79 | | `DEP_LAS` | 87.02 | | `ENTS_P` | 83.06 | | `ENTS_R` | 81.72 | | `ENTS_F` | 82.38 | | `LEMMA_ACC` | 94.67 | | `COREF_LEA_F1` | 42.18 | | `COREF_LEA_PRECISION` | 44.79 | | `COREF_LEA_RECALL` | 39.86 | | `NEL_SCORE` | 35.20 | | `NEL_MICRO_P` | 84.62 | | `NEL_MICRO_R` | 22.22 | | `NEL_MICRO_F` | 35.20 | | `NEL_MACRO_P` | 87.68 | | `NEL_MACRO_R` | 24.76 | | `NEL_MACRO_F` | 37.52 | ### Training This model was trained using [spaCy](https://spacy.io) and logged to [Weights & Biases](https://wandb.ai/kenevoldsen/dacy-v0.2.0). You can find all the training logs [here](https://wandb.ai/kenevoldsen/dacy-v0.2.0).
{"language": ["da"], "license": "apache-2.0", "library_name": "spacy", "tags": ["spacy", "dacy", "danish", "token-classification", "pos tagging", "morphological analysis", "lemmatization", "dependency parsing", "named entity recognition", "coreference resolution", "named entity linking", "named entity disambiguation"], "datasets": ["universal_dependencies", "dane", "alexandrainst/dacoref"], "metrics": ["accuracy"], "model-index": [{"name": "da_dacy_small_trf-0.2.0", "results": [{"task": {"type": "token-classification", "name": "NER"}, "dataset": {"name": "DaNE", "type": "dane", "split": "test"}, "metrics": [{"type": "precision", "value": 0.8306010929, "name": "NER Precision"}, {"type": "recall", "value": 0.8172043011, "name": "NER Recall"}, {"type": "f_score", "value": 0.8238482385, "name": "NER F Score"}]}, {"task": {"type": "token-classification", "name": "TAG"}, "dataset": {"name": "UD Danish DDT", "type": "universal_dependencies", "config": "da_ddt", "split": "test"}, "metrics": [{"type": "accuracy", "value": 0.9846798742, "name": "TAG (XPOS) Accuracy"}, {"type": "accuracy", "value": 0.9842315369, "name": "POS (UPOS) Accuracy"}, {"type": "accuracy", "value": 0.9772942762, "name": "Morph (UFeats) Accuracy"}, {"type": "accuracy", "value": 0.9466699925, "name": "Lemma Accuracy"}, {"type": "f_score", "value": 0.8978522787, "name": "Unlabeled Attachment Score (UAS)"}, {"type": "f_score", "value": 0.8701623698, "name": "Labeled Attachment Score (LAS)"}, {"type": "f_score", "value": 0.9433304272, "name": "Sentences F-Score"}]}, {"task": {"type": "coreference-resolution", "name": "coreference-resolution"}, "dataset": {"name": "DaCoref", "type": "alexandrainst/dacoref", "split": "custom"}, "metrics": [{"type": "f_score", "value": 0.4218334451, "name": "LEA"}]}, {"task": {"type": "coreference-resolution", "name": "coreference-resolution"}, "dataset": {"name": "DaNED", "type": "named-entity-linking", "split": "custom"}, "metrics": [{"type": "precision", "value": 0.8461538462, "name": "Named entity Linking Precision"}, {"type": "recall", "value": 0.2222222222, "name": "Named entity Linking Recall"}, {"type": "f_score", "value": 0.352, "name": "Named entity Linking F Score"}]}]}]}
token-classification
chcaa/da_dacy_small_trf
[ "spacy", "dacy", "danish", "token-classification", "pos tagging", "morphological analysis", "lemmatization", "dependency parsing", "named entity recognition", "coreference resolution", "named entity linking", "named entity disambiguation", "da", "dataset:universal_dependencies", "dataset:dane", "dataset:alexandrainst/dacoref", "license:apache-2.0", "model-index", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[ "da" ]
TAGS #spacy #dacy #danish #token-classification #pos tagging #morphological analysis #lemmatization #dependency parsing #named entity recognition #coreference resolution #named entity linking #named entity disambiguation #da #dataset-universal_dependencies #dataset-dane #dataset-alexandrainst/dacoref #license-apache-2.0 #model-index #region-us
<a href="URL src="URL width="175" height="175" align="right" /> DaCy small ========== DaCy is a Danish language processing framework with state-of-the-art pipelines as well as functionality for analysing Danish pipelines. DaCy's largest pipeline has achieved State-of-the-Art performance on parts-of-speech tagging and dependency parsing for Danish on the Danish Dependency treebank as well as competitive performance on named entity recognition, named entity disambiguation and coreference resolution. To read more check out the DaCy repository for material on how to use DaCy and reproduce the results. DaCy also contains guides on usage of the package as well as behavioural test for biases and robustness of Danish NLP pipelines. ### Label Scheme View label scheme (211 labels for 4 components) ### Accuracy ### Training This model was trained using spaCy and logged to Weights & Biases. You can find all the training logs here.
[ "### Label Scheme\n\n\n\nView label scheme (211 labels for 4 components)", "### Accuracy", "### Training\n\n\nThis model was trained using spaCy and logged to Weights & Biases. You can find all the training logs here." ]
[ "TAGS\n#spacy #dacy #danish #token-classification #pos tagging #morphological analysis #lemmatization #dependency parsing #named entity recognition #coreference resolution #named entity linking #named entity disambiguation #da #dataset-universal_dependencies #dataset-dane #dataset-alexandrainst/dacoref #license-apache-2.0 #model-index #region-us \n", "### Label Scheme\n\n\n\nView label scheme (211 labels for 4 components)", "### Accuracy", "### Training\n\n\nThis model was trained using spaCy and logged to Weights & Biases. You can find all the training logs here." ]
[ 107, 17, 5, 33 ]
[ "passage: TAGS\n#spacy #dacy #danish #token-classification #pos tagging #morphological analysis #lemmatization #dependency parsing #named entity recognition #coreference resolution #named entity linking #named entity disambiguation #da #dataset-universal_dependencies #dataset-dane #dataset-alexandrainst/dacoref #license-apache-2.0 #model-index #region-us \n### Label Scheme\n\n\n\nView label scheme (211 labels for 4 components)### Accuracy### Training\n\n\nThis model was trained using spaCy and logged to Weights & Biases. You can find all the training logs here." ]
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null
null
transformers
#Chizuru Ichinose~ DialoGPT Model
{"tags": ["conversational"]}
text-generation
chellver24/DialoGPT-medium-chizuru_ichinose
[ "transformers", "pytorch", "gpt2", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[]
TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
#Chizuru Ichinose~ DialoGPT Model
[]
[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]
[ 51 ]
[ "passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]
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null
null
transformers
<!-- 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. --> # bart-large-chinese-cnhdwriter This model is a fine-tuned version of [fnlp/bart-large-chinese](https://huggingface.co/fnlp/bart-large-chinese) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.3859 - Rouge1: 16.8496 - Rouge2: 2.5548 - Rougel: 16.8123 - Rougelsum: 16.8056 - Gen Len: 18.9357 ## 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: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:------:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:| | 1.2119 | 1.0 | 62716 | 1.1876 | 15.3858 | 2.1251 | 15.3709 | 15.3705 | 18.7269 | | 1.0847 | 2.0 | 125432 | 1.3353 | 13.7743 | 1.9047 | 13.7664 | 13.7421 | 18.6183 | | 0.6995 | 3.0 | 188148 | 1.2209 | 16.6797 | 2.3979 | 16.6258 | 16.6368 | 18.8953 | | 0.4819 | 4.0 | 250864 | 1.3859 | 16.8496 | 2.5548 | 16.8123 | 16.8056 | 18.9357 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3
{"tags": ["generated_from_trainer"], "metrics": ["rouge"], "base_model": "fnlp/bart-large-chinese", "model-index": [{"name": "bart-large-chinese-cnhdwriter", "results": []}]}
text2text-generation
chinhon/bart-large-chinese-cnhdwriter
[ "transformers", "pytorch", "tensorboard", "safetensors", "bart", "text2text-generation", "generated_from_trainer", "base_model:fnlp/bart-large-chinese", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[]
TAGS #transformers #pytorch #tensorboard #safetensors #bart #text2text-generation #generated_from_trainer #base_model-fnlp/bart-large-chinese #autotrain_compatible #endpoints_compatible #has_space #region-us
bart-large-chinese-cnhdwriter ============================= This model is a fine-tuned version of fnlp/bart-large-chinese on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 1.3859 * Rouge1: 16.8496 * Rouge2: 2.5548 * Rougel: 16.8123 * Rougelsum: 16.8056 * Gen Len: 18.9357 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: 1 * eval\_batch\_size: 1 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 4 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.15.0 * Pytorch 1.10.0+cu111 * Datasets 1.17.0 * Tokenizers 0.10.3
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3" ]
[ "TAGS\n#transformers #pytorch #tensorboard #safetensors #bart #text2text-generation #generated_from_trainer #base_model-fnlp/bart-large-chinese #autotrain_compatible #endpoints_compatible #has_space #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3" ]
[ 73, 113, 4, 33 ]
[ "passage: TAGS\n#transformers #pytorch #tensorboard #safetensors #bart #text2text-generation #generated_from_trainer #base_model-fnlp/bart-large-chinese #autotrain_compatible #endpoints_compatible #has_space #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3" ]
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null
null
transformers
<!-- 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. --> # bart-large-cnn-summarizer_03 This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.0999 - Rouge1: 51.6222 - Rouge2: 33.428 - Rougel: 40.2093 - Rougelsum: 47.7154 - Gen Len: 102.7962 ## 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: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:| | 0.9348 | 1.0 | 17166 | 0.9969 | 51.0763 | 32.9497 | 39.6851 | 47.0744 | 99.664 | | 0.7335 | 2.0 | 34332 | 1.0019 | 51.8002 | 33.8081 | 40.5887 | 47.9445 | 99.7884 | | 0.471 | 3.0 | 51498 | 1.0999 | 51.6222 | 33.428 | 40.2093 | 47.7154 | 102.7962 | ### Framework versions - Transformers 4.12.3 - Pytorch 1.9.0+cu111 - Datasets 1.15.1 - Tokenizers 0.10.3
{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "base_model": "facebook/bart-large-cnn", "model-index": [{"name": "bart-large-cnn-summarizer_03", "results": []}]}
text2text-generation
chinhon/bart-large-cnn-summarizer_03
[ "transformers", "pytorch", "tensorboard", "safetensors", "bart", "text2text-generation", "generated_from_trainer", "base_model:facebook/bart-large-cnn", "license:mit", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[]
TAGS #transformers #pytorch #tensorboard #safetensors #bart #text2text-generation #generated_from_trainer #base_model-facebook/bart-large-cnn #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
bart-large-cnn-summarizer\_03 ============================= This model is a fine-tuned version of facebook/bart-large-cnn on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 1.0999 * Rouge1: 51.6222 * Rouge2: 33.428 * Rougel: 40.2093 * Rougelsum: 47.7154 * Gen Len: 102.7962 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: 1 * eval\_batch\_size: 1 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 3 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.12.3 * Pytorch 1.9.0+cu111 * Datasets 1.15.1 * Tokenizers 0.10.3
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.12.3\n* Pytorch 1.9.0+cu111\n* Datasets 1.15.1\n* Tokenizers 0.10.3" ]
[ "TAGS\n#transformers #pytorch #tensorboard #safetensors #bart #text2text-generation #generated_from_trainer #base_model-facebook/bart-large-cnn #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.12.3\n* Pytorch 1.9.0+cu111\n* Datasets 1.15.1\n* Tokenizers 0.10.3" ]
[ 77, 113, 4, 34 ]
[ "passage: TAGS\n#transformers #pytorch #tensorboard #safetensors #bart #text2text-generation #generated_from_trainer #base_model-facebook/bart-large-cnn #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.12.3\n* Pytorch 1.9.0+cu111\n* Datasets 1.15.1\n* Tokenizers 0.10.3" ]
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null
null
transformers
<!-- 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. --> # bart-large-commentaries_hdwriter This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.1619 - Rouge1: 26.1101 - Rouge2: 9.928 - Rougel: 22.9007 - Rougelsum: 23.117 - Gen Len: 15.9536 ## 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: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:| | 2.6237 | 1.0 | 5072 | 2.5309 | 26.4063 | 9.1795 | 22.6699 | 22.9125 | 17.3103 | | 1.8808 | 2.0 | 10144 | 2.5049 | 25.3706 | 8.7568 | 21.8594 | 22.1233 | 15.8579 | | 1.3084 | 3.0 | 15216 | 2.6680 | 26.6284 | 9.9914 | 23.1477 | 23.3625 | 16.8832 | | 0.9247 | 4.0 | 20288 | 2.8923 | 26.3827 | 9.8217 | 22.9524 | 23.1651 | 15.4529 | | 0.692 | 5.0 | 25360 | 3.1619 | 26.1101 | 9.928 | 22.9007 | 23.117 | 15.9536 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "bart-large-commentaries_hdwriter", "results": []}]}
text2text-generation
chinhon/bart-large-commentaries_hdwriter
[ "transformers", "pytorch", "tensorboard", "safetensors", "bart", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[]
TAGS #transformers #pytorch #tensorboard #safetensors #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
bart-large-commentaries\_hdwriter ================================= This model is a fine-tuned version of facebook/bart-large on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 3.1619 * Rouge1: 26.1101 * Rouge2: 9.928 * Rougel: 22.9007 * Rougelsum: 23.117 * Gen Len: 15.9536 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: 1 * eval\_batch\_size: 1 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 5 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.15.0 * Pytorch 1.10.0+cu111 * Datasets 1.17.0 * Tokenizers 0.10.3
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3" ]
[ "TAGS\n#transformers #pytorch #tensorboard #safetensors #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3" ]
[ 66, 113, 4, 33 ]
[ "passage: TAGS\n#transformers #pytorch #tensorboard #safetensors #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3" ]
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null
null
transformers
<!-- 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. --> # distilgpt2-sgnews This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.1516 ## 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: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 3.3558 | 1.0 | 23769 | 3.2316 | | 3.2558 | 2.0 | 47538 | 3.1683 | | 3.2321 | 3.0 | 71307 | 3.1516 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.9.0+cu111 - Datasets 1.14.0 - Tokenizers 0.10.3
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "distilgpt2", "model-index": [{"name": "distilgpt2-sgnews", "results": []}]}
text-generation
chinhon/distilgpt2-sgnews
[ "transformers", "pytorch", "tensorboard", "gpt2", "text-generation", "generated_from_trainer", "base_model:distilgpt2", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[]
TAGS #transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #base_model-distilgpt2 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
distilgpt2-sgnews ================= This model is a fine-tuned version of distilgpt2 on the None dataset. It achieves the following results on the evaluation set: * Loss: 3.1516 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: 3.0 ### Training results ### Framework versions * Transformers 4.11.3 * Pytorch 1.9.0+cu111 * Datasets 1.14.0 * Tokenizers 0.10.3
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0", "### Training results", "### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.9.0+cu111\n* Datasets 1.14.0\n* Tokenizers 0.10.3" ]
[ "TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #base_model-distilgpt2 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0", "### Training results", "### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.9.0+cu111\n* Datasets 1.14.0\n* Tokenizers 0.10.3" ]
[ 76, 98, 4, 34 ]
[ "passage: TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #base_model-distilgpt2 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0### Training results### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.9.0+cu111\n* Datasets 1.14.0\n* Tokenizers 0.10.3" ]
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null
null
transformers
# Model Trained Using AutoNLP - Problem type: Summarization - Model ID: 25965855 - CO2 Emissions (in grams): 114.71292762345828 ## Validation Metrics - Loss: 1.3862273693084717 - Rouge1: 52.4988 - Rouge2: 31.6973 - RougeL: 47.1727 - RougeLsum: 47.1576 - Gen Len: 17.6194 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP"}' https://api-inference.huggingface.co/chinhon/autonlp-sg_headline_generator-25965855 ```
{"language": "en", "tags": "autonlp", "datasets": ["chinhon/autonlp-data-sg_headline_generator"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 114.71292762345828}
text2text-generation
chinhon/headline_writer
[ "transformers", "pytorch", "safetensors", "bart", "text2text-generation", "autonlp", "en", "dataset:chinhon/autonlp-data-sg_headline_generator", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[ "en" ]
TAGS #transformers #pytorch #safetensors #bart #text2text-generation #autonlp #en #dataset-chinhon/autonlp-data-sg_headline_generator #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us
# Model Trained Using AutoNLP - Problem type: Summarization - Model ID: 25965855 - CO2 Emissions (in grams): 114.71292762345828 ## Validation Metrics - Loss: 1.3862273693084717 - Rouge1: 52.4988 - Rouge2: 31.6973 - RougeL: 47.1727 - RougeLsum: 47.1576 - Gen Len: 17.6194 ## Usage You can use cURL to access this model:
[ "# Model Trained Using AutoNLP\n\n- Problem type: Summarization\n- Model ID: 25965855\n- CO2 Emissions (in grams): 114.71292762345828", "## Validation Metrics\n\n- Loss: 1.3862273693084717\n- Rouge1: 52.4988\n- Rouge2: 31.6973\n- RougeL: 47.1727\n- RougeLsum: 47.1576\n- Gen Len: 17.6194", "## Usage\n\nYou can use cURL to access this model:" ]
[ "TAGS\n#transformers #pytorch #safetensors #bart #text2text-generation #autonlp #en #dataset-chinhon/autonlp-data-sg_headline_generator #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us \n", "# Model Trained Using AutoNLP\n\n- Problem type: Summarization\n- Model ID: 25965855\n- CO2 Emissions (in grams): 114.71292762345828", "## Validation Metrics\n\n- Loss: 1.3862273693084717\n- Rouge1: 52.4988\n- Rouge2: 31.6973\n- RougeL: 47.1727\n- RougeLsum: 47.1576\n- Gen Len: 17.6194", "## Usage\n\nYou can use cURL to access this model:" ]
[ 83, 41, 56, 13 ]
[ "passage: TAGS\n#transformers #pytorch #safetensors #bart #text2text-generation #autonlp #en #dataset-chinhon/autonlp-data-sg_headline_generator #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us \n# Model Trained Using AutoNLP\n\n- Problem type: Summarization\n- Model ID: 25965855\n- CO2 Emissions (in grams): 114.71292762345828## Validation Metrics\n\n- Loss: 1.3862273693084717\n- Rouge1: 52.4988\n- Rouge2: 31.6973\n- RougeL: 47.1727\n- RougeLsum: 47.1576\n- Gen Len: 17.6194## Usage\n\nYou can use cURL to access this model:" ]
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null
null
transformers
# Model Trained Using AutoNLP - Problem type: Summarization - Model ID: 25965856 - CO2 Emissions (in grams): 396.629376395644 ## Validation Metrics - Loss: 1.4130597114562988 - Rouge1: 51.7922 - Rouge2: 30.8259 - RougeL: 46.4585 - RougeLsum: 46.4807 - Gen Len: 15.8411 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP"}' https://api-inference.huggingface.co/chinhon/autonlp-sg_headline_generator-25965856 ```
{"language": "en", "tags": "autonlp", "datasets": ["chinhon/autonlp-data-sg_headline_generator"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 396.629376395644}
text2text-generation
chinhon/headline_writer2
[ "transformers", "pytorch", "safetensors", "bart", "text2text-generation", "autonlp", "en", "dataset:chinhon/autonlp-data-sg_headline_generator", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[ "en" ]
TAGS #transformers #pytorch #safetensors #bart #text2text-generation #autonlp #en #dataset-chinhon/autonlp-data-sg_headline_generator #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
# Model Trained Using AutoNLP - Problem type: Summarization - Model ID: 25965856 - CO2 Emissions (in grams): 396.629376395644 ## Validation Metrics - Loss: 1.4130597114562988 - Rouge1: 51.7922 - Rouge2: 30.8259 - RougeL: 46.4585 - RougeLsum: 46.4807 - Gen Len: 15.8411 ## Usage You can use cURL to access this model:
[ "# Model Trained Using AutoNLP\n\n- Problem type: Summarization\n- Model ID: 25965856\n- CO2 Emissions (in grams): 396.629376395644", "## Validation Metrics\n\n- Loss: 1.4130597114562988\n- Rouge1: 51.7922\n- Rouge2: 30.8259\n- RougeL: 46.4585\n- RougeLsum: 46.4807\n- Gen Len: 15.8411", "## Usage\n\nYou can use cURL to access this model:" ]
[ "TAGS\n#transformers #pytorch #safetensors #bart #text2text-generation #autonlp #en #dataset-chinhon/autonlp-data-sg_headline_generator #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n", "# Model Trained Using AutoNLP\n\n- Problem type: Summarization\n- Model ID: 25965856\n- CO2 Emissions (in grams): 396.629376395644", "## Validation Metrics\n\n- Loss: 1.4130597114562988\n- Rouge1: 51.7922\n- Rouge2: 30.8259\n- RougeL: 46.4585\n- RougeLsum: 46.4807\n- Gen Len: 15.8411", "## Usage\n\nYou can use cURL to access this model:" ]
[ 79, 41, 57, 13 ]
[ "passage: TAGS\n#transformers #pytorch #safetensors #bart #text2text-generation #autonlp #en #dataset-chinhon/autonlp-data-sg_headline_generator #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n# Model Trained Using AutoNLP\n\n- Problem type: Summarization\n- Model ID: 25965856\n- CO2 Emissions (in grams): 396.629376395644## Validation Metrics\n\n- Loss: 1.4130597114562988\n- Rouge1: 51.7922\n- Rouge2: 30.8259\n- RougeL: 46.4585\n- RougeLsum: 46.4807\n- Gen Len: 15.8411## Usage\n\nYou can use cURL to access this model:" ]
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null
null
transformers
<!-- 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. --> # pegasus-large-commentaries_hd This model is a fine-tuned version of [google/pegasus-large](https://huggingface.co/google/pegasus-large) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.5453 - Rouge1: 26.3475 - Rouge2: 9.5095 - Rougel: 22.6367 - Rougelsum: 22.8127 - Gen Len: 14.4789 ## 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: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:| | 2.5718 | 1.0 | 4710 | 2.5277 | 25.1384 | 8.6528 | 21.3443 | 21.5289 | 15.3268 | | 2.4034 | 2.0 | 9420 | 2.4973 | 25.9298 | 9.2238 | 22.3192 | 22.4817 | 14.2243 | | 2.2093 | 3.0 | 14130 | 2.5013 | 26.6036 | 9.7482 | 22.8409 | 23.0077 | 14.2263 | | 2.0518 | 4.0 | 18840 | 2.5272 | 26.4723 | 9.6599 | 22.7439 | 22.9201 | 14.38 | | 1.9906 | 5.0 | 23550 | 2.5453 | 26.3475 | 9.5095 | 22.6367 | 22.8127 | 14.4789 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3
{"tags": ["generated_from_trainer"], "metrics": ["rouge"], "base_model": "google/pegasus-large", "model-index": [{"name": "pegasus-large-commentaries_hd", "results": []}]}
text2text-generation
chinhon/pegasus-large-commentaries_hd
[ "transformers", "pytorch", "tensorboard", "pegasus", "text2text-generation", "generated_from_trainer", "base_model:google/pegasus-large", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[]
TAGS #transformers #pytorch #tensorboard #pegasus #text2text-generation #generated_from_trainer #base_model-google/pegasus-large #autotrain_compatible #endpoints_compatible #has_space #region-us
pegasus-large-commentaries\_hd ============================== This model is a fine-tuned version of google/pegasus-large on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 2.5453 * Rouge1: 26.3475 * Rouge2: 9.5095 * Rougel: 22.6367 * Rougelsum: 22.8127 * Gen Len: 14.4789 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: 1 * eval\_batch\_size: 1 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 5 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.15.0 * Pytorch 1.10.0+cu111 * Datasets 1.17.0 * Tokenizers 0.10.3
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3" ]
[ "TAGS\n#transformers #pytorch #tensorboard #pegasus #text2text-generation #generated_from_trainer #base_model-google/pegasus-large #autotrain_compatible #endpoints_compatible #has_space #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3" ]
[ 68, 113, 4, 33 ]
[ "passage: TAGS\n#transformers #pytorch #tensorboard #pegasus #text2text-generation #generated_from_trainer #base_model-google/pegasus-large #autotrain_compatible #endpoints_compatible #has_space #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3" ]
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null
null
transformers
<!-- 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. --> # pegasus-multi_news-commentaries_hdwriter This model is a fine-tuned version of [google/pegasus-multi_news](https://huggingface.co/google/pegasus-multi_news) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.7259 - Rouge1: 21.3899 - Rouge2: 6.2409 - Rougel: 16.6172 - Rougelsum: 17.808 - Gen Len: 34.7016 ## 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: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:| | 2.847 | 1.0 | 4710 | 2.7513 | 20.5559 | 5.9762 | 16.1223 | 17.2872 | 35.81 | | 2.6399 | 2.0 | 9420 | 2.6890 | 21.2052 | 6.0104 | 16.5753 | 17.6517 | 34.5242 | | 2.3811 | 3.0 | 14130 | 2.6904 | 21.2358 | 6.1416 | 16.6053 | 17.7067 | 34.6157 | | 2.2388 | 4.0 | 18840 | 2.7112 | 21.3806 | 6.1895 | 16.6909 | 17.7504 | 34.5227 | | 2.1589 | 5.0 | 23550 | 2.7259 | 21.3899 | 6.2409 | 16.6172 | 17.808 | 34.7016 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3
{"tags": ["generated_from_trainer"], "metrics": ["rouge"], "base_model": "google/pegasus-multi_news", "model-index": [{"name": "pegasus-multi_news-commentaries_hdwriter", "results": []}]}
text2text-generation
chinhon/pegasus-multi_news-commentaries_hdwriter
[ "transformers", "pytorch", "tensorboard", "pegasus", "text2text-generation", "generated_from_trainer", "base_model:google/pegasus-multi_news", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[]
TAGS #transformers #pytorch #tensorboard #pegasus #text2text-generation #generated_from_trainer #base_model-google/pegasus-multi_news #autotrain_compatible #endpoints_compatible #has_space #region-us
pegasus-multi\_news-commentaries\_hdwriter ========================================== This model is a fine-tuned version of google/pegasus-multi\_news on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 2.7259 * Rouge1: 21.3899 * Rouge2: 6.2409 * Rougel: 16.6172 * Rougelsum: 17.808 * Gen Len: 34.7016 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: 1 * eval\_batch\_size: 1 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 5 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.15.0 * Pytorch 1.10.0+cu111 * Datasets 1.17.0 * Tokenizers 0.10.3
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3" ]
[ "TAGS\n#transformers #pytorch #tensorboard #pegasus #text2text-generation #generated_from_trainer #base_model-google/pegasus-multi_news #autotrain_compatible #endpoints_compatible #has_space #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3" ]
[ 69, 113, 4, 33 ]
[ "passage: TAGS\n#transformers #pytorch #tensorboard #pegasus #text2text-generation #generated_from_trainer #base_model-google/pegasus-multi_news #autotrain_compatible #endpoints_compatible #has_space #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3" ]
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null
null
transformers
<!-- 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. --> # pegasus-multi_news-headline This model is a fine-tuned version of [google/pegasus-multi_news](https://huggingface.co/google/pegasus-multi_news) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.4421 - Rouge1: 41.616 - Rouge2: 22.922 - Rougel: 35.2189 - Rougelsum: 35.3561 - Gen Len: 33.9532 ## 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: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 1.6637 | 1.0 | 31200 | 1.4877 | 41.0996 | 22.579 | 34.9311 | 35.0611 | 34.3431 | | 1.4395 | 2.0 | 62400 | 1.4388 | 41.6075 | 22.8274 | 35.2051 | 35.3526 | 33.7965 | | 1.3137 | 3.0 | 93600 | 1.4421 | 41.616 | 22.922 | 35.2189 | 35.3561 | 33.9532 | ### Framework versions - Transformers 4.12.2 - Pytorch 1.9.0+cu111 - Datasets 1.14.0 - Tokenizers 0.10.3
{"tags": ["generated_from_trainer"], "metrics": ["rouge"], "base_model": "google/pegasus-multi_news", "model-index": [{"name": "pegasus-multi_news-headline", "results": []}]}
text2text-generation
chinhon/pegasus-multi_news-headline
[ "transformers", "pytorch", "tensorboard", "safetensors", "pegasus", "text2text-generation", "generated_from_trainer", "base_model:google/pegasus-multi_news", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[]
TAGS #transformers #pytorch #tensorboard #safetensors #pegasus #text2text-generation #generated_from_trainer #base_model-google/pegasus-multi_news #autotrain_compatible #endpoints_compatible #has_space #region-us
pegasus-multi\_news-headline ============================ This model is a fine-tuned version of google/pegasus-multi\_news on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 1.4421 * Rouge1: 41.616 * Rouge2: 22.922 * Rougel: 35.2189 * Rougelsum: 35.3561 * Gen Len: 33.9532 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: 1 * eval\_batch\_size: 1 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 3 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.12.2 * Pytorch 1.9.0+cu111 * Datasets 1.14.0 * Tokenizers 0.10.3
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.12.2\n* Pytorch 1.9.0+cu111\n* Datasets 1.14.0\n* Tokenizers 0.10.3" ]
[ "TAGS\n#transformers #pytorch #tensorboard #safetensors #pegasus #text2text-generation #generated_from_trainer #base_model-google/pegasus-multi_news #autotrain_compatible #endpoints_compatible #has_space #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.12.2\n* Pytorch 1.9.0+cu111\n* Datasets 1.14.0\n* Tokenizers 0.10.3" ]
[ 74, 113, 4, 34 ]
[ "passage: TAGS\n#transformers #pytorch #tensorboard #safetensors #pegasus #text2text-generation #generated_from_trainer #base_model-google/pegasus-multi_news #autotrain_compatible #endpoints_compatible #has_space #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.12.2\n* Pytorch 1.9.0+cu111\n* Datasets 1.14.0\n* Tokenizers 0.10.3" ]
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null
null
transformers
<!-- 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. --> # pegasus-multi_news-malay_headlines_02 This model is a fine-tuned version of [google/pegasus-multi_news](https://huggingface.co/google/pegasus-multi_news) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.9295 - Rouge1: 39.9859 - Rouge2: 20.1943 - Rougel: 36.1927 - Rougelsum: 36.2105 - Gen Len: 35.6062 ## 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: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 2.0943 | 1.0 | 53582 | 1.9295 | 39.9859 | 20.1943 | 36.1927 | 36.2105 | 35.6062 | ### Framework versions - Transformers 4.12.3 - Pytorch 1.10.0+cu111 - Datasets 1.15.1 - Tokenizers 0.10.3
{"tags": ["generated_from_trainer"], "metrics": ["rouge"], "base_model": "google/pegasus-multi_news", "model-index": [{"name": "pegasus-multi_news-malay_headlines_02", "results": []}]}
text2text-generation
chinhon/pegasus-multi_news-malay_headlines_02
[ "transformers", "pytorch", "tensorboard", "safetensors", "pegasus", "text2text-generation", "generated_from_trainer", "base_model:google/pegasus-multi_news", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[]
TAGS #transformers #pytorch #tensorboard #safetensors #pegasus #text2text-generation #generated_from_trainer #base_model-google/pegasus-multi_news #autotrain_compatible #endpoints_compatible #has_space #region-us
pegasus-multi\_news-malay\_headlines\_02 ======================================== This model is a fine-tuned version of google/pegasus-multi\_news on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 1.9295 * Rouge1: 39.9859 * Rouge2: 20.1943 * Rougel: 36.1927 * Rougelsum: 36.2105 * Gen Len: 35.6062 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: 1 * eval\_batch\_size: 1 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 1 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.12.3 * Pytorch 1.10.0+cu111 * Datasets 1.15.1 * Tokenizers 0.10.3
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.12.3\n* Pytorch 1.10.0+cu111\n* Datasets 1.15.1\n* Tokenizers 0.10.3" ]
[ "TAGS\n#transformers #pytorch #tensorboard #safetensors #pegasus #text2text-generation #generated_from_trainer #base_model-google/pegasus-multi_news #autotrain_compatible #endpoints_compatible #has_space #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.12.3\n* Pytorch 1.10.0+cu111\n* Datasets 1.15.1\n* Tokenizers 0.10.3" ]
[ 74, 113, 4, 33 ]
[ "passage: TAGS\n#transformers #pytorch #tensorboard #safetensors #pegasus #text2text-generation #generated_from_trainer #base_model-google/pegasus-multi_news #autotrain_compatible #endpoints_compatible #has_space #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.12.3\n* Pytorch 1.10.0+cu111\n* Datasets 1.15.1\n* Tokenizers 0.10.3" ]
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null
null
transformers
<!-- 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. --> # pegasus-multi_news-summarizer_01 This model is a fine-tuned version of [google/pegasus-multi_news](https://huggingface.co/google/pegasus-multi_news) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.2794 - Rouge1: 52.1693 - Rouge2: 34.8989 - Rougel: 41.2385 - Rougelsum: 48.4365 - Gen Len: 98.6433 ## 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: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:| | 1.3936 | 1.0 | 16113 | 1.2972 | 51.5747 | 34.2062 | 40.7279 | 47.7783 | 95.0004 | | 1.3664 | 2.0 | 32226 | 1.2817 | 52.1077 | 34.8189 | 41.1614 | 48.3894 | 100.3265 | | 1.3002 | 3.0 | 48339 | 1.2794 | 52.1693 | 34.8989 | 41.2385 | 48.4365 | 98.6433 | ### Framework versions - Transformers 4.12.3 - Pytorch 1.9.0+cu111 - Datasets 1.15.1 - Tokenizers 0.10.3
{"tags": ["generated_from_trainer"], "metrics": ["rouge"], "base_model": "google/pegasus-multi_news", "model-index": [{"name": "pegasus-multi_news-summarizer_01", "results": []}]}
text2text-generation
chinhon/pegasus-multi_news-summarizer_01
[ "transformers", "pytorch", "tensorboard", "safetensors", "pegasus", "text2text-generation", "generated_from_trainer", "base_model:google/pegasus-multi_news", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[]
TAGS #transformers #pytorch #tensorboard #safetensors #pegasus #text2text-generation #generated_from_trainer #base_model-google/pegasus-multi_news #autotrain_compatible #endpoints_compatible #region-us
pegasus-multi\_news-summarizer\_01 ================================== This model is a fine-tuned version of google/pegasus-multi\_news on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 1.2794 * Rouge1: 52.1693 * Rouge2: 34.8989 * Rougel: 41.2385 * Rougelsum: 48.4365 * Gen Len: 98.6433 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: 1 * eval\_batch\_size: 1 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 3 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.12.3 * Pytorch 1.9.0+cu111 * Datasets 1.15.1 * Tokenizers 0.10.3
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.12.3\n* Pytorch 1.9.0+cu111\n* Datasets 1.15.1\n* Tokenizers 0.10.3" ]
[ "TAGS\n#transformers #pytorch #tensorboard #safetensors #pegasus #text2text-generation #generated_from_trainer #base_model-google/pegasus-multi_news #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.12.3\n* Pytorch 1.9.0+cu111\n* Datasets 1.15.1\n* Tokenizers 0.10.3" ]
[ 70, 113, 4, 34 ]
[ "passage: TAGS\n#transformers #pytorch #tensorboard #safetensors #pegasus #text2text-generation #generated_from_trainer #base_model-google/pegasus-multi_news #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.12.3\n* Pytorch 1.9.0+cu111\n* Datasets 1.15.1\n* Tokenizers 0.10.3" ]
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null
null
transformers
<!-- 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. --> # pegasus-newsroom-commentaries_hdwriter This model is a fine-tuned version of [google/pegasus-newsroom](https://huggingface.co/google/pegasus-newsroom) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.5316 - Rouge1: 21.4079 - Rouge2: 6.2399 - Rougel: 16.6644 - Rougelsum: 17.8501 - Gen Len: 34.4111 ## 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: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:| | 2.6327 | 1.0 | 4710 | 2.5474 | 20.9392 | 6.1702 | 16.3859 | 17.5963 | 35.6626 | | 2.4322 | 2.0 | 9420 | 2.5198 | 21.4026 | 6.1811 | 16.5874 | 17.8207 | 34.5976 | | 2.2703 | 3.0 | 14130 | 2.5316 | 21.4079 | 6.2399 | 16.6644 | 17.8501 | 34.4111 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3
{"tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "pegasus-newsroom-commentaries_hdwriter", "results": []}]}
text2text-generation
chinhon/pegasus-newsroom-commentaries_hdwriter
[ "transformers", "pytorch", "tensorboard", "safetensors", "pegasus", "text2text-generation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[]
TAGS #transformers #pytorch #tensorboard #safetensors #pegasus #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
pegasus-newsroom-commentaries\_hdwriter ======================================= This model is a fine-tuned version of google/pegasus-newsroom on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 2.5316 * Rouge1: 21.4079 * Rouge2: 6.2399 * Rougel: 16.6644 * Rougelsum: 17.8501 * Gen Len: 34.4111 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: 1 * eval\_batch\_size: 1 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 3 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.15.0 * Pytorch 1.10.0+cu111 * Datasets 1.17.0 * Tokenizers 0.10.3
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3" ]
[ "TAGS\n#transformers #pytorch #tensorboard #safetensors #pegasus #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3" ]
[ 56, 113, 4, 33 ]
[ "passage: TAGS\n#transformers #pytorch #tensorboard #safetensors #pegasus #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3" ]
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null
null
transformers
<!-- 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. --> # pegasus-newsroom-headline_writer This model is a fine-tuned version of [google/pegasus-newsroom](https://huggingface.co/google/pegasus-newsroom) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.3988 - Rouge1: 41.8748 - Rouge2: 23.1947 - Rougel: 35.6263 - Rougelsum: 35.7355 - Gen Len: 34.1266 ## 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: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 1.5784 | 1.0 | 31200 | 1.4287 | 41.4257 | 22.9355 | 35.3299 | 35.4648 | 34.4677 | | 1.3501 | 2.0 | 62400 | 1.3955 | 41.9119 | 23.1912 | 35.6698 | 35.7479 | 33.8672 | | 1.2417 | 3.0 | 93600 | 1.3988 | 41.8748 | 23.1947 | 35.6263 | 35.7355 | 34.1266 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.9.0+cu111 - Datasets 1.14.0 - Tokenizers 0.10.3
{"tags": ["generated_from_trainer"], "metrics": ["rouge"], "base_model": "google/pegasus-newsroom", "model-index": [{"name": "pegasus-newsroom-headline_writer", "results": []}]}
text2text-generation
chinhon/pegasus-newsroom-headline_writer
[ "transformers", "pytorch", "tensorboard", "safetensors", "pegasus", "text2text-generation", "generated_from_trainer", "base_model:google/pegasus-newsroom", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[]
TAGS #transformers #pytorch #tensorboard #safetensors #pegasus #text2text-generation #generated_from_trainer #base_model-google/pegasus-newsroom #autotrain_compatible #endpoints_compatible #has_space #region-us
pegasus-newsroom-headline\_writer ================================= This model is a fine-tuned version of google/pegasus-newsroom on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 1.3988 * Rouge1: 41.8748 * Rouge2: 23.1947 * Rougel: 35.6263 * Rougelsum: 35.7355 * Gen Len: 34.1266 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: 1 * eval\_batch\_size: 1 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 3 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.11.3 * Pytorch 1.9.0+cu111 * Datasets 1.14.0 * Tokenizers 0.10.3
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.9.0+cu111\n* Datasets 1.14.0\n* Tokenizers 0.10.3" ]
[ "TAGS\n#transformers #pytorch #tensorboard #safetensors #pegasus #text2text-generation #generated_from_trainer #base_model-google/pegasus-newsroom #autotrain_compatible #endpoints_compatible #has_space #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.9.0+cu111\n* Datasets 1.14.0\n* Tokenizers 0.10.3" ]
[ 73, 113, 4, 34 ]
[ "passage: TAGS\n#transformers #pytorch #tensorboard #safetensors #pegasus #text2text-generation #generated_from_trainer #base_model-google/pegasus-newsroom #autotrain_compatible #endpoints_compatible #has_space #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.9.0+cu111\n* Datasets 1.14.0\n* Tokenizers 0.10.3" ]
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null
null
transformers
<!-- 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. --> # pegasus-newsroom-malay_headlines This model is a fine-tuned version of [google/pegasus-newsroom](https://huggingface.co/google/pegasus-newsroom) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.6603 - Rouge1: 42.6667 - Rouge2: 22.8739 - Rougel: 38.6684 - Rougelsum: 38.6928 - Gen Len: 34.7995 ## 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: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 1.9713 | 1.0 | 15310 | 1.8121 | 41.1469 | 21.5262 | 37.3081 | 37.3377 | 35.0939 | | 1.7917 | 2.0 | 30620 | 1.6913 | 42.4027 | 22.6089 | 38.4471 | 38.4699 | 34.8149 | | 1.7271 | 3.0 | 45930 | 1.6603 | 42.6667 | 22.8739 | 38.6684 | 38.6928 | 34.7995 | ### Framework versions - Transformers 4.12.2 - Pytorch 1.9.0+cu111 - Datasets 1.14.0 - Tokenizers 0.10.3
{"tags": ["generated_from_trainer"], "metrics": ["rouge"], "base_model": "google/pegasus-newsroom", "model-index": [{"name": "pegasus-newsroom-malay_headlines", "results": []}]}
text2text-generation
chinhon/pegasus-newsroom-malay_headlines
[ "transformers", "pytorch", "tensorboard", "pegasus", "text2text-generation", "generated_from_trainer", "base_model:google/pegasus-newsroom", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[]
TAGS #transformers #pytorch #tensorboard #pegasus #text2text-generation #generated_from_trainer #base_model-google/pegasus-newsroom #autotrain_compatible #endpoints_compatible #has_space #region-us
pegasus-newsroom-malay\_headlines ================================= This model is a fine-tuned version of google/pegasus-newsroom on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 1.6603 * Rouge1: 42.6667 * Rouge2: 22.8739 * Rougel: 38.6684 * Rougelsum: 38.6928 * Gen Len: 34.7995 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: 4 * eval\_batch\_size: 4 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 3 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.12.2 * Pytorch 1.9.0+cu111 * Datasets 1.14.0 * Tokenizers 0.10.3
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.12.2\n* Pytorch 1.9.0+cu111\n* Datasets 1.14.0\n* Tokenizers 0.10.3" ]
[ "TAGS\n#transformers #pytorch #tensorboard #pegasus #text2text-generation #generated_from_trainer #base_model-google/pegasus-newsroom #autotrain_compatible #endpoints_compatible #has_space #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.12.2\n* Pytorch 1.9.0+cu111\n* Datasets 1.14.0\n* Tokenizers 0.10.3" ]
[ 68, 113, 4, 34 ]
[ "passage: TAGS\n#transformers #pytorch #tensorboard #pegasus #text2text-generation #generated_from_trainer #base_model-google/pegasus-newsroom #autotrain_compatible #endpoints_compatible #has_space #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.12.2\n* Pytorch 1.9.0+cu111\n* Datasets 1.14.0\n* Tokenizers 0.10.3" ]
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null
null
transformers
<!-- 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. --> # pegasus-newsroom-summarizer_02 This model is a fine-tuned version of [google/pegasus-newsroom](https://huggingface.co/google/pegasus-newsroom) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.2204 - Rouge1: 52.4459 - Rouge2: 35.2568 - Rougel: 41.6213 - Rougelsum: 48.7859 - Gen Len: 98.0627 ## 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: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 1.3231 | 1.0 | 16113 | 1.2305 | 52.1565 | 34.8681 | 41.3189 | 48.4258 | 95.9049 | | 1.3001 | 2.0 | 32226 | 1.2186 | 52.4921 | 35.2661 | 41.6264 | 48.8168 | 98.9241 | | 1.2372 | 3.0 | 48339 | 1.2204 | 52.4459 | 35.2568 | 41.6213 | 48.7859 | 98.0627 | ### Framework versions - Transformers 4.12.3 - Pytorch 1.9.0+cu111 - Datasets 1.15.1 - Tokenizers 0.10.3
{"tags": ["generated_from_trainer"], "metrics": ["rouge"], "base_model": "google/pegasus-newsroom", "model-index": [{"name": "pegasus-newsroom-summarizer_02", "results": []}]}
text2text-generation
chinhon/pegasus-newsroom-summarizer_02
[ "transformers", "pytorch", "tensorboard", "safetensors", "pegasus", "text2text-generation", "generated_from_trainer", "base_model:google/pegasus-newsroom", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[]
TAGS #transformers #pytorch #tensorboard #safetensors #pegasus #text2text-generation #generated_from_trainer #base_model-google/pegasus-newsroom #autotrain_compatible #endpoints_compatible #has_space #region-us
pegasus-newsroom-summarizer\_02 =============================== This model is a fine-tuned version of google/pegasus-newsroom on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 1.2204 * Rouge1: 52.4459 * Rouge2: 35.2568 * Rougel: 41.6213 * Rougelsum: 48.7859 * Gen Len: 98.0627 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: 1 * eval\_batch\_size: 1 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 3 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.12.3 * Pytorch 1.9.0+cu111 * Datasets 1.15.1 * Tokenizers 0.10.3
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.12.3\n* Pytorch 1.9.0+cu111\n* Datasets 1.15.1\n* Tokenizers 0.10.3" ]
[ "TAGS\n#transformers #pytorch #tensorboard #safetensors #pegasus #text2text-generation #generated_from_trainer #base_model-google/pegasus-newsroom #autotrain_compatible #endpoints_compatible #has_space #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.12.3\n* Pytorch 1.9.0+cu111\n* Datasets 1.15.1\n* Tokenizers 0.10.3" ]
[ 73, 113, 4, 34 ]
[ "passage: TAGS\n#transformers #pytorch #tensorboard #safetensors #pegasus #text2text-generation #generated_from_trainer #base_model-google/pegasus-newsroom #autotrain_compatible #endpoints_compatible #has_space #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.12.3\n* Pytorch 1.9.0+cu111\n* Datasets 1.15.1\n* Tokenizers 0.10.3" ]
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null
null
transformers
Chizuru Ichinose DialoGPT Model.
{"tags": ["conversational"]}
text-generation
chip/DialoGPT-small-chizuru
[ "transformers", "pytorch", "gpt2", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[]
TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
Chizuru Ichinose DialoGPT Model.
[]
[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]
[ 51 ]
[ "passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]
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null
null
transformers
### Distibert model finetuned on the task of classifying product descriptions to one of 45 broad [NICE classifications](https://www.wipo.int/classifications/nice/en/)
{}
text-classification
chisadi/nice-distilbert-v2
[ "transformers", "pytorch", "distilbert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[]
TAGS #transformers #pytorch #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us
### Distibert model finetuned on the task of classifying product descriptions to one of 45 broad NICE classifications
[ "### Distibert model finetuned on the task of classifying product descriptions to one of 45 broad NICE classifications" ]
[ "TAGS\n#transformers #pytorch #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n", "### Distibert model finetuned on the task of classifying product descriptions to one of 45 broad NICE classifications" ]
[ 38, 27 ]
[ "passage: TAGS\n#transformers #pytorch #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n### Distibert model finetuned on the task of classifying product descriptions to one of 45 broad NICE classifications" ]
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null
null
transformers
<!-- 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. --> # finetune-paraphrase-model This model is a fine-tuned version of [coderpotter/adversarial-paraphrasing-detector](https://huggingface.co/coderpotter/adversarial-paraphrasing-detector) on an unknown 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: 0.1 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 0.1 | 200 | 3.0116 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3
{"tags": ["generated_from_trainer"], "model-index": [{"name": "finetune-paraphrase-model", "results": []}]}
text-classification
chitra/finetune-paraphrase-model
[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[]
TAGS #transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
finetune-paraphrase-model ========================= This model is a fine-tuned version of coderpotter/adversarial-paraphrasing-detector on an unknown 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: 0.1 ### Training results ### Framework versions * Transformers 4.15.0 * Pytorch 1.10.0+cu111 * Datasets 1.17.0 * Tokenizers 0.10.3
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 0.1", "### Training results", "### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3" ]
[ "TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 0.1", "### Training results", "### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3" ]
[ 48, 98, 4, 33 ]
[ "passage: TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 0.1### Training results### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3" ]
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null
null
transformers
<!-- 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. --> # finetuned-adversarial-paraphrase-model This model is a fine-tuned version of [coderpotter/adversarial-paraphrasing-detector](https://huggingface.co/coderpotter/adversarial-paraphrasing-detector) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 7.5680 ## 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: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.0848 | 1.0 | 2000 | 5.4633 | | 0.0495 | 2.0 | 4000 | 6.0352 | | 0.0121 | 3.0 | 6000 | 7.5680 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3
{"tags": ["generated_from_trainer"], "model-index": [{"name": "finetuned-adversarial-paraphrase-model", "results": []}]}
text-classification
chitra/finetuned-adversarial-paraphrase-model
[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[]
TAGS #transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
finetuned-adversarial-paraphrase-model ====================================== This model is a fine-tuned version of coderpotter/adversarial-paraphrasing-detector on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 7.5680 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: 3 ### Training results ### Framework versions * Transformers 4.15.0 * Pytorch 1.10.0+cu111 * Datasets 1.17.0 * Tokenizers 0.10.3
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3" ]
[ "TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3" ]
[ 48, 98, 4, 33 ]
[ "passage: TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3### Training results### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3" ]
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null
null
transformers
### Welcome to RoBERTArg! 🤖 **Model description** This model was trained on ~25k heterogeneous manually annotated sentences (📚 [Stab et al. 2018](https://www.aclweb.org/anthology/D18-1402/)) of controversial topics to classify text into one of two labels: 🏷 **NON-ARGUMENT** (0) and **ARGUMENT** (1). 🗃 **Dataset** The dataset (📚 Stab et al. 2018) consists of **ARGUMENTS** (\~11k) that either support or oppose a topic if it includes a relevant reason for supporting or opposing the topic, or as a **NON-ARGUMENT** (\~14k) if it does not include reasons. The authors focus on controversial topics, i.e., topics that include "an obvious polarity to the possible outcomes" and compile a final set of eight controversial topics: _abortion, school uniforms, death penalty, marijuana legalization, nuclear energy, cloning, gun control, and minimum wage_. | TOPIC | ARGUMENT | NON-ARGUMENT | |----|----|----| | abortion | 2213 | 2,427 | | school uniforms | 325 | 1,734 | | death penalty | 325 | 2,083 | | marijuana legalization | 325 | 1,262 | | nuclear energy | 325 | 2,118 | | cloning | 325 | 1,494 | | gun control | 325 | 1,889 | | minimum wage | 325 | 1,346 | 🏃🏼‍♂️**Model training** **RoBERTArg** was fine-tuned on a RoBERTA (base) pre-trained model from HuggingFace using the HuggingFace trainer with the following hyperparameters: ``` training_args = TrainingArguments( num_train_epochs=2, learning_rate=2.3102e-06, seed=8, per_device_train_batch_size=64, per_device_eval_batch_size=64, ) ``` 📊 **Evaluation** The model was evaluated on an evaluation set (20%): | Model | Acc | F1 | R arg | R non | P arg | P non | |----|----|----|----|----|----|----| | RoBERTArg | 0.8193 | 0.8021 | 0.8463 | 0.7986 | 0.7623 | 0.8719 | Showing the **confusion matrix** using again the evaluation set: | | ARGUMENT | NON-ARGUMENT | |----|----|----| | ARGUMENT | 2213 | 558 | | NON-ARGUMENT | 325 | 1790 | ⚠️ **Intended Uses & Potential Limitations** The model can only be a starting point to dive into the exciting field of argument mining. But be aware. An argument is a complex structure, with multiple dependencies. Therefore, the model may perform less well on different topics and text types not included in the training set. Enjoy and stay tuned! 🚀 🐦 Twitter: [@chklamm](http://twitter.com/chklamm)
{"language": "en", "widget": [{"text": "It has been determined that the amount of greenhouse gases have decreased by almost half because of the prevalence in the utilization of nuclear power."}]}
text-classification
chkla/roberta-argument
[ "transformers", "pytorch", "tf", "jax", "safetensors", "roberta", "text-classification", "en", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[ "en" ]
TAGS #transformers #pytorch #tf #jax #safetensors #roberta #text-classification #en #autotrain_compatible #endpoints_compatible #has_space #region-us
### Welcome to RoBERTArg! Model description This model was trained on ~25k heterogeneous manually annotated sentences ( Stab et al. 2018) of controversial topics to classify text into one of two labels: NON-ARGUMENT (0) and ARGUMENT (1). Dataset The dataset ( Stab et al. 2018) consists of ARGUMENTS (~11k) that either support or oppose a topic if it includes a relevant reason for supporting or opposing the topic, or as a NON-ARGUMENT (~14k) if it does not include reasons. The authors focus on controversial topics, i.e., topics that include "an obvious polarity to the possible outcomes" and compile a final set of eight controversial topics: *abortion, school uniforms, death penalty, marijuana legalization, nuclear energy, cloning, gun control, and minimum wage*. TOPIC: abortion, ARGUMENT: 2213, NON-ARGUMENT: 2,427 TOPIC: school uniforms, ARGUMENT: 325, NON-ARGUMENT: 1,734 TOPIC: death penalty, ARGUMENT: 325, NON-ARGUMENT: 2,083 TOPIC: marijuana legalization, ARGUMENT: 325, NON-ARGUMENT: 1,262 TOPIC: nuclear energy, ARGUMENT: 325, NON-ARGUMENT: 2,118 TOPIC: cloning, ARGUMENT: 325, NON-ARGUMENT: 1,494 TOPIC: gun control, ARGUMENT: 325, NON-ARGUMENT: 1,889 TOPIC: minimum wage, ARGUMENT: 325, NON-ARGUMENT: 1,346 ‍️Model training RoBERTArg was fine-tuned on a RoBERTA (base) pre-trained model from HuggingFace using the HuggingFace trainer with the following hyperparameters: Evaluation The model was evaluated on an evaluation set (20%): Showing the confusion matrix using again the evaluation set: ARGUMENT: ARGUMENT, NON-ARGUMENT: 2213 ARGUMENT: NON-ARGUMENT, NON-ARGUMENT: 325 ️ Intended Uses & Potential Limitations The model can only be a starting point to dive into the exciting field of argument mining. But be aware. An argument is a complex structure, with multiple dependencies. Therefore, the model may perform less well on different topics and text types not included in the training set. Enjoy and stay tuned! Twitter: @chklamm
[ "### Welcome to RoBERTArg!\n\n\nModel description\n\n\nThis model was trained on ~25k heterogeneous manually annotated sentences ( Stab et al. 2018) of controversial topics to classify text into one of two labels: NON-ARGUMENT (0) and ARGUMENT (1).\n\n\nDataset\n\n\nThe dataset ( Stab et al. 2018) consists of ARGUMENTS (~11k) that either support or oppose a topic if it includes a relevant reason for supporting or opposing the topic, or as a NON-ARGUMENT (~14k) if it does not include reasons. The authors focus on controversial topics, i.e., topics that include \"an obvious polarity to the possible outcomes\" and compile a final set of eight controversial topics: *abortion, school uniforms, death penalty, marijuana legalization, nuclear energy, cloning, gun control, and minimum wage*.\n\n\nTOPIC: abortion, ARGUMENT: 2213, NON-ARGUMENT: 2,427\nTOPIC: school uniforms, ARGUMENT: 325, NON-ARGUMENT: 1,734\nTOPIC: death penalty, ARGUMENT: 325, NON-ARGUMENT: 2,083\nTOPIC: marijuana legalization, ARGUMENT: 325, NON-ARGUMENT: 1,262\nTOPIC: nuclear energy, ARGUMENT: 325, NON-ARGUMENT: 2,118\nTOPIC: cloning, ARGUMENT: 325, NON-ARGUMENT: 1,494\nTOPIC: gun control, ARGUMENT: 325, NON-ARGUMENT: 1,889\nTOPIC: minimum wage, ARGUMENT: 325, NON-ARGUMENT: 1,346\n\n\n‍️Model training\n\n\nRoBERTArg was fine-tuned on a RoBERTA (base) pre-trained model from HuggingFace using the HuggingFace trainer with the following hyperparameters:\n\n\nEvaluation\n\n\nThe model was evaluated on an evaluation set (20%):\n\n\n\nShowing the confusion matrix using again the evaluation set:\n\n\nARGUMENT: ARGUMENT, NON-ARGUMENT: 2213\nARGUMENT: NON-ARGUMENT, NON-ARGUMENT: 325\n\n\n️ Intended Uses & Potential Limitations\n\n\nThe model can only be a starting point to dive into the exciting field of argument mining. But be aware. An argument is a complex structure, with multiple dependencies. Therefore, the model may perform less well on different topics and text types not included in the training set.\n\n\nEnjoy and stay tuned!\n\n\nTwitter: @chklamm" ]
[ "TAGS\n#transformers #pytorch #tf #jax #safetensors #roberta #text-classification #en #autotrain_compatible #endpoints_compatible #has_space #region-us \n", "### Welcome to RoBERTArg!\n\n\nModel description\n\n\nThis model was trained on ~25k heterogeneous manually annotated sentences ( Stab et al. 2018) of controversial topics to classify text into one of two labels: NON-ARGUMENT (0) and ARGUMENT (1).\n\n\nDataset\n\n\nThe dataset ( Stab et al. 2018) consists of ARGUMENTS (~11k) that either support or oppose a topic if it includes a relevant reason for supporting or opposing the topic, or as a NON-ARGUMENT (~14k) if it does not include reasons. The authors focus on controversial topics, i.e., topics that include \"an obvious polarity to the possible outcomes\" and compile a final set of eight controversial topics: *abortion, school uniforms, death penalty, marijuana legalization, nuclear energy, cloning, gun control, and minimum wage*.\n\n\nTOPIC: abortion, ARGUMENT: 2213, NON-ARGUMENT: 2,427\nTOPIC: school uniforms, ARGUMENT: 325, NON-ARGUMENT: 1,734\nTOPIC: death penalty, ARGUMENT: 325, NON-ARGUMENT: 2,083\nTOPIC: marijuana legalization, ARGUMENT: 325, NON-ARGUMENT: 1,262\nTOPIC: nuclear energy, ARGUMENT: 325, NON-ARGUMENT: 2,118\nTOPIC: cloning, ARGUMENT: 325, NON-ARGUMENT: 1,494\nTOPIC: gun control, ARGUMENT: 325, NON-ARGUMENT: 1,889\nTOPIC: minimum wage, ARGUMENT: 325, NON-ARGUMENT: 1,346\n\n\n‍️Model training\n\n\nRoBERTArg was fine-tuned on a RoBERTA (base) pre-trained model from HuggingFace using the HuggingFace trainer with the following hyperparameters:\n\n\nEvaluation\n\n\nThe model was evaluated on an evaluation set (20%):\n\n\n\nShowing the confusion matrix using again the evaluation set:\n\n\nARGUMENT: ARGUMENT, NON-ARGUMENT: 2213\nARGUMENT: NON-ARGUMENT, NON-ARGUMENT: 325\n\n\n️ Intended Uses & Potential Limitations\n\n\nThe model can only be a starting point to dive into the exciting field of argument mining. But be aware. An argument is a complex structure, with multiple dependencies. Therefore, the model may perform less well on different topics and text types not included in the training set.\n\n\nEnjoy and stay tuned!\n\n\nTwitter: @chklamm" ]
[ 54, 558 ]
[ "passage: TAGS\n#transformers #pytorch #tf #jax #safetensors #roberta #text-classification #en #autotrain_compatible #endpoints_compatible #has_space #region-us \n" ]
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null
null
transformers
<!-- 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. --> # This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the OPENSLR_SLR66 - NA dataset. It achieves the following results on the evaluation set: - Loss: 0.3119 - Wer: 0.2613 ### Evaluation metrics | Metric | Split | Decode with LM | Value | |:------:|:------:|:--------------:|:---------:| | WER | Train | No | 5.36 | | CER | Train | No | 1.11 | | WER | Test | No | 26.14 | | CER | Test | No | 4.93 | | WER | Train | Yes | 5.04 | | CER | Train | Yes | 1.07 | | WER | Test | Yes | 20.69 | | CER | Test | Yes | 3.986 | ## 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: 4 - seed: 42 - gradient_accumulation_steps: 2 - 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: 2000 - num_epochs: 150.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:-----:|:---------------:|:------:| | 2.9038 | 4.8 | 500 | 3.0125 | 1.0 | | 1.3777 | 9.61 | 1000 | 0.8681 | 0.8753 | | 1.1436 | 14.42 | 1500 | 0.6256 | 0.7961 | | 1.0997 | 19.23 | 2000 | 0.5244 | 0.6875 | | 1.0363 | 24.04 | 2500 | 0.4585 | 0.6276 | | 0.7996 | 28.84 | 3000 | 0.4072 | 0.5295 | | 0.825 | 33.65 | 3500 | 0.3590 | 0.5222 | | 0.8018 | 38.46 | 4000 | 0.3678 | 0.4671 | | 0.7545 | 43.27 | 4500 | 0.3474 | 0.3962 | | 0.7375 | 48.08 | 5000 | 0.3224 | 0.3869 | | 0.6198 | 52.88 | 5500 | 0.3233 | 0.3630 | | 0.6608 | 57.69 | 6000 | 0.3029 | 0.3308 | | 0.645 | 62.5 | 6500 | 0.3195 | 0.3722 | | 0.5249 | 67.31 | 7000 | 0.3004 | 0.3202 | | 0.4875 | 72.11 | 7500 | 0.2826 | 0.2992 | | 0.5171 | 76.92 | 8000 | 0.2962 | 0.2976 | | 0.4974 | 81.73 | 8500 | 0.2990 | 0.2933 | | 0.4387 | 86.54 | 9000 | 0.2834 | 0.2755 | | 0.4511 | 91.34 | 9500 | 0.2886 | 0.2787 | | 0.4112 | 96.15 | 10000 | 0.3093 | 0.2976 | | 0.4064 | 100.96 | 10500 | 0.3123 | 0.2863 | | 0.4047 | 105.77 | 11000 | 0.2968 | 0.2719 | | 0.3519 | 110.57 | 11500 | 0.3106 | 0.2832 | | 0.3719 | 115.38 | 12000 | 0.3030 | 0.2737 | | 0.3669 | 120.19 | 12500 | 0.2964 | 0.2714 | | 0.3386 | 125.0 | 13000 | 0.3101 | 0.2714 | | 0.3137 | 129.8 | 13500 | 0.3063 | 0.2710 | | 0.3008 | 134.61 | 14000 | 0.3082 | 0.2617 | | 0.301 | 139.42 | 14500 | 0.3121 | 0.2628 | | 0.3291 | 144.23 | 15000 | 0.3105 | 0.2612 | | 0.3133 | 149.04 | 15500 | 0.3114 | 0.2624 | ### Framework versions - Transformers 4.16.0.dev0 - Pytorch 1.10.1+cu102 - Datasets 1.17.1.dev0 - Tokenizers 0.11.0
{"language": ["te"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "openslr_SLR66", "generated_from_trainer", "robust-speech-event", "hf-asr-leaderboard"], "datasets": ["openslr", "SLR66"], "metrics": ["wer"], "model-index": [{"name": "xls-r-1B-te", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Open SLR", "type": "openslr", "args": "SLR66"}, "metrics": [{"type": "wer", "value": 20.624, "name": "Test WER"}, {"type": "cer", "value": 3.979, "name": "Test CER"}, {"type": "wer", "value": 26.14777618364419, "name": "Test WER (without LM)"}, {"type": "cer", "value": 4.932543184970369, "name": "Test CER (without LM)"}]}]}]}
automatic-speech-recognition
chmanoj/xls-r-1B-te
[ "transformers", "pytorch", "tensorboard", "safetensors", "wav2vec2", "automatic-speech-recognition", "openslr_SLR66", "generated_from_trainer", "robust-speech-event", "hf-asr-leaderboard", "te", "dataset:openslr", "dataset:SLR66", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[ "te" ]
TAGS #transformers #pytorch #tensorboard #safetensors #wav2vec2 #automatic-speech-recognition #openslr_SLR66 #generated_from_trainer #robust-speech-event #hf-asr-leaderboard #te #dataset-openslr #dataset-SLR66 #license-apache-2.0 #model-index #endpoints_compatible #region-us
This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the OPENSLR\_SLR66 - NA dataset. It achieves the following results on the evaluation set: * Loss: 0.3119 * Wer: 0.2613 ### Evaluation metrics 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: 4 * seed: 42 * gradient\_accumulation\_steps: 2 * 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: 2000 * num\_epochs: 150.0 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.16.0.dev0 * Pytorch 1.10.1+cu102 * Datasets 1.17.1.dev0 * Tokenizers 0.11.0
[ "### Evaluation metrics\n\n\n\nModel description\n-----------------\n\n\nMore information needed\n\n\nIntended uses & limitations\n---------------------------\n\n\nMore information needed\n\n\nTraining and evaluation data\n----------------------------\n\n\nMore information needed\n\n\nTraining procedure\n------------------", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 2000\n* num\\_epochs: 150.0\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.16.0.dev0\n* Pytorch 1.10.1+cu102\n* Datasets 1.17.1.dev0\n* Tokenizers 0.11.0" ]
[ "TAGS\n#transformers #pytorch #tensorboard #safetensors #wav2vec2 #automatic-speech-recognition #openslr_SLR66 #generated_from_trainer #robust-speech-event #hf-asr-leaderboard #te #dataset-openslr #dataset-SLR66 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n", "### Evaluation metrics\n\n\n\nModel description\n-----------------\n\n\nMore information needed\n\n\nIntended uses & limitations\n---------------------------\n\n\nMore information needed\n\n\nTraining and evaluation data\n----------------------------\n\n\nMore information needed\n\n\nTraining procedure\n------------------", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 2000\n* num\\_epochs: 150.0\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.16.0.dev0\n* Pytorch 1.10.1+cu102\n* Datasets 1.17.1.dev0\n* Tokenizers 0.11.0" ]
[ 107, 41, 160, 4, 41 ]
[ "passage: TAGS\n#transformers #pytorch #tensorboard #safetensors #wav2vec2 #automatic-speech-recognition #openslr_SLR66 #generated_from_trainer #robust-speech-event #hf-asr-leaderboard #te #dataset-openslr #dataset-SLR66 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n### Evaluation metrics\n\n\n\nModel description\n-----------------\n\n\nMore information needed\n\n\nIntended uses & limitations\n---------------------------\n\n\nMore information needed\n\n\nTraining and evaluation data\n----------------------------\n\n\nMore information needed\n\n\nTraining procedure\n------------------### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 2000\n* num\\_epochs: 150.0\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.16.0.dev0\n* Pytorch 1.10.1+cu102\n* Datasets 1.17.1.dev0\n* Tokenizers 0.11.0" ]
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null
null
transformers
<!-- 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. --> # This model is a fine-tuned version of [facebook/wav2vec2-xls-r-2b](https://huggingface.co/facebook/wav2vec2-xls-r-2b) on the OPENSLR_SLR66 - NA dataset. It achieves the following results on the evaluation set: - Loss: 0.4253 - Wer: 0.5109 ### Evaluation metrics | Metric | Split | Decode with LM | Value | |:------:|:------:|:--------------:|:---------:| | WER | Train | No | | | CER | Train | No | | | WER | Test | No | | | CER | Test | No | | | WER | Train | Yes | | | CER | Train | Yes | | | WER | Test | Yes | | | CER | Test | Yes | | ## 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: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 12 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - learning_rate: 3e-6 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 150.0 - hidden_dropout: 0.15 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.16.0.dev0 - Pytorch 1.10.1+cu102 - Datasets 1.17.1.dev0 - Tokenizers 0.11.0
{"language": ["te"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "openslr_SLR66", "generated_from_trainer", "robust-speech-event", "hf-asr-leaderboard"], "datasets": ["openslr", "SLR66"], "metrics": ["wer"], "model-index": [{"name": "xls-r-1B-te", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Open SLR", "type": "openslr", "args": "SLR66"}, "metrics": [{"type": "wer", "value": 0.51, "name": "Test WER"}, {"type": "cer", "value": 0.097, "name": "Test CER"}]}]}]}
automatic-speech-recognition
chmanoj/xls-r-2B-te
[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "openslr_SLR66", "generated_from_trainer", "robust-speech-event", "hf-asr-leaderboard", "te", "dataset:openslr", "dataset:SLR66", "license:apache-2.0", "model-index", "endpoints_compatible", "has_space", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[ "te" ]
TAGS #transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #openslr_SLR66 #generated_from_trainer #robust-speech-event #hf-asr-leaderboard #te #dataset-openslr #dataset-SLR66 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us
This model is a fine-tuned version of facebook/wav2vec2-xls-r-2b on the OPENSLR\_SLR66 - NA dataset. It achieves the following results on the evaluation set: * Loss: 0.4253 * Wer: 0.5109 ### Evaluation metrics 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: 4 * eval\_batch\_size: 4 * seed: 42 * gradient\_accumulation\_steps: 12 * total\_train\_batch\_size: 64 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * learning\_rate: 3e-6 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_steps: 2000 * num\_epochs: 150.0 * hidden\_dropout: 0.15 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.16.0.dev0 * Pytorch 1.10.1+cu102 * Datasets 1.17.1.dev0 * Tokenizers 0.11.0
[ "### Evaluation metrics\n\n\n\nModel description\n-----------------\n\n\nMore information needed\n\n\nIntended uses & limitations\n---------------------------\n\n\nMore information needed\n\n\nTraining and evaluation data\n----------------------------\n\n\nMore information needed\n\n\nTraining procedure\n------------------", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 12\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* learning\\_rate: 3e-6\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 2000\n* num\\_epochs: 150.0\n* hidden\\_dropout: 0.15\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.16.0.dev0\n* Pytorch 1.10.1+cu102\n* Datasets 1.17.1.dev0\n* Tokenizers 0.11.0" ]
[ "TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #openslr_SLR66 #generated_from_trainer #robust-speech-event #hf-asr-leaderboard #te #dataset-openslr #dataset-SLR66 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us \n", "### Evaluation metrics\n\n\n\nModel description\n-----------------\n\n\nMore information needed\n\n\nIntended uses & limitations\n---------------------------\n\n\nMore information needed\n\n\nTraining and evaluation data\n----------------------------\n\n\nMore information needed\n\n\nTraining procedure\n------------------", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 12\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* learning\\_rate: 3e-6\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 2000\n* num\\_epochs: 150.0\n* hidden\\_dropout: 0.15\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.16.0.dev0\n* Pytorch 1.10.1+cu102\n* Datasets 1.17.1.dev0\n* Tokenizers 0.11.0" ]
[ 106, 41, 178, 4, 41 ]
[ "passage: TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #openslr_SLR66 #generated_from_trainer #robust-speech-event #hf-asr-leaderboard #te #dataset-openslr #dataset-SLR66 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us \n### Evaluation metrics\n\n\n\nModel description\n-----------------\n\n\nMore information needed\n\n\nIntended uses & limitations\n---------------------------\n\n\nMore information needed\n\n\nTraining and evaluation data\n----------------------------\n\n\nMore information needed\n\n\nTraining procedure\n------------------### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 12\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* learning\\_rate: 3e-6\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 2000\n* num\\_epochs: 150.0\n* hidden\\_dropout: 0.15\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.16.0.dev0\n* Pytorch 1.10.1+cu102\n* Datasets 1.17.1.dev0\n* Tokenizers 0.11.0" ]
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null
null
transformers
<!-- 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. --> # 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_7_0 - SV-SE dataset. It achieves the following results on the evaluation set: - Loss: 0.8004 - Wer: 0.7139 ## 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: 7.5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 8 - 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: 2000 - num_epochs: 10.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 2.6683 | 1.45 | 500 | 1.7698 | 1.0041 | | 1.9548 | 2.91 | 1000 | 1.0890 | 0.8602 | | 1.9568 | 4.36 | 1500 | 1.0878 | 0.8680 | | 1.9497 | 5.81 | 2000 | 1.1501 | 0.8838 | | 1.8453 | 7.27 | 2500 | 1.0452 | 0.8418 | | 1.6952 | 8.72 | 3000 | 0.9153 | 0.7823 | ### Framework versions - Transformers 4.16.0.dev0 - Pytorch 1.10.0+cu113 - Datasets 1.18.1.dev0 - Tokenizers 0.10.3
{"language": ["sv-SE"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_7_0", "generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "", "results": []}]}
automatic-speech-recognition
chmanoj/xls-r-300m-sv
[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "mozilla-foundation/common_voice_7_0", "generated_from_trainer", "dataset:common_voice", "license:apache-2.0", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[ "sv-SE" ]
TAGS #transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_7_0 #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_7\_0 - SV-SE dataset. It achieves the following results on the evaluation set: * Loss: 0.8004 * Wer: 0.7139 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: 7.5e-05 * train\_batch\_size: 4 * eval\_batch\_size: 4 * seed: 42 * gradient\_accumulation\_steps: 8 * 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: 2000 * num\_epochs: 10.0 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.16.0.dev0 * Pytorch 1.10.0+cu113 * Datasets 1.18.1.dev0 * Tokenizers 0.10.3
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7.5e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 2000\n* num\\_epochs: 10.0\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.16.0.dev0\n* Pytorch 1.10.0+cu113\n* Datasets 1.18.1.dev0\n* Tokenizers 0.10.3" ]
[ "TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_7_0 #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7.5e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 2000\n* num\\_epochs: 10.0\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.16.0.dev0\n* Pytorch 1.10.0+cu113\n* Datasets 1.18.1.dev0\n* Tokenizers 0.10.3" ]
[ 77, 160, 4, 41 ]
[ "passage: TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_7_0 #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7.5e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 2000\n* num\\_epochs: 10.0\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.16.0.dev0\n* Pytorch 1.10.0+cu113\n* Datasets 1.18.1.dev0\n* Tokenizers 0.10.3" ]
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null
null
transformers
<!-- 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. --> # This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the OPENSLR_SLR66 - NA dataset. It achieves the following results on the evaluation set: - Loss: 0.2680 - Wer: 0.3467 ## 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: 7.5e-05 - train_batch_size: 16 - eval_batch_size: 16 - 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: 2000 - num_epochs: 10.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 3.0304 | 4.81 | 500 | 1.5676 | 1.0554 | | 1.5263 | 9.61 | 1000 | 0.4693 | 0.8023 | | 1.5299 | 14.42 | 1500 | 0.4368 | 0.7311 | | 1.5063 | 19.23 | 2000 | 0.4360 | 0.7302 | | 1.455 | 24.04 | 2500 | 0.4213 | 0.6692 | | 1.4755 | 28.84 | 3000 | 0.4329 | 0.5943 | | 1.352 | 33.65 | 3500 | 0.4074 | 0.5765 | | 1.3122 | 38.46 | 4000 | 0.3866 | 0.5630 | | 1.2799 | 43.27 | 4500 | 0.3860 | 0.5480 | | 1.212 | 48.08 | 5000 | 0.3590 | 0.5317 | | 1.1645 | 52.88 | 5500 | 0.3283 | 0.4757 | | 1.0854 | 57.69 | 6000 | 0.3162 | 0.4687 | | 1.0292 | 62.5 | 6500 | 0.3126 | 0.4416 | | 0.9607 | 67.31 | 7000 | 0.2990 | 0.4066 | | 0.9156 | 72.12 | 7500 | 0.2870 | 0.4009 | | 0.8329 | 76.92 | 8000 | 0.2791 | 0.3909 | | 0.7979 | 81.73 | 8500 | 0.2770 | 0.3670 | | 0.7144 | 86.54 | 9000 | 0.2841 | 0.3661 | | 0.6997 | 91.35 | 9500 | 0.2721 | 0.3485 | | 0.6568 | 96.15 | 10000 | 0.2681 | 0.3437 | ### Framework versions - Transformers 4.16.0.dev0 - Pytorch 1.10.1+cu102 - Datasets 1.17.1.dev0 - Tokenizers 0.11.0
{"language": ["te"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "openslr_SLR66", "generated_from_trainer", "robust-speech-event", "hf-asr-leaderboard"], "datasets": ["openslr", "SLR66"], "metrics": ["wer"], "model-index": [{"name": "xls-r-300m-te", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Open SLR", "type": "openslr", "args": "SLR66"}, "metrics": [{"type": "wer", "value": 24.695121951219512, "name": "Test WER"}, {"type": "cer", "value": 4.861934182322532, "name": "Test CER"}]}]}]}
automatic-speech-recognition
chmanoj/xls-r-300m-te
[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "openslr_SLR66", "generated_from_trainer", "robust-speech-event", "hf-asr-leaderboard", "te", "dataset:openslr", "dataset:SLR66", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[ "te" ]
TAGS #transformers #pytorch #wav2vec2 #automatic-speech-recognition #openslr_SLR66 #generated_from_trainer #robust-speech-event #hf-asr-leaderboard #te #dataset-openslr #dataset-SLR66 #license-apache-2.0 #model-index #endpoints_compatible #region-us
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the OPENSLR\_SLR66 - NA dataset. It achieves the following results on the evaluation set: * Loss: 0.2680 * Wer: 0.3467 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: 7.5e-05 * train\_batch\_size: 16 * eval\_batch\_size: 16 * 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: 2000 * num\_epochs: 10.0 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.16.0.dev0 * Pytorch 1.10.1+cu102 * Datasets 1.17.1.dev0 * Tokenizers 0.11.0
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7.5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 2000\n* num\\_epochs: 10.0\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.16.0.dev0\n* Pytorch 1.10.1+cu102\n* Datasets 1.17.1.dev0\n* Tokenizers 0.11.0" ]
[ "TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #openslr_SLR66 #generated_from_trainer #robust-speech-event #hf-asr-leaderboard #te #dataset-openslr #dataset-SLR66 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7.5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 2000\n* num\\_epochs: 10.0\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.16.0.dev0\n* Pytorch 1.10.1+cu102\n* Datasets 1.17.1.dev0\n* Tokenizers 0.11.0" ]
[ 98, 160, 4, 41 ]
[ "passage: TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #openslr_SLR66 #generated_from_trainer #robust-speech-event #hf-asr-leaderboard #te #dataset-openslr #dataset-SLR66 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7.5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 2000\n* num\\_epochs: 10.0\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.16.0.dev0\n* Pytorch 1.10.1+cu102\n* Datasets 1.17.1.dev0\n* Tokenizers 0.11.0" ]
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null
null
transformers
<!-- 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. --> # This model is a fine-tuned version of [hf-test/xls-r-dummy](https://huggingface.co/hf-test/xls-r-dummy) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - AB dataset. It achieves the following results on the evaluation set: - Loss: 156.8786 - Wer: 1.3460 ## 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: 2 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.16.0.dev0 - Pytorch 1.10.0+cu113 - Datasets 1.18.1.dev0 - Tokenizers 0.10.3
{"language": ["ab"], "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_7_0", "generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "", "results": []}]}
automatic-speech-recognition
chmanoj/xls-r-demo-test
[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "mozilla-foundation/common_voice_7_0", "generated_from_trainer", "ab", "dataset:common_voice", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[ "ab" ]
TAGS #transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_7_0 #generated_from_trainer #ab #dataset-common_voice #endpoints_compatible #region-us
# This model is a fine-tuned version of hf-test/xls-r-dummy on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - AB dataset. It achieves the following results on the evaluation set: - Loss: 156.8786 - Wer: 1.3460 ## 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: 2 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.16.0.dev0 - Pytorch 1.10.0+cu113 - Datasets 1.18.1.dev0 - Tokenizers 0.10.3
[ "# \n\nThis model is a fine-tuned version of hf-test/xls-r-dummy on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - AB dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 156.8786\n- Wer: 1.3460", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0003\n- train_batch_size: 2\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- training_steps: 10\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\n\n- Transformers 4.16.0.dev0\n- Pytorch 1.10.0+cu113\n- Datasets 1.18.1.dev0\n- Tokenizers 0.10.3" ]
[ "TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_7_0 #generated_from_trainer #ab #dataset-common_voice #endpoints_compatible #region-us \n", "# \n\nThis model is a fine-tuned version of hf-test/xls-r-dummy on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - AB dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 156.8786\n- Wer: 1.3460", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0003\n- train_batch_size: 2\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- training_steps: 10\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\n\n- Transformers 4.16.0.dev0\n- Pytorch 1.10.0+cu113\n- Datasets 1.18.1.dev0\n- Tokenizers 0.10.3" ]
[ 71, 70, 6, 12, 8, 3, 101, 4, 41 ]
[ "passage: TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_7_0 #generated_from_trainer #ab #dataset-common_voice #endpoints_compatible #region-us \n# \n\nThis model is a fine-tuned version of hf-test/xls-r-dummy on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - AB dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 156.8786\n- Wer: 1.3460## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0003\n- train_batch_size: 2\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- training_steps: 10\n- mixed_precision_training: Native AMP### Training results### Framework versions\n\n- Transformers 4.16.0.dev0\n- Pytorch 1.10.0+cu113\n- Datasets 1.18.1.dev0\n- Tokenizers 0.10.3" ]
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null
null
transformers
# Wav2Vec2-Large-XLSR-53 in Thai Language (Train with deepcut tokenizer)
{"language": "th", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning"], "datasets": ["common_voice"]}
automatic-speech-recognition
chompk/wav2vec2-large-xlsr-thai-tokenized
[ "transformers", "pytorch", "jax", "wav2vec2", "automatic-speech-recognition", "audio", "speech", "xlsr-fine-tuning", "th", "dataset:common_voice", "license:apache-2.0", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[ "th" ]
TAGS #transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning #th #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
# Wav2Vec2-Large-XLSR-53 in Thai Language (Train with deepcut tokenizer)
[ "# Wav2Vec2-Large-XLSR-53 in Thai Language (Train with deepcut tokenizer)" ]
[ "TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning #th #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n", "# Wav2Vec2-Large-XLSR-53 in Thai Language (Train with deepcut tokenizer)" ]
[ 74, 27 ]
[ "passage: TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning #th #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n# Wav2Vec2-Large-XLSR-53 in Thai Language (Train with deepcut tokenizer)" ]
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null
null
transformers
Test English-Dhivehi/Dhivehi-English NMT Would need a lot more data to get accurate translations.
{}
text2text-generation
chopey/testmntdv
[ "transformers", "pytorch", "mt5", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[]
TAGS #transformers #pytorch #mt5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
Test English-Dhivehi/Dhivehi-English NMT Would need a lot more data to get accurate translations.
[]
[ "TAGS\n#transformers #pytorch #mt5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]
[ 49 ]
[ "passage: TAGS\n#transformers #pytorch #mt5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]
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null
null
null
These models were made for my course project in NLP and AI special course at the University of Latvia during my first semester of study.
{}
null
chrisAS12/specseminars
[ "region:us" ]
2022-03-02T23:29:05+00:00
[]
[]
TAGS #region-us
These models were made for my course project in NLP and AI special course at the University of Latvia during my first semester of study.
[]
[ "TAGS\n#region-us \n" ]
[ 6 ]
[ "passage: TAGS\n#region-us \n" ]
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null
transformers
# Wav2Vec2-Large-XLSR-53-Fon Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on [Fon (or Fongbe)](https://en.wikipedia.org/wiki/Fon_language) using the [Fon Dataset](https://github.com/laleye/pyFongbe/tree/master/data). When using this model, make sure that your speech input is sampled at 16kHz. ## Usage The model can be used directly (without a language model) as follows: ```python import json import random import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor #Load test_dataset from saved files in folder from datasets import load_dataset, load_metric #for test for root, dirs, files in os.walk(test/): test_dataset= load_dataset("json", data_files=[os.path.join(root,i) for i in files],split="train") #Remove unnecessary chars chars_to_ignore_regex = '[\\,\\?\\.\\!\\-\\;\\:\\"\\“\\%\\‘\\”]' def remove_special_characters(batch): batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() + " " return batch test_dataset = test_dataset.map(remove_special_characters) processor = Wav2Vec2Processor.from_pretrained("chrisjay/wav2vec2-large-xlsr-53-fon") model = Wav2Vec2ForCTC.from_pretrained("chrisjay/wav2vec2-large-xlsr-53-fon") #No need for resampling because audio dataset already at 16kHz #resampler = torchaudio.transforms.Resample(48_000, 16_000) # Preprocessing the datasets. # We need to read the audio files as arrays def speech_file_to_array_fn(batch): speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"]=speech_array.squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): tlogits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits predicted_ids = torch.argmax(logits, dim=-1) print("Prediction:", processor.batch_decode(predicted_ids)) print("Reference:", test_dataset["sentence"][:2]) ``` ## Evaluation The model can be evaluated as follows on our unique Fon test data. ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re for root, dirs, files in os.walk(test/): test_dataset = load_dataset("json", data_files=[os.path.join(root,i) for i in files],split="train") chars_to_ignore_regex = '[\\,\\?\\.\\!\\-\\;\\:\\"\\“\\%\\‘\\”]' batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() + " " return batch test_dataset = test_dataset.map(remove_special_characters) wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("chrisjay/wav2vec2-large-xlsr-53-fon") model = Wav2Vec2ForCTC.from_pretrained("chrisjay/wav2vec2-large-xlsr-53-fon") model.to("cuda") # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = speech_array[0].numpy() batch["sampling_rate"] = sampling_rate batch["target_text"] = batch["sentence"] return batch test_dataset = test_dataset.map(speech_file_to_array_fn) #Evaluation on test dataset def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch.argmax(logits, dim=-1) batch["pred_strings"] = processor.batch_decode(pred_ids) return batch result = test_dataset.map(evaluate, batched=True, batch_size=8) print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"]))) ``` **Test Result**: 14.97 % ## Training The [Fon dataset](https://github.com/laleye/pyFongbe/tree/master/data) was split into `train`(8235 samples), `validation`(1107 samples), and `test`(1061 samples). The script used for training can be found [here](https://colab.research.google.com/drive/11l6qhJCYnPTG1TQZ8f3EvKB9z12TQi4g?usp=sharing) # Collaborators on this project - Chris C. Emezue ([Twitter](https://twitter.com/ChrisEmezue))|([email protected]) - Bonaventure F.P. Dossou (HuggingFace Username: [bonadossou](https://huggingface.co/bonadossou))|([Twitter](https://twitter.com/bonadossou))|([email protected]) ## This is a joint project continuing our research on [OkwuGbé: End-to-End Speech Recognition for Fon and Igbo](https://arxiv.org/abs/2103.07762)
{"language": "fon", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week", "hf-asr-leaderboard"], "datasets": ["fon_dataset"], "metrics": ["wer"], "model-index": [{"name": "Fon XLSR Wav2Vec2 Large 53", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "fon", "type": "fon_dataset", "args": "fon"}, "metrics": [{"type": "wer", "value": 14.97, "name": "Test WER"}]}]}]}
automatic-speech-recognition
chrisjay/fonxlsr
[ "transformers", "pytorch", "jax", "wav2vec2", "automatic-speech-recognition", "audio", "speech", "xlsr-fine-tuning-week", "hf-asr-leaderboard", "fon", "dataset:fon_dataset", "arxiv:2103.07762", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[ "2103.07762" ]
[ "fon" ]
TAGS #transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #hf-asr-leaderboard #fon #dataset-fon_dataset #arxiv-2103.07762 #license-apache-2.0 #model-index #endpoints_compatible #region-us
# Wav2Vec2-Large-XLSR-53-Fon Fine-tuned facebook/wav2vec2-large-xlsr-53 on Fon (or Fongbe) using the Fon Dataset. When using this model, make sure that your speech input is sampled at 16kHz. ## Usage The model can be used directly (without a language model) as follows: ## Evaluation The model can be evaluated as follows on our unique Fon test data. Test Result: 14.97 % ## Training The Fon dataset was split into 'train'(8235 samples), 'validation'(1107 samples), and 'test'(1061 samples). The script used for training can be found here # Collaborators on this project - Chris C. Emezue (Twitter)|(URL@URL) - Bonaventure F.P. Dossou (HuggingFace Username: bonadossou)|(Twitter)|(URL@URL) ## This is a joint project continuing our research on OkwuGbé: End-to-End Speech Recognition for Fon and Igbo
[ "# Wav2Vec2-Large-XLSR-53-Fon\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Fon (or Fongbe) using the Fon Dataset.\n\nWhen using this model, make sure that your speech input is sampled at 16kHz.", "## Usage\n\nThe model can be used directly (without a language model) as follows:", "## Evaluation\n\nThe model can be evaluated as follows on our unique Fon test data. \n\n\n\nTest Result: 14.97 %", "## Training\n\nThe Fon dataset was split into 'train'(8235 samples), 'validation'(1107 samples), and 'test'(1061 samples).\n\nThe script used for training can be found here", "# Collaborators on this project\n\n - Chris C. Emezue (Twitter)|(URL@URL)\n - Bonaventure F.P. Dossou (HuggingFace Username: bonadossou)|(Twitter)|(URL@URL)", "## This is a joint project continuing our research on OkwuGbé: End-to-End Speech Recognition for Fon and Igbo" ]
[ "TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #hf-asr-leaderboard #fon #dataset-fon_dataset #arxiv-2103.07762 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n", "# Wav2Vec2-Large-XLSR-53-Fon\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Fon (or Fongbe) using the Fon Dataset.\n\nWhen using this model, make sure that your speech input is sampled at 16kHz.", "## Usage\n\nThe model can be used directly (without a language model) as follows:", "## Evaluation\n\nThe model can be evaluated as follows on our unique Fon test data. \n\n\n\nTest Result: 14.97 %", "## Training\n\nThe Fon dataset was split into 'train'(8235 samples), 'validation'(1107 samples), and 'test'(1061 samples).\n\nThe script used for training can be found here", "# Collaborators on this project\n\n - Chris C. Emezue (Twitter)|(URL@URL)\n - Bonaventure F.P. Dossou (HuggingFace Username: bonadossou)|(Twitter)|(URL@URL)", "## This is a joint project continuing our research on OkwuGbé: End-to-End Speech Recognition for Fon and Igbo" ]
[ 97, 69, 20, 25, 47, 54, 30 ]
[ "passage: TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #hf-asr-leaderboard #fon #dataset-fon_dataset #arxiv-2103.07762 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n# Wav2Vec2-Large-XLSR-53-Fon\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Fon (or Fongbe) using the Fon Dataset.\n\nWhen using this model, make sure that your speech input is sampled at 16kHz.## Usage\n\nThe model can be used directly (without a language model) as follows:## Evaluation\n\nThe model can be evaluated as follows on our unique Fon test data. \n\n\n\nTest Result: 14.97 %## Training\n\nThe Fon dataset was split into 'train'(8235 samples), 'validation'(1107 samples), and 'test'(1061 samples).\n\nThe script used for training can be found here# Collaborators on this project\n\n - Chris C. Emezue (Twitter)|(URL@URL)\n - Bonaventure F.P. Dossou (HuggingFace Username: bonadossou)|(Twitter)|(URL@URL)## This is a joint project continuing our research on OkwuGbé: End-to-End Speech Recognition for Fon and Igbo" ]
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# Interacting with the Masakhane Benchmark Models I created this demo for very easy interaction with the [benchmark models on Masakhane](https://github.com/masakhane-io/masakhane-mt/tree/master/benchmarks) which were trained with [JoeyNMT](https://github.com/chrisemezue/joeynmt)(my forked version). To access the space click [here](https://huggingface.co/spaces/chrisjay/masakhane-benchmarks). To include your language, all you need to do is: 1. Create a folder in the format *src-tgt/main* for your language pair, if it does not exist. 2. Inside the *main* folder put the following files: 1. model checkpoint. Rename it to `best.ckpt`. 2. `config.yaml` file. This is the JoeyNMT config file which loads the model an pre-processing parameters. 3. `src_vocab.txt` file. 4. `trg_vocab.txt` file. The space currently supports these languages: | source language | target language | |:---------------:|:---------------:| | English | Swahili | | English | Afrikaans | | English | Arabic | | English | Urhobo | | English | Ẹ̀dó | | Efik | English | | English | Hausa | | English | Igbo | | English | Fon | | English | Twi | | English | Dendi | | English | Ẹ̀sán | | English | Isoko | | English | Kamba | | English | Luo | | English | Southern Ndebele | | English | Tshivenda | | Shona | English | | Swahili | English | | Yoruba | English | TO DO: 1. Include more languages from the benchmark.
{"language": "african-languages", "license": "apache-2.0", "tags": ["african-languages", "machine-translation", "text"]}
null
chrisjay/masakhane_benchmarks
[ "african-languages", "machine-translation", "text", "license:apache-2.0", "has_space", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[ "african-languages" ]
TAGS #african-languages #machine-translation #text #license-apache-2.0 #has_space #region-us
Interacting with the Masakhane Benchmark Models =============================================== I created this demo for very easy interaction with the benchmark models on Masakhane which were trained with JoeyNMT(my forked version). To access the space click here. To include your language, all you need to do is: 1. Create a folder in the format *src-tgt/main* for your language pair, if it does not exist. 2. Inside the *main* folder put the following files: 1. model checkpoint. Rename it to 'URL'. 2. 'URL' file. This is the JoeyNMT config file which loads the model an pre-processing parameters. 3. 'src\_vocab.txt' file. 4. 'trg\_vocab.txt' file. The space currently supports these languages: TO DO: 1. Include more languages from the benchmark.
[]
[ "TAGS\n#african-languages #machine-translation #text #license-apache-2.0 #has_space #region-us \n" ]
[ 30 ]
[ "passage: TAGS\n#african-languages #machine-translation #text #license-apache-2.0 #has_space #region-us \n" ]
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null
null
spacy
Text statistics including readability and formality. | Feature | Description | | --- | --- | | **Name** | `en_statistics` | | **Version** | `0.0.1` | | **spaCy** | `>=3.1.1,<3.2.0` | | **Default Pipeline** | `tok2vec`, `tagger`, `parser`, `attribute_ruler`, `lemmatizer`, `syllables`, `formality`, `readability` | | **Components** | `tok2vec`, `tagger`, `parser`, `senter`, `attribute_ruler`, `lemmatizer`, `syllables`, `formality`, `readability` | | **Vectors** | 684830 keys, 20000 unique vectors (300 dimensions) | | **Sources** | [OntoNotes 5](https://catalog.ldc.upenn.edu/LDC2013T19) (Ralph Weischedel, Martha Palmer, Mitchell Marcus, Eduard Hovy, Sameer Pradhan, Lance Ramshaw, Nianwen Xue, Ann Taylor, Jeff Kaufman, Michelle Franchini, Mohammed El-Bachouti, Robert Belvin, Ann Houston)<br />[ClearNLP Constituent-to-Dependency Conversion](https://github.com/clir/clearnlp-guidelines/blob/master/md/components/dependency_conversion.md) (Emory University)<br />[WordNet 3.0](https://wordnet.princeton.edu/) (Princeton University)<br />[GloVe Common Crawl](https://nlp.stanford.edu/projects/glove/) (Jeffrey Pennington, Richard Socher, and Christopher D. Manning) | | **License** | `MIT` | | **Author** | [Chris Knowles](https://explosion.ai) | ### Label Scheme <details> <summary>View label scheme (96 labels for 3 components)</summary> | Component | Labels | | --- | --- | | **`tagger`** | `$`, `''`, `,`, `-LRB-`, `-RRB-`, `.`, `:`, `ADD`, `AFX`, `CC`, `CD`, `DT`, `EX`, `FW`, `HYPH`, `IN`, `JJ`, `JJR`, `JJS`, `LS`, `MD`, `NFP`, `NN`, `NNP`, `NNPS`, `NNS`, `PDT`, `POS`, `PRP`, `PRP$`, `RB`, `RBR`, `RBS`, `RP`, `SYM`, `TO`, `UH`, `VB`, `VBD`, `VBG`, `VBN`, `VBP`, `VBZ`, `WDT`, `WP`, `WP$`, `WRB`, `XX`, ```` | | **`parser`** | `ROOT`, `acl`, `acomp`, `advcl`, `advmod`, `agent`, `amod`, `appos`, `attr`, `aux`, `auxpass`, `case`, `cc`, `ccomp`, `compound`, `conj`, `csubj`, `csubjpass`, `dative`, `dep`, `det`, `dobj`, `expl`, `intj`, `mark`, `meta`, `neg`, `nmod`, `npadvmod`, `nsubj`, `nsubjpass`, `nummod`, `oprd`, `parataxis`, `pcomp`, `pobj`, `poss`, `preconj`, `predet`, `prep`, `prt`, `punct`, `quantmod`, `relcl`, `xcomp` | | **`senter`** | `I`, `S` | </details>
{"language": ["en"], "license": "mit", "tags": ["spacy", "text-classification"], "model-index": [{"name": "en_statistics", "results": []}]}
text-classification
chrisknowles/en_statistics
[ "spacy", "text-classification", "en", "license:mit", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[ "en" ]
TAGS #spacy #text-classification #en #license-mit #region-us
Text statistics including readability and formality. ### Label Scheme View label scheme (96 labels for 3 components)
[ "### Label Scheme\n\n\n\nView label scheme (96 labels for 3 components)" ]
[ "TAGS\n#spacy #text-classification #en #license-mit #region-us \n", "### Label Scheme\n\n\n\nView label scheme (96 labels for 3 components)" ]
[ 21, 17 ]
[ "passage: TAGS\n#spacy #text-classification #en #license-mit #region-us \n### Label Scheme\n\n\n\nView label scheme (96 labels for 3 components)" ]
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null
null
spacy
Check style on English text (currently passive text). | Feature | Description | | --- | --- | | **Name** | `en_stylecheck` | | **Version** | `0.0.1` | | **spaCy** | `>=3.1.1,<3.2.0` | | **Default Pipeline** | `tok2vec`, `tagger`, `parser`, `attribute_ruler`, `lemmatizer`, `ner`, `stylecheck` | | **Components** | `tok2vec`, `tagger`, `parser`, `senter`, `attribute_ruler`, `lemmatizer`, `ner`, `stylecheck` | | **Vectors** | 684830 keys, 20000 unique vectors (300 dimensions) | | **Sources** | n/a | | **License** | `MIT` | | **Author** | [Explosion](https://explosion.ai) | ### Label Scheme <details> <summary>View label scheme (115 labels for 5 components)</summary> | Component | Labels | | --- | --- | | **`tagger`** | `$`, `''`, `,`, `-LRB-`, `-RRB-`, `.`, `:`, `ADD`, `AFX`, `CC`, `CD`, `DT`, `EX`, `FW`, `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`, ```` | | **`parser`** | `ROOT`, `acl`, `acomp`, `advcl`, `advmod`, `agent`, `amod`, `appos`, `attr`, `aux`, `auxpass`, `case`, `cc`, `ccomp`, `compound`, `conj`, `csubj`, `csubjpass`, `dative`, `dep`, `det`, `dobj`, `expl`, `intj`, `mark`, `meta`, `neg`, `nmod`, `npadvmod`, `nsubj`, `nsubjpass`, `nummod`, `oprd`, `parataxis`, `pcomp`, `pobj`, `poss`, `preconj`, `predet`, `prep`, `prt`, `punct`, `quantmod`, `relcl`, `xcomp` | | **`senter`** | `I`, `S` | | **`ner`** | `CARDINAL`, `DATE`, `EVENT`, `FAC`, `GPE`, `LANGUAGE`, `LAW`, `LOC`, `MONEY`, `NORP`, `ORDINAL`, `ORG`, `PERCENT`, `PERSON`, `PRODUCT`, `QUANTITY`, `TIME`, `WORK_OF_ART` | | **`entity_ruler`** | `PASSIVE` | </details>
{"language": ["en"], "license": "mit", "tags": ["spacy", "token-classification"], "model-index": [{"name": "en_stylecheck", "results": []}]}
token-classification
chrisknowles/en_stylecheck
[ "spacy", "token-classification", "en", "license:mit", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[ "en" ]
TAGS #spacy #token-classification #en #license-mit #region-us
Check style on English text (currently passive text). ### Label Scheme View label scheme (115 labels for 5 components)
[ "### Label Scheme\n\n\n\nView label scheme (115 labels for 5 components)" ]
[ "TAGS\n#spacy #token-classification #en #license-mit #region-us \n", "### Label Scheme\n\n\n\nView label scheme (115 labels for 5 components)" ]
[ 22, 17 ]
[ "passage: TAGS\n#spacy #token-classification #en #license-mit #region-us \n### Label Scheme\n\n\n\nView label scheme (115 labels for 5 components)" ]
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null
null
transformers
[DistilGPT2](https://huggingface.co/distilgpt2) English language model fine-tuned on mathematical proofs extracted from [arXiv.org](https://arxiv.org) LaTeX sources from 1992 to 2020. Proofs have been cleaned up a bit. In particular, they use * `CITE` for any citation * `REF` for any reference * `MATH` for any LaTeX mathematical formula * `CASE:` for any `\item` or labeled subcase.
{"widget": [{"text": "Let MATH be given."}, {"text": "If MATH is a nonempty"}, {"text": "By the inductive hypothesis,"}]}
text-generation
christopherastone/distilgpt2-proofs
[ "transformers", "pytorch", "tf", "jax", "safetensors", "gpt2", "text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[]
TAGS #transformers #pytorch #tf #jax #safetensors #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
DistilGPT2 English language model fine-tuned on mathematical proofs extracted from URL LaTeX sources from 1992 to 2020. Proofs have been cleaned up a bit. In particular, they use * 'CITE' for any citation * 'REF' for any reference * 'MATH' for any LaTeX mathematical formula * 'CASE:' for any '\item' or labeled subcase.
[]
[ "TAGS\n#transformers #pytorch #tf #jax #safetensors #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]
[ 58 ]
[ "passage: TAGS\n#transformers #pytorch #tf #jax #safetensors #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]
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null
null
transformers
<!-- 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-multilingual-cased-finetuned-cola This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on an unkown dataset. It achieves the following results on the evaluation set: - Loss: 0.1729 - Accuracy: 0.9755 ## 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: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.5119 | 1.0 | 625 | 0.2386 | 0.922 | | 0.2536 | 2.0 | 1250 | 0.2055 | 0.949 | | 0.1718 | 3.0 | 1875 | 0.1733 | 0.969 | | 0.0562 | 4.0 | 2500 | 0.1661 | 0.974 | | 0.0265 | 5.0 | 3125 | 0.1729 | 0.9755 | ### Framework versions - Transformers 4.9.2 - Pytorch 1.9.0+cu102 - Datasets 1.11.0 - Tokenizers 0.10.3
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model_index": [{"name": "bert-base-multilingual-cased-finetuned-cola", "results": [{"task": {"name": "Text Classification", "type": "text-classification"}, "metric": {"name": "Accuracy", "type": "accuracy", "value": 0.9755}}]}]}
text-classification
chrommium/bert-base-multilingual-cased-finetuned-news-headlines
[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[]
TAGS #transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
bert-base-multilingual-cased-finetuned-cola =========================================== This model is a fine-tuned version of bert-base-multilingual-cased on an unkown dataset. It achieves the following results on the evaluation set: * Loss: 0.1729 * Accuracy: 0.9755 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: 5 ### Training results ### Framework versions * Transformers 4.9.2 * Pytorch 1.9.0+cu102 * Datasets 1.11.0 * Tokenizers 0.10.3
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5", "### Training results", "### Framework versions\n\n\n* Transformers 4.9.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.11.0\n* Tokenizers 0.10.3" ]
[ "TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5", "### Training results", "### Framework versions\n\n\n* Transformers 4.9.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.11.0\n* Tokenizers 0.10.3" ]
[ 55, 98, 4, 34 ]
[ "passage: TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5### Training results### Framework versions\n\n\n* Transformers 4.9.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.11.0\n* Tokenizers 0.10.3" ]
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null
null
transformers
<!-- 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. --> # rubert-base-cased-sentence-finetuned-headlines_X This model is a fine-tuned version of [DeepPavlov/rubert-base-cased-sentence](https://huggingface.co/DeepPavlov/rubert-base-cased-sentence) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2535 - Accuracy: 0.952 ## 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: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 157 | 0.2759 | 0.912 | | No log | 2.0 | 314 | 0.2538 | 0.936 | | No log | 3.0 | 471 | 0.2556 | 0.945 | | 0.1908 | 4.0 | 628 | 0.2601 | 0.95 | | 0.1908 | 5.0 | 785 | 0.2535 | 0.952 | ### Framework versions - Transformers 4.10.2 - Pytorch 1.9.0+cu102 - Datasets 1.12.1 - Tokenizers 0.10.3
{"tags": ["generated_from_trainer"], "metrics": ["accuracy"]}
text-classification
chrommium/rubert-base-cased-sentence-finetuned-headlines_X
[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[]
TAGS #transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #model-index #autotrain_compatible #endpoints_compatible #region-us
rubert-base-cased-sentence-finetuned-headlines\_X ================================================= This model is a fine-tuned version of DeepPavlov/rubert-base-cased-sentence on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 0.2535 * Accuracy: 0.952 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: 32 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 5 ### Training results ### Framework versions * Transformers 4.10.2 * Pytorch 1.9.0+cu102 * Datasets 1.12.1 * Tokenizers 0.10.3
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5", "### Training results", "### Framework versions\n\n\n* Transformers 4.10.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.12.1\n* Tokenizers 0.10.3" ]
[ "TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #model-index #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5", "### Training results", "### Framework versions\n\n\n* Transformers 4.10.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.12.1\n* Tokenizers 0.10.3" ]
[ 51, 98, 4, 34 ]
[ "passage: TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5### Training results### Framework versions\n\n\n* Transformers 4.10.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.12.1\n* Tokenizers 0.10.3" ]
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null
null
transformers
<!-- 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. --> # rubert-base-cased-sentence-finetuned-sent_in_news_sents This model is a fine-tuned version of [DeepPavlov/rubert-base-cased-sentence](https://huggingface.co/DeepPavlov/rubert-base-cased-sentence) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.9506 - Accuracy: 0.7224 - F1: 0.5137 ## 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: 14 - eval_batch_size: 14 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 81 | 1.0045 | 0.6690 | 0.1388 | | No log | 2.0 | 162 | 0.9574 | 0.6228 | 0.2980 | | No log | 3.0 | 243 | 1.0259 | 0.6477 | 0.3208 | | No log | 4.0 | 324 | 1.1262 | 0.6619 | 0.4033 | | No log | 5.0 | 405 | 1.3377 | 0.6299 | 0.3909 | | No log | 6.0 | 486 | 1.5716 | 0.6868 | 0.3624 | | 0.6085 | 7.0 | 567 | 1.6286 | 0.6762 | 0.4130 | | 0.6085 | 8.0 | 648 | 1.6450 | 0.6940 | 0.4775 | | 0.6085 | 9.0 | 729 | 1.7108 | 0.7224 | 0.4920 | | 0.6085 | 10.0 | 810 | 1.8792 | 0.7046 | 0.5028 | | 0.6085 | 11.0 | 891 | 1.8670 | 0.7153 | 0.4992 | | 0.6085 | 12.0 | 972 | 1.8856 | 0.7153 | 0.4934 | | 0.0922 | 13.0 | 1053 | 1.9506 | 0.7224 | 0.5137 | | 0.0922 | 14.0 | 1134 | 2.0363 | 0.7189 | 0.4761 | | 0.0922 | 15.0 | 1215 | 2.0601 | 0.7224 | 0.5053 | | 0.0922 | 16.0 | 1296 | 2.0813 | 0.7153 | 0.5038 | | 0.0922 | 17.0 | 1377 | 2.0960 | 0.7189 | 0.5065 | | 0.0922 | 18.0 | 1458 | 2.1060 | 0.7224 | 0.5098 | | 0.0101 | 19.0 | 1539 | 2.1153 | 0.7260 | 0.5086 | | 0.0101 | 20.0 | 1620 | 2.1187 | 0.7260 | 0.5086 | ### Framework versions - Transformers 4.10.3 - Pytorch 1.9.0+cu102 - Datasets 1.12.1 - Tokenizers 0.10.3
{"tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"]}
text-classification
chrommium/rubert-base-cased-sentence-finetuned-sent_in_news_sents
[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[]
TAGS #transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #model-index #autotrain_compatible #endpoints_compatible #region-us
rubert-base-cased-sentence-finetuned-sent\_in\_news\_sents ========================================================== This model is a fine-tuned version of DeepPavlov/rubert-base-cased-sentence on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 1.9506 * Accuracy: 0.7224 * F1: 0.5137 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: 14 * eval\_batch\_size: 14 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 20 ### Training results ### Framework versions * Transformers 4.10.3 * Pytorch 1.9.0+cu102 * Datasets 1.12.1 * Tokenizers 0.10.3
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 14\n* eval\\_batch\\_size: 14\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 20", "### Training results", "### Framework versions\n\n\n* Transformers 4.10.3\n* Pytorch 1.9.0+cu102\n* Datasets 1.12.1\n* Tokenizers 0.10.3" ]
[ "TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #model-index #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 14\n* eval\\_batch\\_size: 14\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 20", "### Training results", "### Framework versions\n\n\n* Transformers 4.10.3\n* Pytorch 1.9.0+cu102\n* Datasets 1.12.1\n* Tokenizers 0.10.3" ]
[ 51, 98, 4, 34 ]
[ "passage: TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 14\n* eval\\_batch\\_size: 14\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 20### Training results### Framework versions\n\n\n* Transformers 4.10.3\n* Pytorch 1.9.0+cu102\n* Datasets 1.12.1\n* Tokenizers 0.10.3" ]
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null
transformers
<!-- 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. --> # rubert-base-cased-sentence-finetuned-sent_in_ru This model is a fine-tuned version of [DeepPavlov/rubert-base-cased-sentence](https://huggingface.co/DeepPavlov/rubert-base-cased-sentence) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.3503 - Accuracy: 0.6884 - F1: 0.6875 ## 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: 15 - eval_batch_size: 15 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 25 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:| | No log | 1.0 | 441 | 0.7397 | 0.6630 | 0.6530 | | 0.771 | 2.0 | 882 | 0.7143 | 0.6909 | 0.6905 | | 0.5449 | 3.0 | 1323 | 0.8385 | 0.6897 | 0.6870 | | 0.3795 | 4.0 | 1764 | 0.8851 | 0.6939 | 0.6914 | | 0.3059 | 5.0 | 2205 | 1.0728 | 0.6933 | 0.6953 | | 0.2673 | 6.0 | 2646 | 1.0673 | 0.7060 | 0.7020 | | 0.2358 | 7.0 | 3087 | 1.5200 | 0.6830 | 0.6829 | | 0.2069 | 8.0 | 3528 | 1.3439 | 0.7024 | 0.7016 | | 0.2069 | 9.0 | 3969 | 1.3545 | 0.6830 | 0.6833 | | 0.1724 | 10.0 | 4410 | 1.5591 | 0.6927 | 0.6902 | | 0.1525 | 11.0 | 4851 | 1.6425 | 0.6818 | 0.6823 | | 0.131 | 12.0 | 5292 | 1.8999 | 0.6836 | 0.6775 | | 0.1253 | 13.0 | 5733 | 1.6959 | 0.6884 | 0.6877 | | 0.1132 | 14.0 | 6174 | 1.9561 | 0.6776 | 0.6803 | | 0.0951 | 15.0 | 6615 | 2.0356 | 0.6763 | 0.6754 | | 0.1009 | 16.0 | 7056 | 1.7995 | 0.6842 | 0.6741 | | 0.1009 | 17.0 | 7497 | 2.0638 | 0.6884 | 0.6811 | | 0.0817 | 18.0 | 7938 | 2.1686 | 0.6884 | 0.6859 | | 0.0691 | 19.0 | 8379 | 2.0874 | 0.6878 | 0.6889 | | 0.0656 | 20.0 | 8820 | 2.1772 | 0.6854 | 0.6817 | | 0.0652 | 21.0 | 9261 | 2.4018 | 0.6872 | 0.6896 | | 0.0608 | 22.0 | 9702 | 2.2074 | 0.6770 | 0.6656 | | 0.0677 | 23.0 | 10143 | 2.2101 | 0.6848 | 0.6793 | | 0.0559 | 24.0 | 10584 | 2.2920 | 0.6848 | 0.6835 | | 0.0524 | 25.0 | 11025 | 2.3503 | 0.6884 | 0.6875 | ### Framework versions - Transformers 4.11.2 - Pytorch 1.9.0+cu102 - Datasets 1.12.1 - Tokenizers 0.10.3
{"tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "rubert-base-cased-sentence-finetuned-sent_in_ru", "results": []}]}
text-classification
chrommium/rubert-base-cased-sentence-finetuned-sent_in_ru
[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[]
TAGS #transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
rubert-base-cased-sentence-finetuned-sent\_in\_ru ================================================= This model is a fine-tuned version of DeepPavlov/rubert-base-cased-sentence on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 2.3503 * Accuracy: 0.6884 * F1: 0.6875 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: 15 * eval\_batch\_size: 15 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 25 ### Training results ### Framework versions * Transformers 4.11.2 * Pytorch 1.9.0+cu102 * Datasets 1.12.1 * Tokenizers 0.10.3
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 15\n* eval\\_batch\\_size: 15\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 25", "### Training results", "### Framework versions\n\n\n* Transformers 4.11.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.12.1\n* Tokenizers 0.10.3" ]
[ "TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 15\n* eval\\_batch\\_size: 15\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 25", "### Training results", "### Framework versions\n\n\n* Transformers 4.11.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.12.1\n* Tokenizers 0.10.3" ]
[ 47, 98, 4, 34 ]
[ "passage: TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 15\n* eval\\_batch\\_size: 15\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 25### Training results### Framework versions\n\n\n* Transformers 4.11.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.12.1\n* Tokenizers 0.10.3" ]
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null
null
transformers
<!-- 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. --> # sbert_large-finetuned-sent_in_news_sents This model is a fine-tuned version of [sberbank-ai/sbert_large_nlu_ru](https://huggingface.co/sberbank-ai/sbert_large_nlu_ru) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.7056 - Accuracy: 0.7301 - F1: 0.5210 ## Model examples Model responds to label X in news text. For exaple: For 'Газпром отозвал лицензию у X, сообщает Финам' the model will return negative label -3 For 'X отозвал лицензию у Сбербанка, сообщает Финам' the model will return neutral label 0 For 'Газпром отозвал лицензию у Сбербанка, сообщает X' the model will return neutral label 0 For 'X демонстрирует высокую прибыль, сообщает Финам' the model will return positive label 1 ## Simple example of News preprocessing for Russian before BERT ``` from natasha import ( Segmenter, MorphVocab, NewsEmbedding, NewsMorphTagger, NewsSyntaxParser, NewsNERTagger, PER, NamesExtractor, Doc ) segmenter = Segmenter() emb = NewsEmbedding() morph_tagger = NewsMorphTagger(emb) syntax_parser = NewsSyntaxParser(emb) morph_vocab = MorphVocab() ### ----------------------------- key sentences block ----------------------------- def find_synax_tokens_with_order(doc, start, tokens, text_arr, full_str): ''' Находит все синтаксические токены, соответствующие заданному набору простых токенов (найденные для определенной NER другими функциями). Возвращает словарь найденных синтаксических токенов (ключ - идентификатор токена, состоящий из номера предложения и номера токена внутри предложения). Начинает поиск с указанной позиции в списке синтаксических токенов, дополнительно возвращает позицию остановки, с которой нужно продолжить поиск следующей NER. ''' found = [] in_str = False str_candidate = '' str_counter = 0 if len(text_arr) == 0: return [], start for i in range(start, len(doc.syntax.tokens)): t = doc.syntax.tokens[i] if in_str: str_counter += 1 if str_counter < len(text_arr) and t.text == text_arr[str_counter]: str_candidate += t.text found.append(t) if str_candidate == full_str: return found, i+1 else: in_str = False str_candidate = '' str_counter = 0 found = [] if t.text == text_arr[0]: found.append(t) str_candidate = t.text if str_candidate == full_str: return found, i+1 in_str = True return [], len(doc.syntax.tokens) def find_tokens_in_diap_with_order(doc, start_token, diap): ''' Находит все простые токены (без синтаксической информации), которые попадают в указанный диапазон. Эти диапазоны мы получаем из разметки NER. Возвращает набор найденных токенов и в виде массива токенов, и в виде массива строчек. Начинает поиск с указанной позиции в строке и дополнительно возвращает позицию остановки. ''' found_tokens = [] found_text = [] full_str = '' next_i = 0 for i in range(start_token, len(doc.tokens)): t = doc.tokens[i] if t.start > diap[-1]: next_i = i break if t.start in diap: found_tokens.append(t) found_text.append(t.text) full_str += t.text return found_tokens, found_text, full_str, next_i def add_found_arr_to_dict(found, dict_dest): for synt in found: dict_dest.update({synt.id: synt}) return dict_dest def make_all_syntax_dict(doc): all_syntax = {} for synt in doc.syntax.tokens: all_syntax.update({synt.id: synt}) return all_syntax def is_consiquent(id_1, id_2): ''' Проверяет идут ли токены друг за другом без промежутка по ключам. ''' id_1_list = id_1.split('_') id_2_list = id_2.split('_') if id_1_list[0] != id_2_list[0]: return False return int(id_1_list[1]) + 1 == int(id_2_list[1]) def replace_found_to(found, x_str): ''' Заменяет последовательность токенов NER на «заглушку». ''' prev_id = '0_0' for synt in found: if is_consiquent(prev_id, synt.id): synt.text = '' else: synt.text = x_str prev_id = synt.id def analyze_doc(text): ''' Запускает Natasha для анализа документа. ''' doc = Doc(text) doc.segment(segmenter) doc.tag_morph(morph_tagger) doc.parse_syntax(syntax_parser) ner_tagger = NewsNERTagger(emb) doc.tag_ner(ner_tagger) return doc def find_non_sym_syntax_short(entity_name, doc, add_X=False, x_str='X'): ''' Отыскивает заданную сущность в тексте, среди всех NER (возможно, в другой грамматической форме). entity_name - сущность, которую ищем; doc - документ, в котором сделан препроцессинг Natasha; add_X - сделать ли замену сущности на «заглушку»; x_str - текст замены. Возвращает: all_found_syntax - словарь всех подходящих токенов образующих искомые сущности, в котором в случае надобности произведена замена NER на «заглушку»; all_syntax - словарь всех токенов. ''' all_found_syntax = {} current_synt_number = 0 current_tok_number = 0 # идем по всем найденным NER for span in doc.spans: span.normalize(morph_vocab) if span.type != 'ORG': continue diap = range(span.start, span.stop) # создаем словарь всех синтаксических элементов (ключ -- id из номера предложения и номера внутри предложения) all_syntax = make_all_syntax_dict(doc) # находим все простые токены внутри NER found_tokens, found_text, full_str, current_tok_number = find_tokens_in_diap_with_order(doc, current_tok_number, diap) # по найденным простым токенам находим все синтаксические токены внутри данного NER found, current_synt_number = find_synax_tokens_with_order(doc, current_synt_number, found_tokens, found_text, full_str) # если текст NER совпадает с указанной сущностью, то делаем замену if entity_name.find(span.normal) >= 0 or span.normal.find(entity_name) >= 0: if add_X: replace_found_to(found, x_str) all_found_syntax = add_found_arr_to_dict(found, all_found_syntax) return all_found_syntax, all_syntax def key_sentences(all_found_syntax): ''' Находит номера предложений с искомой NER. ''' key_sent_numb = {} for synt in all_found_syntax.keys(): key_sent_numb.update({synt.split('_')[0]: 1}) return key_sent_numb def openinig_punct(x): opennings = ['«', '('] return x in opennings def key_sentences_str(entitiy_name, doc, add_X=False, x_str='X', return_all=True): ''' Составляет окончательный текст, в котором есть только предложения, где есть ключевая сущность, эта сущность, если указано, заменяется на «заглушку». ''' all_found_syntax, all_syntax = find_non_sym_syntax_short(entitiy_name, doc, add_X, x_str) key_sent_numb = key_sentences(all_found_syntax) str_ret = '' for s in all_syntax.keys(): if (s.split('_')[0] in key_sent_numb.keys()) or (return_all): to_add = all_syntax[s] if s in all_found_syntax.keys(): to_add = all_found_syntax[s] else: if to_add.rel == 'punct' and not openinig_punct(to_add.text): str_ret = str_ret.rstrip() str_ret += to_add.text if (not openinig_punct(to_add.text)) and (to_add.text != ''): str_ret += ' ' return str_ret ### ----------------------------- key entities block ----------------------------- def find_synt(doc, synt_id): for synt in doc.syntax.tokens: if synt.id == synt_id: return synt return None def is_subj(doc, synt, recursion_list=[]): ''' Сообщает является ли слово подлежащим или частью сложного подлежащего. ''' if synt.rel == 'nsubj': return True if synt.rel == 'appos': found_head = find_synt(doc, synt.head_id) if found_head.id in recursion_list: return False return is_subj(doc, found_head, recursion_list + [synt.id]) return False def find_subjects_in_syntax(doc): ''' Выдает словарик, в котором для каждой NER написано, является ли он подлежащим в предложении. Выдает стартовую позицию NER и было ли оно подлежащим (или appos) ''' found_subjects = {} current_synt_number = 0 current_tok_number = 0 for span in doc.spans: span.normalize(morph_vocab) if span.type != 'ORG': continue found_subjects.update({span.start: 0}) diap = range(span.start, span.stop) found_tokens, found_text, full_str, current_tok_number = find_tokens_in_diap_with_order(doc, current_tok_number, diap) found, current_synt_number = find_synax_tokens_with_order(doc, current_synt_number, found_tokens, found_text, full_str) found_subjects.update({span.start: 0}) for synt in found: if is_subj(doc, synt): found_subjects.update({span.start: 1}) return found_subjects def entity_weight(lst, c=1): return c*lst[0]+lst[1] def determine_subject(found_subjects, doc, new_agency_list, return_best=True, threshold=0.75): ''' Определяет ключевую NER и список самых важных NER, основываясь на том, сколько раз каждая из них встречается в текста вообще и сколько раз в роли подлежащего ''' objects_arr = [] objects_arr_ners = [] should_continue = False for span in doc.spans: should_continue = False span.normalize(morph_vocab) if span.type != 'ORG': continue if span.normal in new_agency_list: continue for i in range(len(objects_arr)): t, lst = objects_arr[i] if t.find(span.normal) >= 0: lst[0] += 1 lst[1] += found_subjects[span.start] should_continue = True break if span.normal.find(t) >= 0: objects_arr[i] = (span.normal, [lst[0]+1, lst[1]+found_subjects[span.start]]) should_continue = True break if should_continue: continue objects_arr.append((span.normal, [1, found_subjects[span.start]])) objects_arr_ners.append(span.normal) max_weight = 0 opt_ent = 0 for obj in objects_arr: t, lst = obj w = entity_weight(lst) if max_weight < w: max_weight = w opt_ent = t if not return_best: return opt_ent, objects_arr_ners bests = [] for obj in objects_arr: t, lst = obj w = entity_weight(lst) if max_weight*threshold < w: bests.append(t) return opt_ent, bests text = '''В офисах Сбера начали тестировать технологию помощи посетителям в экстренных ситуациях. «Зеленая кнопка» будет в зонах круглосуточного обслуживания офисов банка в Воронеже, Санкт-Петербурге, Подольске, Пскове, Орле и Ярославле. В них находятся стенды с сенсорными кнопками, обеспечивающие связь с операторами центра мониторинга службы безопасности банка. Получив сигнал о помощи, оператор центра может подключиться к объекту по голосовой связи. С помощью камер видеонаблюдения он оценит обстановку и при необходимости вызовет полицию или скорую помощь. «Зеленой кнопкой» можно воспользоваться в нерабочее для отделения время, если возникла угроза жизни или здоровью. В остальных случаях помочь клиентам готовы сотрудники отделения банка. «Одно из направлений нашей работы в области ESG и устойчивого развития — это забота об обществе. И здоровье людей как высшая ценность является его основой. Поэтому задача банка в области безопасности гораздо масштабнее, чем обеспечение только финансовой безопасности клиентов. Этот пилотный проект приурочен к 180-летию Сбербанка: мы хотим, чтобы, приходя в банк, клиент чувствовал, что его жизнь и безопасность — наша ценность», — отметил заместитель председателя правления Сбербанка Станислав Кузнецов.''' doc = analyze_doc(text) key_entity = determine_subject(find_subjects_in_syntax(doc), doc, [])[0] text_for_model = key_sentences_str(key_entity, doc, add_X=True, x_str='X', return_all=False) ``` ## 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: 6 - eval_batch_size: 6 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 176 | 0.9504 | 0.6903 | 0.2215 | | No log | 2.0 | 352 | 0.9065 | 0.7159 | 0.4760 | | 0.8448 | 3.0 | 528 | 0.9687 | 0.7045 | 0.4774 | | 0.8448 | 4.0 | 704 | 1.2436 | 0.7045 | 0.4686 | | 0.8448 | 5.0 | 880 | 1.4809 | 0.7273 | 0.4630 | | 0.2074 | 6.0 | 1056 | 1.5866 | 0.7330 | 0.5185 | | 0.2074 | 7.0 | 1232 | 1.7056 | 0.7301 | 0.5210 | | 0.2074 | 8.0 | 1408 | 1.6982 | 0.7415 | 0.5056 | | 0.0514 | 9.0 | 1584 | 1.8088 | 0.7273 | 0.5203 | | 0.0514 | 10.0 | 1760 | 1.9250 | 0.7102 | 0.4879 | ### Framework versions - Transformers 4.11.2 - Pytorch 1.9.0+cu102 - Datasets 1.12.1 - Tokenizers 0.10.3
{"tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "sbert_large-finetuned-sent_in_news_sents", "results": []}]}
text-classification
chrommium/sbert_large-finetuned-sent_in_news_sents
[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[]
TAGS #transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
sbert\_large-finetuned-sent\_in\_news\_sents ============================================ This model is a fine-tuned version of sberbank-ai/sbert\_large\_nlu\_ru on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 1.7056 * Accuracy: 0.7301 * F1: 0.5210 Model examples -------------- Model responds to label X in news text. For exaple: For 'Газпром отозвал лицензию у X, сообщает Финам' the model will return negative label -3 For 'X отозвал лицензию у Сбербанка, сообщает Финам' the model will return neutral label 0 For 'Газпром отозвал лицензию у Сбербанка, сообщает X' the model will return neutral label 0 For 'X демонстрирует высокую прибыль, сообщает Финам' the model will return positive label 1 Simple example of News preprocessing for Russian before BERT ------------------------------------------------------------ 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: 6 * eval\_batch\_size: 6 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 20 ### Training results ### Framework versions * Transformers 4.11.2 * Pytorch 1.9.0+cu102 * Datasets 1.12.1 * Tokenizers 0.10.3
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 6\n* eval\\_batch\\_size: 6\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 20", "### Training results", "### Framework versions\n\n\n* Transformers 4.11.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.12.1\n* Tokenizers 0.10.3" ]
[ "TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 6\n* eval\\_batch\\_size: 6\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 20", "### Training results", "### Framework versions\n\n\n* Transformers 4.11.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.12.1\n* Tokenizers 0.10.3" ]
[ 47, 98, 4, 34 ]
[ "passage: TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 6\n* eval\\_batch\\_size: 6\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 20### Training results### Framework versions\n\n\n* Transformers 4.11.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.12.1\n* Tokenizers 0.10.3" ]
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null
null
transformers
<!-- 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. --> # sbert_large-finetuned-sent_in_news_sents_3lab This model is a fine-tuned version of [sberbank-ai/sbert_large_nlu_ru](https://huggingface.co/sberbank-ai/sbert_large_nlu_ru) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.9443 - Accuracy: 0.8580 - F1: 0.6199 ## 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: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 17 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 264 | 0.6137 | 0.8608 | 0.3084 | | 0.524 | 2.0 | 528 | 0.6563 | 0.8722 | 0.4861 | | 0.524 | 3.0 | 792 | 0.7110 | 0.8494 | 0.4687 | | 0.2225 | 4.0 | 1056 | 0.7323 | 0.8608 | 0.6015 | | 0.2225 | 5.0 | 1320 | 0.9604 | 0.8551 | 0.6185 | | 0.1037 | 6.0 | 1584 | 0.8801 | 0.8523 | 0.5535 | | 0.1037 | 7.0 | 1848 | 0.9443 | 0.8580 | 0.6199 | | 0.0479 | 8.0 | 2112 | 1.0048 | 0.8608 | 0.6168 | | 0.0479 | 9.0 | 2376 | 0.9757 | 0.8551 | 0.6097 | | 0.0353 | 10.0 | 2640 | 1.0743 | 0.8580 | 0.6071 | | 0.0353 | 11.0 | 2904 | 1.1216 | 0.8580 | 0.6011 | ### Framework versions - Transformers 4.11.2 - Pytorch 1.9.0+cu102 - Datasets 1.12.1 - Tokenizers 0.10.3
{"tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "sbert_large-finetuned-sent_in_news_sents_3lab", "results": []}]}
text-classification
chrommium/sbert_large-finetuned-sent_in_news_sents_3lab
[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[]
TAGS #transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
sbert\_large-finetuned-sent\_in\_news\_sents\_3lab ================================================== This model is a fine-tuned version of sberbank-ai/sbert\_large\_nlu\_ru on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 0.9443 * Accuracy: 0.8580 * F1: 0.6199 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: 4 * eval\_batch\_size: 4 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 17 ### Training results ### Framework versions * Transformers 4.11.2 * Pytorch 1.9.0+cu102 * Datasets 1.12.1 * Tokenizers 0.10.3
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 17", "### Training results", "### Framework versions\n\n\n* Transformers 4.11.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.12.1\n* Tokenizers 0.10.3" ]
[ "TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 17", "### Training results", "### Framework versions\n\n\n* Transformers 4.11.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.12.1\n* Tokenizers 0.10.3" ]
[ 47, 98, 4, 34 ]
[ "passage: TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 17### Training results### Framework versions\n\n\n* Transformers 4.11.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.12.1\n* Tokenizers 0.10.3" ]
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null
null
transformers
<!-- 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. --> # xlm-roberta-large-finetuned-sent_in_news This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.8872 - Accuracy: 0.7273 - F1: 0.5125 ## Model description Модель ассиметрична, реагирует на метку X в тексте новости. Попробуйте следующие примеры: a) Агентство X понизило рейтинг банка Fitch. b) Агентство Fitch понизило рейтинг банка X. a) Компания Финам показала рекордную прибыль, говорят аналитики компании X. b) Компания X показала рекордную прибыль, говорят аналитики компании Финам. ## 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: 3e-05 - train_batch_size: 10 - eval_batch_size: 10 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 16 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 106 | 1.2526 | 0.6108 | 0.1508 | | No log | 2.0 | 212 | 1.1553 | 0.6648 | 0.1141 | | No log | 3.0 | 318 | 1.1150 | 0.6591 | 0.1247 | | No log | 4.0 | 424 | 1.0007 | 0.6705 | 0.1383 | | 1.1323 | 5.0 | 530 | 0.9267 | 0.6733 | 0.2027 | | 1.1323 | 6.0 | 636 | 1.0869 | 0.6335 | 0.4084 | | 1.1323 | 7.0 | 742 | 1.1224 | 0.6932 | 0.4586 | | 1.1323 | 8.0 | 848 | 1.2535 | 0.6307 | 0.3424 | | 1.1323 | 9.0 | 954 | 1.4288 | 0.6932 | 0.4881 | | 0.5252 | 10.0 | 1060 | 1.5856 | 0.6932 | 0.4739 | | 0.5252 | 11.0 | 1166 | 1.7101 | 0.6733 | 0.4530 | | 0.5252 | 12.0 | 1272 | 1.7330 | 0.6903 | 0.4750 | | 0.5252 | 13.0 | 1378 | 1.8872 | 0.7273 | 0.5125 | | 0.5252 | 14.0 | 1484 | 1.8797 | 0.7301 | 0.5033 | | 0.1252 | 15.0 | 1590 | 1.9339 | 0.7330 | 0.5024 | | 0.1252 | 16.0 | 1696 | 1.9632 | 0.7301 | 0.4967 | ### Framework versions - Transformers 4.11.2 - Pytorch 1.9.0+cu102 - Datasets 1.12.1 - Tokenizers 0.10.3
{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "xlm-roberta-large-finetuned-sent_in_news", "results": []}]}
text-classification
chrommium/xlm-roberta-large-finetuned-sent_in_news
[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "text-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[]
TAGS #transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
xlm-roberta-large-finetuned-sent\_in\_news ========================================== This model is a fine-tuned version of xlm-roberta-large on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 1.8872 * Accuracy: 0.7273 * F1: 0.5125 Model description ----------------- Модель ассиметрична, реагирует на метку X в тексте новости. Попробуйте следующие примеры: a) Агентство X понизило рейтинг банка Fitch. b) Агентство Fitch понизило рейтинг банка X. a) Компания Финам показала рекордную прибыль, говорят аналитики компании X. b) Компания X показала рекордную прибыль, говорят аналитики компании Финам. 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: 3e-05 * train\_batch\_size: 10 * eval\_batch\_size: 10 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 16 ### Training results ### Framework versions * Transformers 4.11.2 * Pytorch 1.9.0+cu102 * Datasets 1.12.1 * Tokenizers 0.10.3
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 10\n* eval\\_batch\\_size: 10\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 16", "### Training results", "### Framework versions\n\n\n* Transformers 4.11.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.12.1\n* Tokenizers 0.10.3" ]
[ "TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 10\n* eval\\_batch\\_size: 10\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 16", "### Training results", "### Framework versions\n\n\n* Transformers 4.11.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.12.1\n* Tokenizers 0.10.3" ]
[ 56, 98, 4, 34 ]
[ "passage: TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 10\n* eval\\_batch\\_size: 10\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 16### Training results### Framework versions\n\n\n* Transformers 4.11.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.12.1\n* Tokenizers 0.10.3" ]
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null
null
transformers
[blenderbot-400M-distill](https://huggingface.co/facebook/blenderbot-400M-distill) fine-tuned on the [ESConv dataset](https://github.com/thu-coai/Emotional-Support-Conversation). Usage example: ```python import torch from transformers import AutoTokenizer from transformers.models.blenderbot import BlenderbotTokenizer, BlenderbotForConditionalGeneration def _norm(x): return ' '.join(x.strip().split()) tokenizer = BlenderbotTokenizer.from_pretrained('thu-coai/blenderbot-400M-esconv') model = BlenderbotForConditionalGeneration.from_pretrained('thu-coai/blenderbot-400M-esconv') model.eval() utterances = [ "I am having a lot of anxiety about quitting my current job. It is too stressful but pays well", "What makes your job stressful for you?", "I have to deal with many people in hard financial situations and it is upsetting", "Do you help your clients to make it to a better financial situation?", "I do, but often they are not going to get back to what they want. Many people are going to lose their home when safeguards are lifted", ] input_sequence = ' '.join([' ' + e for e in utterances]) + tokenizer.eos_token # add space prefix and separate utterances with two spaces input_ids = tokenizer.convert_tokens_to_ids(tokenizer.tokenize(input_sequence))[-128:] input_ids = torch.LongTensor([input_ids]) model_output = model.generate(input_ids, num_beams=1, do_sample=True, top_p=0.9, num_return_sequences=5, return_dict=False) generation = tokenizer.batch_decode(model_output, skip_special_tokens=True) generation = [_norm(e) for e in generation] print(generation) utterances.append(generation[0]) # for future loop ``` Please kindly cite the [original paper](https://arxiv.org/abs/2106.01144) if you use this model: ```bib @inproceedings{liu-etal-2021-towards, title={Towards Emotional Support Dialog Systems}, author={Liu, Siyang and Zheng, Chujie and Demasi, Orianna and Sabour, Sahand and Li, Yu and Yu, Zhou and Jiang, Yong and Huang, Minlie}, booktitle={Proceedings of the 59th annual meeting of the Association for Computational Linguistics}, year={2021} } ```
{"language": ["en"], "tags": ["pytorch", "coai"], "pipeline_tag": "conversational"}
text-generation
thu-coai/blenderbot-400M-esconv
[ "transformers", "pytorch", "safetensors", "blenderbot", "text2text-generation", "coai", "conversational", "en", "arxiv:2106.01144", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]
2022-03-02T23:29:05+00:00
[ "2106.01144" ]
[ "en" ]
TAGS #transformers #pytorch #safetensors #blenderbot #text2text-generation #coai #conversational #en #arxiv-2106.01144 #autotrain_compatible #endpoints_compatible #has_space #region-us
blenderbot-400M-distill fine-tuned on the ESConv dataset. Usage example: Please kindly cite the original paper if you use this model:
[]
[ "TAGS\n#transformers #pytorch #safetensors #blenderbot #text2text-generation #coai #conversational #en #arxiv-2106.01144 #autotrain_compatible #endpoints_compatible #has_space #region-us \n" ]
[ 66 ]
[ "passage: TAGS\n#transformers #pytorch #safetensors #blenderbot #text2text-generation #coai #conversational #en #arxiv-2106.01144 #autotrain_compatible #endpoints_compatible #has_space #region-us \n" ]
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null
null
transformers
## EnDR-BERT EnDR-BERT - Multilingual, Cased, which pretrained on the english collection of consumer comments on drug administration from [2]. Pre-training was based on the [original BERT code](https://github.com/google-research/bert) provided by Google. In particular, Multi-BERT was for used for initialization and all the parameters are the same as in Multi-BERT. Training details are described in our paper. \ link: https://yadi.sk/d/-PTn0xhk1PqvgQ ## Citing & Authors If you find this repository helpful, feel free to cite our publication: [1] Tutubalina E, Alimova I, Miftahutdinov Z, et al. The Russian Drug Reaction Corpus and Neural Models for Drug Reactions and Effectiveness Detection in User Reviews.//Bioinformatics. - 2020. preprint: https://arxiv.org/abs/2004.03659 ``` @article{10.1093/bioinformatics/btaa675, author = {Tutubalina, Elena and Alimova, Ilseyar and Miftahutdinov, Zulfat and Sakhovskiy, Andrey and Malykh, Valentin and Nikolenko, Sergey}, title = "{The Russian Drug Reaction Corpus and Neural Models for Drug Reactions and Effectiveness Detection in User Reviews}", journal = {Bioinformatics}, year = {2020}, month = {07}, issn = {1367-4803}, doi = {10.1093/bioinformatics/btaa675}, url = {https://doi.org/10.1093/bioinformatics/btaa675}, note = {btaa675}, eprint = {https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btaa675/33539752/btaa675.pdf}, } ``` [2] Tutubalina, EV and Miftahutdinov, Z Sh and Nugmanov, RI and Madzhidov, TI and Nikolenko, SI and Alimova, IS and Tropsha, AE Using semantic analysis of texts for the identification of drugs with similar therapeutic effects.//Russian Chemical Bulletin. – 2017. – Т. 66. – №. 11. – С. 2180-2189. [link to paper](https://www.researchgate.net/profile/Elena_Tutubalina/publication/323751823_Using_semantic_analysis_of_texts_for_the_identification_of_drugs_with_similar_therapeutic_effects/links/5bf7cfc3299bf1a0202cbc1f/Using-semantic-analysis-of-texts-for-the-identification-of-drugs-with-similar-therapeutic-effects.pdf) ``` @article{tutubalina2017using, title={Using semantic analysis of texts for the identification of drugs with similar therapeutic effects}, author={Tutubalina, EV and Miftahutdinov, Z Sh and Nugmanov, RI and Madzhidov, TI and Nikolenko, SI and Alimova, IS and Tropsha, AE}, journal={Russian Chemical Bulletin}, volume={66}, number={11}, pages={2180--2189}, year={2017}, publisher={Springer} } ```
{"language": ["ru", "en"], "tags": ["bio", "med", "biomedical"]}
null
cimm-kzn/endr-bert
[ "transformers", "pytorch", "bio", "med", "biomedical", "ru", "en", "arxiv:2004.03659", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[ "2004.03659" ]
[ "ru", "en" ]
TAGS #transformers #pytorch #bio #med #biomedical #ru #en #arxiv-2004.03659 #endpoints_compatible #region-us
## EnDR-BERT EnDR-BERT - Multilingual, Cased, which pretrained on the english collection of consumer comments on drug administration from [2]. Pre-training was based on the original BERT code provided by Google. In particular, Multi-BERT was for used for initialization and all the parameters are the same as in Multi-BERT. Training details are described in our paper. \ link: URL ## Citing & Authors If you find this repository helpful, feel free to cite our publication: [1] Tutubalina E, Alimova I, Miftahutdinov Z, et al. The Russian Drug Reaction Corpus and Neural Models for Drug Reactions and Effectiveness Detection in User Reviews.//Bioinformatics. - 2020. preprint: URL [2] Tutubalina, EV and Miftahutdinov, Z Sh and Nugmanov, RI and Madzhidov, TI and Nikolenko, SI and Alimova, IS and Tropsha, AE Using semantic analysis of texts for the identification of drugs with similar therapeutic effects.//Russian Chemical Bulletin. – 2017. – Т. 66. – №. 11. – С. 2180-2189. link to paper
[ "## EnDR-BERT\n\n EnDR-BERT - Multilingual, Cased, which pretrained on the english collection of consumer comments on drug administration from [2]. Pre-training was based on the original BERT code provided by Google. In particular, Multi-BERT was for used for initialization and all the parameters are the same as in Multi-BERT. Training details are described in our paper. \\\n link: URL\n\n \n ## Citing & Authors\n\n If you find this repository helpful, feel free to cite our publication:\n\n [1] Tutubalina E, Alimova I, Miftahutdinov Z, et al. The Russian Drug Reaction Corpus and Neural Models for Drug Reactions and Effectiveness Detection in User Reviews.//Bioinformatics. - 2020. \n\n preprint: URL\n \n [2] Tutubalina, EV and Miftahutdinov, Z Sh and Nugmanov, RI and Madzhidov, TI and Nikolenko, SI and Alimova, IS and Tropsha, AE Using semantic analysis of texts for the identification of drugs with similar therapeutic effects.//Russian Chemical Bulletin. – 2017. – Т. 66. – №. 11. – С. 2180-2189.\n link to paper" ]
[ "TAGS\n#transformers #pytorch #bio #med #biomedical #ru #en #arxiv-2004.03659 #endpoints_compatible #region-us \n", "## EnDR-BERT\n\n EnDR-BERT - Multilingual, Cased, which pretrained on the english collection of consumer comments on drug administration from [2]. Pre-training was based on the original BERT code provided by Google. In particular, Multi-BERT was for used for initialization and all the parameters are the same as in Multi-BERT. Training details are described in our paper. \\\n link: URL\n\n \n ## Citing & Authors\n\n If you find this repository helpful, feel free to cite our publication:\n\n [1] Tutubalina E, Alimova I, Miftahutdinov Z, et al. The Russian Drug Reaction Corpus and Neural Models for Drug Reactions and Effectiveness Detection in User Reviews.//Bioinformatics. - 2020. \n\n preprint: URL\n \n [2] Tutubalina, EV and Miftahutdinov, Z Sh and Nugmanov, RI and Madzhidov, TI and Nikolenko, SI and Alimova, IS and Tropsha, AE Using semantic analysis of texts for the identification of drugs with similar therapeutic effects.//Russian Chemical Bulletin. – 2017. – Т. 66. – №. 11. – С. 2180-2189.\n link to paper" ]
[ 42, 267 ]
[ "passage: TAGS\n#transformers #pytorch #bio #med #biomedical #ru #en #arxiv-2004.03659 #endpoints_compatible #region-us \n## EnDR-BERT\n\n EnDR-BERT - Multilingual, Cased, which pretrained on the english collection of consumer comments on drug administration from [2]. Pre-training was based on the original BERT code provided by Google. In particular, Multi-BERT was for used for initialization and all the parameters are the same as in Multi-BERT. Training details are described in our paper. \\\n link: URL\n\n \n ## Citing & Authors\n\n If you find this repository helpful, feel free to cite our publication:\n\n [1] Tutubalina E, Alimova I, Miftahutdinov Z, et al. The Russian Drug Reaction Corpus and Neural Models for Drug Reactions and Effectiveness Detection in User Reviews.//Bioinformatics. - 2020. \n\n preprint: URL\n \n [2] Tutubalina, EV and Miftahutdinov, Z Sh and Nugmanov, RI and Madzhidov, TI and Nikolenko, SI and Alimova, IS and Tropsha, AE Using semantic analysis of texts for the identification of drugs with similar therapeutic effects.//Russian Chemical Bulletin. – 2017. – Т. 66. – №. 11. – С. 2180-2189.\n link to paper" ]
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transformers
## EnRuDR-BERT EnRuDR-BERT - Multilingual, Cased, which pretrained on the raw part of the RuDReC corpus (1.4M reviews) and english collection of consumer comments on drug administration from [2]. Pre-training was based on the [original BERT code](https://github.com/google-research/bert) provided by Google. In particular, Multi-BERT was for used for initialization; vocabulary of Russian subtokens and parameters are the same as in Multi-BERT. Training details are described in our paper. \ link: https://yadi.sk/d/-PTn0xhk1PqvgQ ## Citing & Authors If you find this repository helpful, feel free to cite our publication: [1] Tutubalina E, Alimova I, Miftahutdinov Z, et al. The Russian Drug Reaction Corpus and Neural Models for Drug Reactions and Effectiveness Detection in User Reviews.//Bioinformatics. - 2020. preprint: https://arxiv.org/abs/2004.03659 ``` @article{10.1093/bioinformatics/btaa675, author = {Tutubalina, Elena and Alimova, Ilseyar and Miftahutdinov, Zulfat and Sakhovskiy, Andrey and Malykh, Valentin and Nikolenko, Sergey}, title = "{The Russian Drug Reaction Corpus and Neural Models for Drug Reactions and Effectiveness Detection in User Reviews}", journal = {Bioinformatics}, year = {2020}, month = {07}, issn = {1367-4803}, doi = {10.1093/bioinformatics/btaa675}, url = {https://doi.org/10.1093/bioinformatics/btaa675}, note = {btaa675}, eprint = {https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btaa675/33539752/btaa675.pdf}, } ``` [2] Tutubalina, EV and Miftahutdinov, Z Sh and Nugmanov, RI and Madzhidov, TI and Nikolenko, SI and Alimova, IS and Tropsha, AE Using semantic analysis of texts for the identification of drugs with similar therapeutic effects.//Russian Chemical Bulletin. – 2017. – Т. 66. – №. 11. – С. 2180-2189. [link to paper](https://www.researchgate.net/profile/Elena_Tutubalina/publication/323751823_Using_semantic_analysis_of_texts_for_the_identification_of_drugs_with_similar_therapeutic_effects/links/5bf7cfc3299bf1a0202cbc1f/Using-semantic-analysis-of-texts-for-the-identification-of-drugs-with-similar-therapeutic-effects.pdf) ``` @article{tutubalina2017using, title={Using semantic analysis of texts for the identification of drugs with similar therapeutic effects}, author={Tutubalina, EV and Miftahutdinov, Z Sh and Nugmanov, RI and Madzhidov, TI and Nikolenko, SI and Alimova, IS and Tropsha, AE}, journal={Russian Chemical Bulletin}, volume={66}, number={11}, pages={2180--2189}, year={2017}, publisher={Springer} } ```
{"language": ["ru", "en"], "tags": ["bio", "med", "biomedical"]}
null
cimm-kzn/enrudr-bert
[ "transformers", "pytorch", "bio", "med", "biomedical", "ru", "en", "arxiv:2004.03659", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[ "2004.03659" ]
[ "ru", "en" ]
TAGS #transformers #pytorch #bio #med #biomedical #ru #en #arxiv-2004.03659 #endpoints_compatible #region-us
## EnRuDR-BERT EnRuDR-BERT - Multilingual, Cased, which pretrained on the raw part of the RuDReC corpus (1.4M reviews) and english collection of consumer comments on drug administration from [2]. Pre-training was based on the original BERT code provided by Google. In particular, Multi-BERT was for used for initialization; vocabulary of Russian subtokens and parameters are the same as in Multi-BERT. Training details are described in our paper. \ link: URL ## Citing & Authors If you find this repository helpful, feel free to cite our publication: [1] Tutubalina E, Alimova I, Miftahutdinov Z, et al. The Russian Drug Reaction Corpus and Neural Models for Drug Reactions and Effectiveness Detection in User Reviews.//Bioinformatics. - 2020. preprint: URL [2] Tutubalina, EV and Miftahutdinov, Z Sh and Nugmanov, RI and Madzhidov, TI and Nikolenko, SI and Alimova, IS and Tropsha, AE Using semantic analysis of texts for the identification of drugs with similar therapeutic effects.//Russian Chemical Bulletin. – 2017. – Т. 66. – №. 11. – С. 2180-2189. link to paper
[ "## EnRuDR-BERT\n\nEnRuDR-BERT - Multilingual, Cased, which pretrained on the raw part of the RuDReC corpus (1.4M reviews) and english collection of consumer comments on drug administration from [2]. Pre-training was based on the original BERT code provided by Google. In particular, Multi-BERT was for used for initialization; vocabulary of Russian subtokens and parameters are the same as in Multi-BERT. Training details are described in our paper. \\\n link: URL", "## Citing & Authors\n\nIf you find this repository helpful, feel free to cite our publication:\n\n[1] Tutubalina E, Alimova I, Miftahutdinov Z, et al. The Russian Drug Reaction Corpus and Neural Models for Drug Reactions and Effectiveness Detection in User Reviews.//Bioinformatics. - 2020. \n \n preprint: URL\n\n[2] Tutubalina, EV and Miftahutdinov, Z Sh and Nugmanov, RI and Madzhidov, TI and Nikolenko, SI and Alimova, IS and Tropsha, AE Using semantic analysis of texts for the identification of drugs with similar therapeutic effects.//Russian Chemical Bulletin. – 2017. – Т. 66. – №. 11. – С. 2180-2189.\n link to paper" ]
[ "TAGS\n#transformers #pytorch #bio #med #biomedical #ru #en #arxiv-2004.03659 #endpoints_compatible #region-us \n", "## EnRuDR-BERT\n\nEnRuDR-BERT - Multilingual, Cased, which pretrained on the raw part of the RuDReC corpus (1.4M reviews) and english collection of consumer comments on drug administration from [2]. Pre-training was based on the original BERT code provided by Google. In particular, Multi-BERT was for used for initialization; vocabulary of Russian subtokens and parameters are the same as in Multi-BERT. Training details are described in our paper. \\\n link: URL", "## Citing & Authors\n\nIf you find this repository helpful, feel free to cite our publication:\n\n[1] Tutubalina E, Alimova I, Miftahutdinov Z, et al. The Russian Drug Reaction Corpus and Neural Models for Drug Reactions and Effectiveness Detection in User Reviews.//Bioinformatics. - 2020. \n \n preprint: URL\n\n[2] Tutubalina, EV and Miftahutdinov, Z Sh and Nugmanov, RI and Madzhidov, TI and Nikolenko, SI and Alimova, IS and Tropsha, AE Using semantic analysis of texts for the identification of drugs with similar therapeutic effects.//Russian Chemical Bulletin. – 2017. – Т. 66. – №. 11. – С. 2180-2189.\n link to paper" ]
[ 42, 115, 175 ]
[ "passage: TAGS\n#transformers #pytorch #bio #med #biomedical #ru #en #arxiv-2004.03659 #endpoints_compatible #region-us \n## EnRuDR-BERT\n\nEnRuDR-BERT - Multilingual, Cased, which pretrained on the raw part of the RuDReC corpus (1.4M reviews) and english collection of consumer comments on drug administration from [2]. Pre-training was based on the original BERT code provided by Google. In particular, Multi-BERT was for used for initialization; vocabulary of Russian subtokens and parameters are the same as in Multi-BERT. Training details are described in our paper. \\\n link: URL## Citing & Authors\n\nIf you find this repository helpful, feel free to cite our publication:\n\n[1] Tutubalina E, Alimova I, Miftahutdinov Z, et al. The Russian Drug Reaction Corpus and Neural Models for Drug Reactions and Effectiveness Detection in User Reviews.//Bioinformatics. - 2020. \n \n preprint: URL\n\n[2] Tutubalina, EV and Miftahutdinov, Z Sh and Nugmanov, RI and Madzhidov, TI and Nikolenko, SI and Alimova, IS and Tropsha, AE Using semantic analysis of texts for the identification of drugs with similar therapeutic effects.//Russian Chemical Bulletin. – 2017. – Т. 66. – №. 11. – С. 2180-2189.\n link to paper" ]
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null
null
transformers
## RuDR-BERT RuDR-BERT - Multilingual, Cased, which pretrained on the raw part of the RuDReC corpus (1.4M reviews). Pre-training was based on the [original BERT code](https://github.com/google-research/bert) provided by Google. In particular, Multi-BERT was for used for initialization; vocabulary of Russian subtokens and parameters are the same as in Multi-BERT. Training details are described in our paper. \ link: https://yadi.sk/d/-PTn0xhk1PqvgQ ## Citing & Authors If you find this repository helpful, feel free to cite our publication: [1] Tutubalina E, Alimova I, Miftahutdinov Z, et al. The Russian Drug Reaction Corpus and Neural Models for Drug Reactions and Effectiveness Detection in User Reviews. preprint: https://arxiv.org/abs/2004.03659 ``` @article{10.1093/bioinformatics/btaa675, author = {Tutubalina, Elena and Alimova, Ilseyar and Miftahutdinov, Zulfat and Sakhovskiy, Andrey and Malykh, Valentin and Nikolenko, Sergey}, title = "{The Russian Drug Reaction Corpus and Neural Models for Drug Reactions and Effectiveness Detection in User Reviews}", journal = {Bioinformatics}, year = {2020}, month = {07}, issn = {1367-4803}, doi = {10.1093/bioinformatics/btaa675}, url = {https://doi.org/10.1093/bioinformatics/btaa675}, note = {btaa675}, eprint = {https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btaa675/33539752/btaa675.pdf}, } ``` [2] Tutubalina, EV and Miftahutdinov, Z Sh and Nugmanov, RI and Madzhidov, TI and Nikolenko, SI and Alimova, IS and Tropsha, AE Using semantic analysis of texts for the identification of drugs with similar therapeutic effects. [link to paper](https://www.researchgate.net/profile/Elena_Tutubalina/publication/323751823_Using_semantic_analysis_of_texts_for_the_identification_of_drugs_with_similar_therapeutic_effects/links/5bf7cfc3299bf1a0202cbc1f/Using-semantic-analysis-of-texts-for-the-identification-of-drugs-with-similar-therapeutic-effects.pdf) ``` @article{tutubalina2017using, title={Using semantic analysis of texts for the identification of drugs with similar therapeutic effects}, author={Tutubalina, EV and Miftahutdinov, Z Sh and Nugmanov, RI and Madzhidov, TI and Nikolenko, SI and Alimova, IS and Tropsha, AE}, journal={Russian Chemical Bulletin}, volume={66}, number={11}, pages={2180--2189}, year={2017}, publisher={Springer} } ```
{"language": ["ru"], "tags": ["bio", "med", "biomedical"]}
null
cimm-kzn/rudr-bert
[ "transformers", "pytorch", "bio", "med", "biomedical", "ru", "arxiv:2004.03659", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[ "2004.03659" ]
[ "ru" ]
TAGS #transformers #pytorch #bio #med #biomedical #ru #arxiv-2004.03659 #endpoints_compatible #region-us
## RuDR-BERT RuDR-BERT - Multilingual, Cased, which pretrained on the raw part of the RuDReC corpus (1.4M reviews). Pre-training was based on the original BERT code provided by Google. In particular, Multi-BERT was for used for initialization; vocabulary of Russian subtokens and parameters are the same as in Multi-BERT. Training details are described in our paper. \ link: URL ## Citing & Authors If you find this repository helpful, feel free to cite our publication: [1] Tutubalina E, Alimova I, Miftahutdinov Z, et al. The Russian Drug Reaction Corpus and Neural Models for Drug Reactions and Effectiveness Detection in User Reviews. preprint: URL [2] Tutubalina, EV and Miftahutdinov, Z Sh and Nugmanov, RI and Madzhidov, TI and Nikolenko, SI and Alimova, IS and Tropsha, AE Using semantic analysis of texts for the identification of drugs with similar therapeutic effects. link to paper
[ "## RuDR-BERT\n\nRuDR-BERT - Multilingual, Cased, which pretrained on the raw part of the RuDReC corpus (1.4M reviews). Pre-training was based on the original BERT code provided by Google. In particular, Multi-BERT was for used for initialization; vocabulary of Russian subtokens and parameters are the same as in Multi-BERT. Training details are described in our paper. \\\n link: URL", "## Citing & Authors\n\nIf you find this repository helpful, feel free to cite our publication:\n\n[1] Tutubalina E, Alimova I, Miftahutdinov Z, et al. The Russian Drug Reaction Corpus and Neural Models for Drug Reactions and Effectiveness Detection in User Reviews. \n \n preprint: URL\n\n[2] Tutubalina, EV and Miftahutdinov, Z Sh and Nugmanov, RI and Madzhidov, TI and Nikolenko, SI and Alimova, IS and Tropsha, AE Using semantic analysis of texts for the identification of drugs with similar therapeutic effects.\n link to paper" ]
[ "TAGS\n#transformers #pytorch #bio #med #biomedical #ru #arxiv-2004.03659 #endpoints_compatible #region-us \n", "## RuDR-BERT\n\nRuDR-BERT - Multilingual, Cased, which pretrained on the raw part of the RuDReC corpus (1.4M reviews). Pre-training was based on the original BERT code provided by Google. In particular, Multi-BERT was for used for initialization; vocabulary of Russian subtokens and parameters are the same as in Multi-BERT. Training details are described in our paper. \\\n link: URL", "## Citing & Authors\n\nIf you find this repository helpful, feel free to cite our publication:\n\n[1] Tutubalina E, Alimova I, Miftahutdinov Z, et al. The Russian Drug Reaction Corpus and Neural Models for Drug Reactions and Effectiveness Detection in User Reviews. \n \n preprint: URL\n\n[2] Tutubalina, EV and Miftahutdinov, Z Sh and Nugmanov, RI and Madzhidov, TI and Nikolenko, SI and Alimova, IS and Tropsha, AE Using semantic analysis of texts for the identification of drugs with similar therapeutic effects.\n link to paper" ]
[ 40, 101, 141 ]
[ "passage: TAGS\n#transformers #pytorch #bio #med #biomedical #ru #arxiv-2004.03659 #endpoints_compatible #region-us \n## RuDR-BERT\n\nRuDR-BERT - Multilingual, Cased, which pretrained on the raw part of the RuDReC corpus (1.4M reviews). Pre-training was based on the original BERT code provided by Google. In particular, Multi-BERT was for used for initialization; vocabulary of Russian subtokens and parameters are the same as in Multi-BERT. Training details are described in our paper. \\\n link: URL## Citing & Authors\n\nIf you find this repository helpful, feel free to cite our publication:\n\n[1] Tutubalina E, Alimova I, Miftahutdinov Z, et al. The Russian Drug Reaction Corpus and Neural Models for Drug Reactions and Effectiveness Detection in User Reviews. \n \n preprint: URL\n\n[2] Tutubalina, EV and Miftahutdinov, Z Sh and Nugmanov, RI and Madzhidov, TI and Nikolenko, SI and Alimova, IS and Tropsha, AE Using semantic analysis of texts for the identification of drugs with similar therapeutic effects.\n link to paper" ]
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null
null
null
End-2-End with english
{}
null
cjcu/End2End-asr
[ "region:us" ]
2022-03-02T23:29:05+00:00
[]
[]
TAGS #region-us
End-2-End with english
[]
[ "TAGS\n#region-us \n" ]
[ 6 ]
[ "passage: TAGS\n#region-us \n" ]
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null
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transformers
<!-- 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. --> # afriberta_base-finetuned-tydiqa This model is a fine-tuned version of [castorini/afriberta_base](https://huggingface.co/castorini/afriberta_base) on the tydiqa dataset. It achieves the following results on the evaluation set: - Loss: 2.3728 ## 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 | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 192 | 2.1359 | | No log | 2.0 | 384 | 2.3409 | | 0.8353 | 3.0 | 576 | 2.3728 | ### Framework versions - Transformers 4.14.1 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3
{"language": ["sw"], "tags": ["generated_from_trainer"], "datasets": ["tydiqa"], "model-index": [{"name": "afriberta_base-finetuned-tydiqa", "results": []}]}
question-answering
cjrowe/afriberta_base-finetuned-tydiqa
[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "question-answering", "generated_from_trainer", "sw", "dataset:tydiqa", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[ "sw" ]
TAGS #transformers #pytorch #tensorboard #xlm-roberta #question-answering #generated_from_trainer #sw #dataset-tydiqa #endpoints_compatible #region-us
afriberta\_base-finetuned-tydiqa ================================ This model is a fine-tuned version of castorini/afriberta\_base on the tydiqa dataset. It achieves the following results on the evaluation set: * Loss: 2.3728 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 ### Framework versions * Transformers 4.14.1 * Pytorch 1.10.0+cu111 * Datasets 1.16.1 * Tokenizers 0.10.3
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* Transformers 4.14.1\n* Pytorch 1.10.0+cu111\n* Datasets 1.16.1\n* Tokenizers 0.10.3" ]
[ "TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #question-answering #generated_from_trainer #sw #dataset-tydiqa #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* Transformers 4.14.1\n* Pytorch 1.10.0+cu111\n* Datasets 1.16.1\n* Tokenizers 0.10.3" ]
[ 54, 98, 4, 33 ]
[ "passage: TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #question-answering #generated_from_trainer #sw #dataset-tydiqa #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3### Training results### Framework versions\n\n\n* Transformers 4.14.1\n* Pytorch 1.10.0+cu111\n* Datasets 1.16.1\n* Tokenizers 0.10.3" ]
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null
null
transformers
<!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # nlu_sherlock_model 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: ## 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: - 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': -947, '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: float32 ### Training results ### Framework versions - Transformers 4.16.2 - TensorFlow 2.8.0 - Datasets 1.18.3 - Tokenizers 0.11.0
{"license": "mit", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "nlu_sherlock_model", "results": []}]}
fill-mask
ckenlam/nlu_sherlock_model
[ "transformers", "tf", "roberta", "fill-mask", "generated_from_keras_callback", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[]
TAGS #transformers #tf #roberta #fill-mask #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #region-us
# nlu_sherlock_model This model is a fine-tuned version of roberta-base on an unknown dataset. It achieves the following results on the evaluation set: ## 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: - 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': -947, '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: float32 ### Training results ### Framework versions - Transformers 4.16.2 - TensorFlow 2.8.0 - Datasets 1.18.3 - Tokenizers 0.11.0
[ "# nlu_sherlock_model\n\nThis model is a fine-tuned version of roberta-base on an unknown dataset.\nIt achieves the following results on the evaluation set:", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- 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': -947, '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}\n- training_precision: float32", "### Training results", "### Framework versions\n\n- Transformers 4.16.2\n- TensorFlow 2.8.0\n- Datasets 1.18.3\n- Tokenizers 0.11.0" ]
[ "TAGS\n#transformers #tf #roberta #fill-mask #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #region-us \n", "# nlu_sherlock_model\n\nThis model is a fine-tuned version of roberta-base on an unknown dataset.\nIt achieves the following results on the evaluation set:", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- 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': -947, '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}\n- training_precision: float32", "### Training results", "### Framework versions\n\n- Transformers 4.16.2\n- TensorFlow 2.8.0\n- Datasets 1.18.3\n- Tokenizers 0.11.0" ]
[ 52, 41, 6, 12, 8, 3, 268, 4, 34 ]
[ "passage: TAGS\n#transformers #tf #roberta #fill-mask #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #region-us \n# nlu_sherlock_model\n\nThis model is a fine-tuned version of roberta-base on an unknown dataset.\nIt achieves the following results on the evaluation set:## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- 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': -947, '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}\n- training_precision: float32### Training results### Framework versions\n\n- Transformers 4.16.2\n- TensorFlow 2.8.0\n- Datasets 1.18.3\n- Tokenizers 0.11.0" ]
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null
null
transformers
<!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # nlu_sherlock_model_20220220 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: ## 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: - 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': -955, '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: float32 ### Training results ### Framework versions - Transformers 4.16.2 - TensorFlow 2.8.0 - Datasets 1.18.3 - Tokenizers 0.11.0
{"license": "mit", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "nlu_sherlock_model_20220220", "results": []}]}
fill-mask
ckenlam/nlu_sherlock_model_20220220
[ "transformers", "tf", "roberta", "fill-mask", "generated_from_keras_callback", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[]
TAGS #transformers #tf #roberta #fill-mask #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #region-us
# nlu_sherlock_model_20220220 This model is a fine-tuned version of roberta-base on an unknown dataset. It achieves the following results on the evaluation set: ## 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: - 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': -955, '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: float32 ### Training results ### Framework versions - Transformers 4.16.2 - TensorFlow 2.8.0 - Datasets 1.18.3 - Tokenizers 0.11.0
[ "# nlu_sherlock_model_20220220\n\nThis model is a fine-tuned version of roberta-base on an unknown dataset.\nIt achieves the following results on the evaluation set:", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- 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': -955, '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}\n- training_precision: float32", "### Training results", "### Framework versions\n\n- Transformers 4.16.2\n- TensorFlow 2.8.0\n- Datasets 1.18.3\n- Tokenizers 0.11.0" ]
[ "TAGS\n#transformers #tf #roberta #fill-mask #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #region-us \n", "# nlu_sherlock_model_20220220\n\nThis model is a fine-tuned version of roberta-base on an unknown dataset.\nIt achieves the following results on the evaluation set:", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- 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': -955, '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}\n- training_precision: float32", "### Training results", "### Framework versions\n\n- Transformers 4.16.2\n- TensorFlow 2.8.0\n- Datasets 1.18.3\n- Tokenizers 0.11.0" ]
[ 52, 45, 6, 12, 8, 3, 267, 4, 34 ]
[ "passage: TAGS\n#transformers #tf #roberta #fill-mask #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #region-us \n# nlu_sherlock_model_20220220\n\nThis model is a fine-tuned version of roberta-base on an unknown dataset.\nIt achieves the following results on the evaluation set:## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- 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': -955, '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}\n- training_precision: float32### Training results### Framework versions\n\n- Transformers 4.16.2\n- TensorFlow 2.8.0\n- Datasets 1.18.3\n- Tokenizers 0.11.0" ]
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null
null
transformers
# CKIP ALBERT Base Chinese This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition). 這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。 ## Homepage - https://github.com/ckiplab/ckip-transformers ## Contributers - [Mu Yang](https://muyang.pro) at [CKIP](https://ckip.iis.sinica.edu.tw) (Author & Maintainer) ## Usage Please use BertTokenizerFast as tokenizer instead of AutoTokenizer. 請使用 BertTokenizerFast 而非 AutoTokenizer。 ``` from transformers import ( BertTokenizerFast, AutoModel, ) tokenizer = BertTokenizerFast.from_pretrained('bert-base-chinese') model = AutoModel.from_pretrained('ckiplab/albert-base-chinese-ner') ``` For full usage and more information, please refer to https://github.com/ckiplab/ckip-transformers. 有關完整使用方法及其他資訊，請參見 https://github.com/ckiplab/ckip-transformers 。
{"language": ["zh"], "license": "gpl-3.0", "tags": ["pytorch", "token-classification", "albert", "zh"], "thumbnail": "https://ckip.iis.sinica.edu.tw/files/ckip_logo.png"}
token-classification
ckiplab/albert-base-chinese-ner
[ "transformers", "pytorch", "albert", "token-classification", "zh", "license:gpl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[ "zh" ]
TAGS #transformers #pytorch #albert #token-classification #zh #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us
# CKIP ALBERT Base Chinese This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition). 這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。 ## Homepage - URL ## Contributers - Mu Yang at CKIP (Author & Maintainer) ## Usage Please use BertTokenizerFast as tokenizer instead of AutoTokenizer. 請使用 BertTokenizerFast 而非 AutoTokenizer。 For full usage and more information, please refer to URL 有關完整使用方法及其他資訊，請參見 URL 。
[ "# CKIP ALBERT Base Chinese\n\nThis project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).\n\n這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。", "## Homepage\n\n- URL", "## Contributers\n\n- Mu Yang at CKIP (Author & Maintainer)", "## Usage\n\nPlease use BertTokenizerFast as tokenizer instead of AutoTokenizer.\n\n請使用 BertTokenizerFast 而非 AutoTokenizer。\n\n\n\nFor full usage and more information, please refer to URL\n\n有關完整使用方法及其他資訊，請參見 URL 。" ]
[ "TAGS\n#transformers #pytorch #albert #token-classification #zh #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# CKIP ALBERT Base Chinese\n\nThis project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).\n\n這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。", "## Homepage\n\n- URL", "## Contributers\n\n- Mu Yang at CKIP (Author & Maintainer)", "## Usage\n\nPlease use BertTokenizerFast as tokenizer instead of AutoTokenizer.\n\n請使用 BertTokenizerFast 而非 AutoTokenizer。\n\n\n\nFor full usage and more information, please refer to URL\n\n有關完整使用方法及其他資訊，請參見 URL 。" ]
[ 48, 93, 4, 19, 63 ]
[ "passage: TAGS\n#transformers #pytorch #albert #token-classification #zh #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us \n# CKIP ALBERT Base Chinese\n\nThis project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).\n\n這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。## Homepage\n\n- URL## Contributers\n\n- Mu Yang at CKIP (Author & Maintainer)## Usage\n\nPlease use BertTokenizerFast as tokenizer instead of AutoTokenizer.\n\n請使用 BertTokenizerFast 而非 AutoTokenizer。\n\n\n\nFor full usage and more information, please refer to URL\n\n有關完整使用方法及其他資訊，請參見 URL 。" ]
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null
null
transformers
# CKIP ALBERT Base Chinese This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition). 這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。 ## Homepage - https://github.com/ckiplab/ckip-transformers ## Contributers - [Mu Yang](https://muyang.pro) at [CKIP](https://ckip.iis.sinica.edu.tw) (Author & Maintainer) ## Usage Please use BertTokenizerFast as tokenizer instead of AutoTokenizer. 請使用 BertTokenizerFast 而非 AutoTokenizer。 ``` from transformers import ( BertTokenizerFast, AutoModel, ) tokenizer = BertTokenizerFast.from_pretrained('bert-base-chinese') model = AutoModel.from_pretrained('ckiplab/albert-base-chinese-pos') ``` For full usage and more information, please refer to https://github.com/ckiplab/ckip-transformers. 有關完整使用方法及其他資訊，請參見 https://github.com/ckiplab/ckip-transformers 。
{"language": ["zh"], "license": "gpl-3.0", "tags": ["pytorch", "token-classification", "albert", "zh"], "thumbnail": "https://ckip.iis.sinica.edu.tw/files/ckip_logo.png"}
token-classification
ckiplab/albert-base-chinese-pos
[ "transformers", "pytorch", "albert", "token-classification", "zh", "license:gpl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[ "zh" ]
TAGS #transformers #pytorch #albert #token-classification #zh #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us
# CKIP ALBERT Base Chinese This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition). 這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。 ## Homepage - URL ## Contributers - Mu Yang at CKIP (Author & Maintainer) ## Usage Please use BertTokenizerFast as tokenizer instead of AutoTokenizer. 請使用 BertTokenizerFast 而非 AutoTokenizer。 For full usage and more information, please refer to URL 有關完整使用方法及其他資訊，請參見 URL 。
[ "# CKIP ALBERT Base Chinese\n\nThis project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).\n\n這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。", "## Homepage\n\n- URL", "## Contributers\n\n- Mu Yang at CKIP (Author & Maintainer)", "## Usage\n\nPlease use BertTokenizerFast as tokenizer instead of AutoTokenizer.\n\n請使用 BertTokenizerFast 而非 AutoTokenizer。\n\n\n\nFor full usage and more information, please refer to URL\n\n有關完整使用方法及其他資訊，請參見 URL 。" ]
[ "TAGS\n#transformers #pytorch #albert #token-classification #zh #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# CKIP ALBERT Base Chinese\n\nThis project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).\n\n這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。", "## Homepage\n\n- URL", "## Contributers\n\n- Mu Yang at CKIP (Author & Maintainer)", "## Usage\n\nPlease use BertTokenizerFast as tokenizer instead of AutoTokenizer.\n\n請使用 BertTokenizerFast 而非 AutoTokenizer。\n\n\n\nFor full usage and more information, please refer to URL\n\n有關完整使用方法及其他資訊，請參見 URL 。" ]
[ 48, 93, 4, 19, 63 ]
[ "passage: TAGS\n#transformers #pytorch #albert #token-classification #zh #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us \n# CKIP ALBERT Base Chinese\n\nThis project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).\n\n這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。## Homepage\n\n- URL## Contributers\n\n- Mu Yang at CKIP (Author & Maintainer)## Usage\n\nPlease use BertTokenizerFast as tokenizer instead of AutoTokenizer.\n\n請使用 BertTokenizerFast 而非 AutoTokenizer。\n\n\n\nFor full usage and more information, please refer to URL\n\n有關完整使用方法及其他資訊，請參見 URL 。" ]
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null
null
transformers
# CKIP ALBERT Base Chinese This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition). 這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。 ## Homepage - https://github.com/ckiplab/ckip-transformers ## Contributers - [Mu Yang](https://muyang.pro) at [CKIP](https://ckip.iis.sinica.edu.tw) (Author & Maintainer) ## Usage Please use BertTokenizerFast as tokenizer instead of AutoTokenizer. 請使用 BertTokenizerFast 而非 AutoTokenizer。 ``` from transformers import ( BertTokenizerFast, AutoModel, ) tokenizer = BertTokenizerFast.from_pretrained('bert-base-chinese') model = AutoModel.from_pretrained('ckiplab/albert-base-chinese-ws') ``` For full usage and more information, please refer to https://github.com/ckiplab/ckip-transformers. 有關完整使用方法及其他資訊，請參見 https://github.com/ckiplab/ckip-transformers 。
{"language": ["zh"], "license": "gpl-3.0", "tags": ["pytorch", "token-classification", "albert", "zh"], "thumbnail": "https://ckip.iis.sinica.edu.tw/files/ckip_logo.png"}
token-classification
ckiplab/albert-base-chinese-ws
[ "transformers", "pytorch", "albert", "token-classification", "zh", "license:gpl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[ "zh" ]
TAGS #transformers #pytorch #albert #token-classification #zh #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us
# CKIP ALBERT Base Chinese This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition). 這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。 ## Homepage - URL ## Contributers - Mu Yang at CKIP (Author & Maintainer) ## Usage Please use BertTokenizerFast as tokenizer instead of AutoTokenizer. 請使用 BertTokenizerFast 而非 AutoTokenizer。 For full usage and more information, please refer to URL 有關完整使用方法及其他資訊，請參見 URL 。
[ "# CKIP ALBERT Base Chinese\n\nThis project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).\n\n這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。", "## Homepage\n\n- URL", "## Contributers\n\n- Mu Yang at CKIP (Author & Maintainer)", "## Usage\n\nPlease use BertTokenizerFast as tokenizer instead of AutoTokenizer.\n\n請使用 BertTokenizerFast 而非 AutoTokenizer。\n\n\n\nFor full usage and more information, please refer to URL\n\n有關完整使用方法及其他資訊，請參見 URL 。" ]
[ "TAGS\n#transformers #pytorch #albert #token-classification #zh #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# CKIP ALBERT Base Chinese\n\nThis project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).\n\n這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。", "## Homepage\n\n- URL", "## Contributers\n\n- Mu Yang at CKIP (Author & Maintainer)", "## Usage\n\nPlease use BertTokenizerFast as tokenizer instead of AutoTokenizer.\n\n請使用 BertTokenizerFast 而非 AutoTokenizer。\n\n\n\nFor full usage and more information, please refer to URL\n\n有關完整使用方法及其他資訊，請參見 URL 。" ]
[ 48, 93, 4, 19, 63 ]
[ "passage: TAGS\n#transformers #pytorch #albert #token-classification #zh #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us \n# CKIP ALBERT Base Chinese\n\nThis project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).\n\n這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。## Homepage\n\n- URL## Contributers\n\n- Mu Yang at CKIP (Author & Maintainer)## Usage\n\nPlease use BertTokenizerFast as tokenizer instead of AutoTokenizer.\n\n請使用 BertTokenizerFast 而非 AutoTokenizer。\n\n\n\nFor full usage and more information, please refer to URL\n\n有關完整使用方法及其他資訊，請參見 URL 。" ]
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null
null
transformers
# CKIP ALBERT Base Chinese This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition). 這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。 ## Homepage - https://github.com/ckiplab/ckip-transformers ## Contributers - [Mu Yang](https://muyang.pro) at [CKIP](https://ckip.iis.sinica.edu.tw) (Author & Maintainer) ## Usage Please use BertTokenizerFast as tokenizer instead of AutoTokenizer. 請使用 BertTokenizerFast 而非 AutoTokenizer。 ``` from transformers import ( BertTokenizerFast, AutoModel, ) tokenizer = BertTokenizerFast.from_pretrained('bert-base-chinese') model = AutoModel.from_pretrained('ckiplab/albert-base-chinese') ``` For full usage and more information, please refer to https://github.com/ckiplab/ckip-transformers. 有關完整使用方法及其他資訊，請參見 https://github.com/ckiplab/ckip-transformers 。
{"language": ["zh"], "license": "gpl-3.0", "tags": ["pytorch", "lm-head", "albert", "zh"], "thumbnail": "https://ckip.iis.sinica.edu.tw/files/ckip_logo.png"}
fill-mask
ckiplab/albert-base-chinese
[ "transformers", "pytorch", "albert", "fill-mask", "lm-head", "zh", "license:gpl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[ "zh" ]
TAGS #transformers #pytorch #albert #fill-mask #lm-head #zh #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us
# CKIP ALBERT Base Chinese This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition). 這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。 ## Homepage - URL ## Contributers - Mu Yang at CKIP (Author & Maintainer) ## Usage Please use BertTokenizerFast as tokenizer instead of AutoTokenizer. 請使用 BertTokenizerFast 而非 AutoTokenizer。 For full usage and more information, please refer to URL 有關完整使用方法及其他資訊，請參見 URL 。
[ "# CKIP ALBERT Base Chinese\n\nThis project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).\n\n這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。", "## Homepage\n\n- URL", "## Contributers\n\n- Mu Yang at CKIP (Author & Maintainer)", "## Usage\n\nPlease use BertTokenizerFast as tokenizer instead of AutoTokenizer.\n\n請使用 BertTokenizerFast 而非 AutoTokenizer。\n\n\n\nFor full usage and more information, please refer to URL\n\n有關完整使用方法及其他資訊，請參見 URL 。" ]
[ "TAGS\n#transformers #pytorch #albert #fill-mask #lm-head #zh #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# CKIP ALBERT Base Chinese\n\nThis project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).\n\n這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。", "## Homepage\n\n- URL", "## Contributers\n\n- Mu Yang at CKIP (Author & Maintainer)", "## Usage\n\nPlease use BertTokenizerFast as tokenizer instead of AutoTokenizer.\n\n請使用 BertTokenizerFast 而非 AutoTokenizer。\n\n\n\nFor full usage and more information, please refer to URL\n\n有關完整使用方法及其他資訊，請參見 URL 。" ]
[ 51, 93, 4, 19, 63 ]
[ "passage: TAGS\n#transformers #pytorch #albert #fill-mask #lm-head #zh #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us \n# CKIP ALBERT Base Chinese\n\nThis project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).\n\n這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。## Homepage\n\n- URL## Contributers\n\n- Mu Yang at CKIP (Author & Maintainer)## Usage\n\nPlease use BertTokenizerFast as tokenizer instead of AutoTokenizer.\n\n請使用 BertTokenizerFast 而非 AutoTokenizer。\n\n\n\nFor full usage and more information, please refer to URL\n\n有關完整使用方法及其他資訊，請參見 URL 。" ]
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null
null
transformers
# CKIP ALBERT Tiny Chinese This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition). 這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。 ## Homepage - https://github.com/ckiplab/ckip-transformers ## Contributers - [Mu Yang](https://muyang.pro) at [CKIP](https://ckip.iis.sinica.edu.tw) (Author & Maintainer) ## Usage Please use BertTokenizerFast as tokenizer instead of AutoTokenizer. 請使用 BertTokenizerFast 而非 AutoTokenizer。 ``` from transformers import ( BertTokenizerFast, AutoModel, ) tokenizer = BertTokenizerFast.from_pretrained('bert-base-chinese') model = AutoModel.from_pretrained('ckiplab/albert-tiny-chinese-ner') ``` For full usage and more information, please refer to https://github.com/ckiplab/ckip-transformers. 有關完整使用方法及其他資訊，請參見 https://github.com/ckiplab/ckip-transformers 。
{"language": ["zh"], "license": "gpl-3.0", "tags": ["pytorch", "token-classification", "albert", "zh"], "thumbnail": "https://ckip.iis.sinica.edu.tw/files/ckip_logo.png"}
token-classification
ckiplab/albert-tiny-chinese-ner
[ "transformers", "pytorch", "albert", "token-classification", "zh", "license:gpl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[ "zh" ]
TAGS #transformers #pytorch #albert #token-classification #zh #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us
# CKIP ALBERT Tiny Chinese This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition). 這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。 ## Homepage - URL ## Contributers - Mu Yang at CKIP (Author & Maintainer) ## Usage Please use BertTokenizerFast as tokenizer instead of AutoTokenizer. 請使用 BertTokenizerFast 而非 AutoTokenizer。 For full usage and more information, please refer to URL 有關完整使用方法及其他資訊，請參見 URL 。
[ "# CKIP ALBERT Tiny Chinese\n\nThis project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).\n\n這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。", "## Homepage\n\n- URL", "## Contributers\n\n- Mu Yang at CKIP (Author & Maintainer)", "## Usage\n\nPlease use BertTokenizerFast as tokenizer instead of AutoTokenizer.\n\n請使用 BertTokenizerFast 而非 AutoTokenizer。\n\n\n\nFor full usage and more information, please refer to URL\n\n有關完整使用方法及其他資訊，請參見 URL 。" ]
[ "TAGS\n#transformers #pytorch #albert #token-classification #zh #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# CKIP ALBERT Tiny Chinese\n\nThis project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).\n\n這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。", "## Homepage\n\n- URL", "## Contributers\n\n- Mu Yang at CKIP (Author & Maintainer)", "## Usage\n\nPlease use BertTokenizerFast as tokenizer instead of AutoTokenizer.\n\n請使用 BertTokenizerFast 而非 AutoTokenizer。\n\n\n\nFor full usage and more information, please refer to URL\n\n有關完整使用方法及其他資訊，請參見 URL 。" ]
[ 48, 94, 4, 19, 63 ]
[ "passage: TAGS\n#transformers #pytorch #albert #token-classification #zh #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us \n# CKIP ALBERT Tiny Chinese\n\nThis project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).\n\n這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。## Homepage\n\n- URL## Contributers\n\n- Mu Yang at CKIP (Author & Maintainer)## Usage\n\nPlease use BertTokenizerFast as tokenizer instead of AutoTokenizer.\n\n請使用 BertTokenizerFast 而非 AutoTokenizer。\n\n\n\nFor full usage and more information, please refer to URL\n\n有關完整使用方法及其他資訊，請參見 URL 。" ]
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null
null
transformers
# CKIP ALBERT Tiny Chinese This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition). 這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。 ## Homepage - https://github.com/ckiplab/ckip-transformers ## Contributers - [Mu Yang](https://muyang.pro) at [CKIP](https://ckip.iis.sinica.edu.tw) (Author & Maintainer) ## Usage Please use BertTokenizerFast as tokenizer instead of AutoTokenizer. 請使用 BertTokenizerFast 而非 AutoTokenizer。 ``` from transformers import ( BertTokenizerFast, AutoModel, ) tokenizer = BertTokenizerFast.from_pretrained('bert-base-chinese') model = AutoModel.from_pretrained('ckiplab/albert-tiny-chinese-pos') ``` For full usage and more information, please refer to https://github.com/ckiplab/ckip-transformers. 有關完整使用方法及其他資訊，請參見 https://github.com/ckiplab/ckip-transformers 。
{"language": ["zh"], "license": "gpl-3.0", "tags": ["pytorch", "token-classification", "albert", "zh"], "thumbnail": "https://ckip.iis.sinica.edu.tw/files/ckip_logo.png"}
token-classification
ckiplab/albert-tiny-chinese-pos
[ "transformers", "pytorch", "albert", "token-classification", "zh", "license:gpl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[ "zh" ]
TAGS #transformers #pytorch #albert #token-classification #zh #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us
# CKIP ALBERT Tiny Chinese This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition). 這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。 ## Homepage - URL ## Contributers - Mu Yang at CKIP (Author & Maintainer) ## Usage Please use BertTokenizerFast as tokenizer instead of AutoTokenizer. 請使用 BertTokenizerFast 而非 AutoTokenizer。 For full usage and more information, please refer to URL 有關完整使用方法及其他資訊，請參見 URL 。
[ "# CKIP ALBERT Tiny Chinese\n\nThis project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).\n\n這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。", "## Homepage\n\n- URL", "## Contributers\n\n- Mu Yang at CKIP (Author & Maintainer)", "## Usage\n\nPlease use BertTokenizerFast as tokenizer instead of AutoTokenizer.\n\n請使用 BertTokenizerFast 而非 AutoTokenizer。\n\n\n\nFor full usage and more information, please refer to URL\n\n有關完整使用方法及其他資訊，請參見 URL 。" ]
[ "TAGS\n#transformers #pytorch #albert #token-classification #zh #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# CKIP ALBERT Tiny Chinese\n\nThis project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).\n\n這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。", "## Homepage\n\n- URL", "## Contributers\n\n- Mu Yang at CKIP (Author & Maintainer)", "## Usage\n\nPlease use BertTokenizerFast as tokenizer instead of AutoTokenizer.\n\n請使用 BertTokenizerFast 而非 AutoTokenizer。\n\n\n\nFor full usage and more information, please refer to URL\n\n有關完整使用方法及其他資訊，請參見 URL 。" ]
[ 48, 94, 4, 19, 63 ]
[ "passage: TAGS\n#transformers #pytorch #albert #token-classification #zh #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us \n# CKIP ALBERT Tiny Chinese\n\nThis project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).\n\n這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。## Homepage\n\n- URL## Contributers\n\n- Mu Yang at CKIP (Author & Maintainer)## Usage\n\nPlease use BertTokenizerFast as tokenizer instead of AutoTokenizer.\n\n請使用 BertTokenizerFast 而非 AutoTokenizer。\n\n\n\nFor full usage and more information, please refer to URL\n\n有關完整使用方法及其他資訊，請參見 URL 。" ]
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null
null
transformers
# CKIP ALBERT Tiny Chinese This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition). 這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。 ## Homepage - https://github.com/ckiplab/ckip-transformers ## Contributers - [Mu Yang](https://muyang.pro) at [CKIP](https://ckip.iis.sinica.edu.tw) (Author & Maintainer) ## Usage Please use BertTokenizerFast as tokenizer instead of AutoTokenizer. 請使用 BertTokenizerFast 而非 AutoTokenizer。 ``` from transformers import ( BertTokenizerFast, AutoModel, ) tokenizer = BertTokenizerFast.from_pretrained('bert-base-chinese') model = AutoModel.from_pretrained('ckiplab/albert-tiny-chinese-ws') ``` For full usage and more information, please refer to https://github.com/ckiplab/ckip-transformers. 有關完整使用方法及其他資訊，請參見 https://github.com/ckiplab/ckip-transformers 。
{"language": ["zh"], "license": "gpl-3.0", "tags": ["pytorch", "token-classification", "albert", "zh"], "thumbnail": "https://ckip.iis.sinica.edu.tw/files/ckip_logo.png"}
token-classification
ckiplab/albert-tiny-chinese-ws
[ "transformers", "pytorch", "albert", "token-classification", "zh", "license:gpl-3.0", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[ "zh" ]
TAGS #transformers #pytorch #albert #token-classification #zh #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
# CKIP ALBERT Tiny Chinese This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition). 這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。 ## Homepage - URL ## Contributers - Mu Yang at CKIP (Author & Maintainer) ## Usage Please use BertTokenizerFast as tokenizer instead of AutoTokenizer. 請使用 BertTokenizerFast 而非 AutoTokenizer。 For full usage and more information, please refer to URL 有關完整使用方法及其他資訊，請參見 URL 。
[ "# CKIP ALBERT Tiny Chinese\n\nThis project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).\n\n這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。", "## Homepage\n\n- URL", "## Contributers\n\n- Mu Yang at CKIP (Author & Maintainer)", "## Usage\n\nPlease use BertTokenizerFast as tokenizer instead of AutoTokenizer.\n\n請使用 BertTokenizerFast 而非 AutoTokenizer。\n\n\n\nFor full usage and more information, please refer to URL\n\n有關完整使用方法及其他資訊，請參見 URL 。" ]
[ "TAGS\n#transformers #pytorch #albert #token-classification #zh #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n", "# CKIP ALBERT Tiny Chinese\n\nThis project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).\n\n這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。", "## Homepage\n\n- URL", "## Contributers\n\n- Mu Yang at CKIP (Author & Maintainer)", "## Usage\n\nPlease use BertTokenizerFast as tokenizer instead of AutoTokenizer.\n\n請使用 BertTokenizerFast 而非 AutoTokenizer。\n\n\n\nFor full usage and more information, please refer to URL\n\n有關完整使用方法及其他資訊，請參見 URL 。" ]
[ 52, 94, 4, 19, 63 ]
[ "passage: TAGS\n#transformers #pytorch #albert #token-classification #zh #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n# CKIP ALBERT Tiny Chinese\n\nThis project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).\n\n這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。## Homepage\n\n- URL## Contributers\n\n- Mu Yang at CKIP (Author & Maintainer)## Usage\n\nPlease use BertTokenizerFast as tokenizer instead of AutoTokenizer.\n\n請使用 BertTokenizerFast 而非 AutoTokenizer。\n\n\n\nFor full usage and more information, please refer to URL\n\n有關完整使用方法及其他資訊，請參見 URL 。" ]
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null
null
transformers
# CKIP ALBERT Tiny Chinese This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition). 這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。 ## Homepage - https://github.com/ckiplab/ckip-transformers ## Contributers - [Mu Yang](https://muyang.pro) at [CKIP](https://ckip.iis.sinica.edu.tw) (Author & Maintainer) ## Usage Please use BertTokenizerFast as tokenizer instead of AutoTokenizer. 請使用 BertTokenizerFast 而非 AutoTokenizer。 ``` from transformers import ( BertTokenizerFast, AutoModel, ) tokenizer = BertTokenizerFast.from_pretrained('bert-base-chinese') model = AutoModel.from_pretrained('ckiplab/albert-tiny-chinese') ``` For full usage and more information, please refer to https://github.com/ckiplab/ckip-transformers. 有關完整使用方法及其他資訊，請參見 https://github.com/ckiplab/ckip-transformers 。
{"language": ["zh"], "license": "gpl-3.0", "tags": ["pytorch", "lm-head", "albert", "zh"], "thumbnail": "https://ckip.iis.sinica.edu.tw/files/ckip_logo.png"}
fill-mask
ckiplab/albert-tiny-chinese
[ "transformers", "pytorch", "albert", "fill-mask", "lm-head", "zh", "license:gpl-3.0", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[ "zh" ]
TAGS #transformers #pytorch #albert #fill-mask #lm-head #zh #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
# CKIP ALBERT Tiny Chinese This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition). 這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。 ## Homepage - URL ## Contributers - Mu Yang at CKIP (Author & Maintainer) ## Usage Please use BertTokenizerFast as tokenizer instead of AutoTokenizer. 請使用 BertTokenizerFast 而非 AutoTokenizer。 For full usage and more information, please refer to URL 有關完整使用方法及其他資訊，請參見 URL 。
[ "# CKIP ALBERT Tiny Chinese\n\nThis project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).\n\n這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。", "## Homepage\n\n- URL", "## Contributers\n\n- Mu Yang at CKIP (Author & Maintainer)", "## Usage\n\nPlease use BertTokenizerFast as tokenizer instead of AutoTokenizer.\n\n請使用 BertTokenizerFast 而非 AutoTokenizer。\n\n\n\nFor full usage and more information, please refer to URL\n\n有關完整使用方法及其他資訊，請參見 URL 。" ]
[ "TAGS\n#transformers #pytorch #albert #fill-mask #lm-head #zh #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n", "# CKIP ALBERT Tiny Chinese\n\nThis project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).\n\n這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。", "## Homepage\n\n- URL", "## Contributers\n\n- Mu Yang at CKIP (Author & Maintainer)", "## Usage\n\nPlease use BertTokenizerFast as tokenizer instead of AutoTokenizer.\n\n請使用 BertTokenizerFast 而非 AutoTokenizer。\n\n\n\nFor full usage and more information, please refer to URL\n\n有關完整使用方法及其他資訊，請參見 URL 。" ]
[ 55, 94, 4, 19, 63 ]
[ "passage: TAGS\n#transformers #pytorch #albert #fill-mask #lm-head #zh #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n# CKIP ALBERT Tiny Chinese\n\nThis project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).\n\n這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。## Homepage\n\n- URL## Contributers\n\n- Mu Yang at CKIP (Author & Maintainer)## Usage\n\nPlease use BertTokenizerFast as tokenizer instead of AutoTokenizer.\n\n請使用 BertTokenizerFast 而非 AutoTokenizer。\n\n\n\nFor full usage and more information, please refer to URL\n\n有關完整使用方法及其他資訊，請參見 URL 。" ]
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null
null
transformers
# CKIP BERT Base Chinese This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition). 這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。 ## Homepage - https://github.com/ckiplab/ckip-transformers ## Contributers - [Mu Yang](https://muyang.pro) at [CKIP](https://ckip.iis.sinica.edu.tw) (Author & Maintainer) ## Usage Please use BertTokenizerFast as tokenizer instead of AutoTokenizer. 請使用 BertTokenizerFast 而非 AutoTokenizer。 ``` from transformers import ( BertTokenizerFast, AutoModel, ) tokenizer = BertTokenizerFast.from_pretrained('bert-base-chinese') model = AutoModel.from_pretrained('ckiplab/bert-base-chinese-ner') ``` For full usage and more information, please refer to https://github.com/ckiplab/ckip-transformers. 有關完整使用方法及其他資訊，請參見 https://github.com/ckiplab/ckip-transformers 。
{"language": ["zh"], "license": "gpl-3.0", "tags": ["pytorch", "token-classification", "bert", "zh"], "thumbnail": "https://ckip.iis.sinica.edu.tw/files/ckip_logo.png"}
token-classification
ckiplab/bert-base-chinese-ner
[ "transformers", "pytorch", "jax", "bert", "token-classification", "zh", "license:gpl-3.0", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[ "zh" ]
TAGS #transformers #pytorch #jax #bert #token-classification #zh #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
# CKIP BERT Base Chinese This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition). 這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。 ## Homepage - URL ## Contributers - Mu Yang at CKIP (Author & Maintainer) ## Usage Please use BertTokenizerFast as tokenizer instead of AutoTokenizer. 請使用 BertTokenizerFast 而非 AutoTokenizer。 For full usage and more information, please refer to URL 有關完整使用方法及其他資訊，請參見 URL 。
[ "# CKIP BERT Base Chinese\n\nThis project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).\n\n這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。", "## Homepage\n\n- URL", "## Contributers\n\n- Mu Yang at CKIP (Author & Maintainer)", "## Usage\n\nPlease use BertTokenizerFast as tokenizer instead of AutoTokenizer.\n\n請使用 BertTokenizerFast 而非 AutoTokenizer。\n\n\n\nFor full usage and more information, please refer to URL\n\n有關完整使用方法及其他資訊，請參見 URL 。" ]
[ "TAGS\n#transformers #pytorch #jax #bert #token-classification #zh #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n", "# CKIP BERT Base Chinese\n\nThis project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).\n\n這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。", "## Homepage\n\n- URL", "## Contributers\n\n- Mu Yang at CKIP (Author & Maintainer)", "## Usage\n\nPlease use BertTokenizerFast as tokenizer instead of AutoTokenizer.\n\n請使用 BertTokenizerFast 而非 AutoTokenizer。\n\n\n\nFor full usage and more information, please refer to URL\n\n有關完整使用方法及其他資訊，請參見 URL 。" ]
[ 54, 92, 4, 19, 63 ]
[ "passage: TAGS\n#transformers #pytorch #jax #bert #token-classification #zh #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n# CKIP BERT Base Chinese\n\nThis project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).\n\n這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。## Homepage\n\n- URL## Contributers\n\n- Mu Yang at CKIP (Author & Maintainer)## Usage\n\nPlease use BertTokenizerFast as tokenizer instead of AutoTokenizer.\n\n請使用 BertTokenizerFast 而非 AutoTokenizer。\n\n\n\nFor full usage and more information, please refer to URL\n\n有關完整使用方法及其他資訊，請參見 URL 。" ]
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null
null
transformers
# CKIP BERT Base Chinese This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition). 這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。 ## Homepage - https://github.com/ckiplab/ckip-transformers ## Contributers - [Mu Yang](https://muyang.pro) at [CKIP](https://ckip.iis.sinica.edu.tw) (Author & Maintainer) ## Usage Please use BertTokenizerFast as tokenizer instead of AutoTokenizer. 請使用 BertTokenizerFast 而非 AutoTokenizer。 ``` from transformers import ( BertTokenizerFast, AutoModel, ) tokenizer = BertTokenizerFast.from_pretrained('bert-base-chinese') model = AutoModel.from_pretrained('ckiplab/bert-base-chinese-pos') ``` For full usage and more information, please refer to https://github.com/ckiplab/ckip-transformers. 有關完整使用方法及其他資訊，請參見 https://github.com/ckiplab/ckip-transformers 。
{"language": ["zh"], "license": "gpl-3.0", "tags": ["pytorch", "token-classification", "bert", "zh"], "thumbnail": "https://ckip.iis.sinica.edu.tw/files/ckip_logo.png"}
token-classification
ckiplab/bert-base-chinese-pos
[ "transformers", "pytorch", "jax", "bert", "token-classification", "zh", "license:gpl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[ "zh" ]
TAGS #transformers #pytorch #jax #bert #token-classification #zh #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us
# CKIP BERT Base Chinese This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition). 這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。 ## Homepage - URL ## Contributers - Mu Yang at CKIP (Author & Maintainer) ## Usage Please use BertTokenizerFast as tokenizer instead of AutoTokenizer. 請使用 BertTokenizerFast 而非 AutoTokenizer。 For full usage and more information, please refer to URL 有關完整使用方法及其他資訊，請參見 URL 。
[ "# CKIP BERT Base Chinese\n\nThis project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).\n\n這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。", "## Homepage\n\n- URL", "## Contributers\n\n- Mu Yang at CKIP (Author & Maintainer)", "## Usage\n\nPlease use BertTokenizerFast as tokenizer instead of AutoTokenizer.\n\n請使用 BertTokenizerFast 而非 AutoTokenizer。\n\n\n\nFor full usage and more information, please refer to URL\n\n有關完整使用方法及其他資訊，請參見 URL 。" ]
[ "TAGS\n#transformers #pytorch #jax #bert #token-classification #zh #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# CKIP BERT Base Chinese\n\nThis project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).\n\n這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。", "## Homepage\n\n- URL", "## Contributers\n\n- Mu Yang at CKIP (Author & Maintainer)", "## Usage\n\nPlease use BertTokenizerFast as tokenizer instead of AutoTokenizer.\n\n請使用 BertTokenizerFast 而非 AutoTokenizer。\n\n\n\nFor full usage and more information, please refer to URL\n\n有關完整使用方法及其他資訊，請參見 URL 。" ]
[ 50, 92, 4, 19, 63 ]
[ "passage: TAGS\n#transformers #pytorch #jax #bert #token-classification #zh #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us \n# CKIP BERT Base Chinese\n\nThis project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).\n\n這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。## Homepage\n\n- URL## Contributers\n\n- Mu Yang at CKIP (Author & Maintainer)## Usage\n\nPlease use BertTokenizerFast as tokenizer instead of AutoTokenizer.\n\n請使用 BertTokenizerFast 而非 AutoTokenizer。\n\n\n\nFor full usage and more information, please refer to URL\n\n有關完整使用方法及其他資訊，請參見 URL 。" ]
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null
null
transformers
# CKIP BERT Base Chinese This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition). 這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。 ## Homepage - https://github.com/ckiplab/ckip-transformers ## Contributers - [Mu Yang](https://muyang.pro) at [CKIP](https://ckip.iis.sinica.edu.tw) (Author & Maintainer) ## Usage Please use BertTokenizerFast as tokenizer instead of AutoTokenizer. 請使用 BertTokenizerFast 而非 AutoTokenizer。 ``` from transformers import ( BertTokenizerFast, AutoModel, ) tokenizer = BertTokenizerFast.from_pretrained('bert-base-chinese') model = AutoModel.from_pretrained('ckiplab/bert-base-chinese-ws') ``` For full usage and more information, please refer to https://github.com/ckiplab/ckip-transformers. 有關完整使用方法及其他資訊，請參見 https://github.com/ckiplab/ckip-transformers 。
{"language": ["zh"], "license": "gpl-3.0", "tags": ["pytorch", "token-classification", "bert", "zh"], "thumbnail": "https://ckip.iis.sinica.edu.tw/files/ckip_logo.png"}
token-classification
ckiplab/bert-base-chinese-ws
[ "transformers", "pytorch", "jax", "bert", "token-classification", "zh", "license:gpl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[ "zh" ]
TAGS #transformers #pytorch #jax #bert #token-classification #zh #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us
# CKIP BERT Base Chinese This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition). 這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。 ## Homepage - URL ## Contributers - Mu Yang at CKIP (Author & Maintainer) ## Usage Please use BertTokenizerFast as tokenizer instead of AutoTokenizer. 請使用 BertTokenizerFast 而非 AutoTokenizer。 For full usage and more information, please refer to URL 有關完整使用方法及其他資訊，請參見 URL 。
[ "# CKIP BERT Base Chinese\n\nThis project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).\n\n這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。", "## Homepage\n\n- URL", "## Contributers\n\n- Mu Yang at CKIP (Author & Maintainer)", "## Usage\n\nPlease use BertTokenizerFast as tokenizer instead of AutoTokenizer.\n\n請使用 BertTokenizerFast 而非 AutoTokenizer。\n\n\n\nFor full usage and more information, please refer to URL\n\n有關完整使用方法及其他資訊，請參見 URL 。" ]
[ "TAGS\n#transformers #pytorch #jax #bert #token-classification #zh #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# CKIP BERT Base Chinese\n\nThis project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).\n\n這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。", "## Homepage\n\n- URL", "## Contributers\n\n- Mu Yang at CKIP (Author & Maintainer)", "## Usage\n\nPlease use BertTokenizerFast as tokenizer instead of AutoTokenizer.\n\n請使用 BertTokenizerFast 而非 AutoTokenizer。\n\n\n\nFor full usage and more information, please refer to URL\n\n有關完整使用方法及其他資訊，請參見 URL 。" ]
[ 50, 92, 4, 19, 63 ]
[ "passage: TAGS\n#transformers #pytorch #jax #bert #token-classification #zh #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us \n# CKIP BERT Base Chinese\n\nThis project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).\n\n這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。## Homepage\n\n- URL## Contributers\n\n- Mu Yang at CKIP (Author & Maintainer)## Usage\n\nPlease use BertTokenizerFast as tokenizer instead of AutoTokenizer.\n\n請使用 BertTokenizerFast 而非 AutoTokenizer。\n\n\n\nFor full usage and more information, please refer to URL\n\n有關完整使用方法及其他資訊，請參見 URL 。" ]
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null
null
transformers
# CKIP BERT Base Chinese This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition). 這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。 ## Homepage - https://github.com/ckiplab/ckip-transformers ## Contributers - [Mu Yang](https://muyang.pro) at [CKIP](https://ckip.iis.sinica.edu.tw) (Author & Maintainer) ## Usage Please use BertTokenizerFast as tokenizer instead of AutoTokenizer. 請使用 BertTokenizerFast 而非 AutoTokenizer。 ``` from transformers import ( BertTokenizerFast, AutoModel, ) tokenizer = BertTokenizerFast.from_pretrained('bert-base-chinese') model = AutoModel.from_pretrained('ckiplab/bert-base-chinese') ``` For full usage and more information, please refer to https://github.com/ckiplab/ckip-transformers. 有關完整使用方法及其他資訊，請參見 https://github.com/ckiplab/ckip-transformers 。
{"language": ["zh"], "license": "gpl-3.0", "tags": ["pytorch", "lm-head", "bert", "zh"], "thumbnail": "https://ckip.iis.sinica.edu.tw/files/ckip_logo.png"}
fill-mask
ckiplab/bert-base-chinese
[ "transformers", "pytorch", "jax", "bert", "fill-mask", "lm-head", "zh", "license:gpl-3.0", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[ "zh" ]
TAGS #transformers #pytorch #jax #bert #fill-mask #lm-head #zh #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
# CKIP BERT Base Chinese This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition). 這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。 ## Homepage - URL ## Contributers - Mu Yang at CKIP (Author & Maintainer) ## Usage Please use BertTokenizerFast as tokenizer instead of AutoTokenizer. 請使用 BertTokenizerFast 而非 AutoTokenizer。 For full usage and more information, please refer to URL 有關完整使用方法及其他資訊，請參見 URL 。
[ "# CKIP BERT Base Chinese\n\nThis project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).\n\n這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。", "## Homepage\n\n- URL", "## Contributers\n\n- Mu Yang at CKIP (Author & Maintainer)", "## Usage\n\nPlease use BertTokenizerFast as tokenizer instead of AutoTokenizer.\n\n請使用 BertTokenizerFast 而非 AutoTokenizer。\n\n\n\nFor full usage and more information, please refer to URL\n\n有關完整使用方法及其他資訊，請參見 URL 。" ]
[ "TAGS\n#transformers #pytorch #jax #bert #fill-mask #lm-head #zh #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n", "# CKIP BERT Base Chinese\n\nThis project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).\n\n這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。", "## Homepage\n\n- URL", "## Contributers\n\n- Mu Yang at CKIP (Author & Maintainer)", "## Usage\n\nPlease use BertTokenizerFast as tokenizer instead of AutoTokenizer.\n\n請使用 BertTokenizerFast 而非 AutoTokenizer。\n\n\n\nFor full usage and more information, please refer to URL\n\n有關完整使用方法及其他資訊，請參見 URL 。" ]
[ 57, 92, 4, 19, 63 ]
[ "passage: TAGS\n#transformers #pytorch #jax #bert #fill-mask #lm-head #zh #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n# CKIP BERT Base Chinese\n\nThis project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).\n\n這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。## Homepage\n\n- URL## Contributers\n\n- Mu Yang at CKIP (Author & Maintainer)## Usage\n\nPlease use BertTokenizerFast as tokenizer instead of AutoTokenizer.\n\n請使用 BertTokenizerFast 而非 AutoTokenizer。\n\n\n\nFor full usage and more information, please refer to URL\n\n有關完整使用方法及其他資訊，請參見 URL 。" ]
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null
null
transformers
# CKIP GPT2 Base Chinese This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition). 這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。 ## Homepage - https://github.com/ckiplab/ckip-transformers ## Contributers - [Mu Yang](https://muyang.pro) at [CKIP](https://ckip.iis.sinica.edu.tw) (Author & Maintainer) ## Usage Please use BertTokenizerFast as tokenizer instead of AutoTokenizer. 請使用 BertTokenizerFast 而非 AutoTokenizer。 ``` from transformers import ( BertTokenizerFast, AutoModel, ) tokenizer = BertTokenizerFast.from_pretrained('bert-base-chinese') model = AutoModel.from_pretrained('ckiplab/gpt2-base-chinese') ``` For full usage and more information, please refer to https://github.com/ckiplab/ckip-transformers. 有關完整使用方法及其他資訊，請參見 https://github.com/ckiplab/ckip-transformers 。
{"language": ["zh"], "license": "gpl-3.0", "tags": ["pytorch", "lm-head", "gpt2", "zh"], "thumbnail": "https://ckip.iis.sinica.edu.tw/files/ckip_logo.png"}
text-generation
ckiplab/gpt2-base-chinese
[ "transformers", "pytorch", "jax", "gpt2", "text-generation", "lm-head", "zh", "license:gpl-3.0", "autotrain_compatible", "endpoints_compatible", "has_space", "text-generation-inference", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[ "zh" ]
TAGS #transformers #pytorch #jax #gpt2 #text-generation #lm-head #zh #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
# CKIP GPT2 Base Chinese This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition). 這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。 ## Homepage - URL ## Contributers - Mu Yang at CKIP (Author & Maintainer) ## Usage Please use BertTokenizerFast as tokenizer instead of AutoTokenizer. 請使用 BertTokenizerFast 而非 AutoTokenizer。 For full usage and more information, please refer to URL 有關完整使用方法及其他資訊，請參見 URL 。
[ "# CKIP GPT2 Base Chinese\n\nThis project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).\n\n這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。", "## Homepage\n\n- URL", "## Contributers\n\n- Mu Yang at CKIP (Author & Maintainer)", "## Usage\n\nPlease use BertTokenizerFast as tokenizer instead of AutoTokenizer.\n\n請使用 BertTokenizerFast 而非 AutoTokenizer。\n\n\n\nFor full usage and more information, please refer to URL\n\n有關完整使用方法及其他資訊，請參見 URL 。" ]
[ "TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #lm-head #zh #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n", "# CKIP GPT2 Base Chinese\n\nThis project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).\n\n這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。", "## Homepage\n\n- URL", "## Contributers\n\n- Mu Yang at CKIP (Author & Maintainer)", "## Usage\n\nPlease use BertTokenizerFast as tokenizer instead of AutoTokenizer.\n\n請使用 BertTokenizerFast 而非 AutoTokenizer。\n\n\n\nFor full usage and more information, please refer to URL\n\n有關完整使用方法及其他資訊，請參見 URL 。" ]
[ 68, 93, 4, 19, 63 ]
[ "passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #lm-head #zh #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n# CKIP GPT2 Base Chinese\n\nThis project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).\n\n這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。## Homepage\n\n- URL## Contributers\n\n- Mu Yang at CKIP (Author & Maintainer)## Usage\n\nPlease use BertTokenizerFast as tokenizer instead of AutoTokenizer.\n\n請使用 BertTokenizerFast 而非 AutoTokenizer。\n\n\n\nFor full usage and more information, please refer to URL\n\n有關完整使用方法及其他資訊，請參見 URL 。" ]
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null
null
transformers
# BERT base Japanese (character-level tokenization with whole word masking, jawiki-20200831) This is a [BERT](https://github.com/google-research/bert) model pretrained on texts in the Japanese language. This version of the model processes input texts with word-level tokenization based on the Unidic 2.1.2 dictionary (available in [unidic-lite](https://pypi.org/project/unidic-lite/) package), followed by character-level tokenization. Additionally, the model is trained with the whole word masking enabled for the masked language modeling (MLM) objective. The codes for the pretraining are available at [cl-tohoku/bert-japanese](https://github.com/cl-tohoku/bert-japanese/tree/v2.0). ## Model architecture The model architecture is the same as the original BERT base model; 12 layers, 768 dimensions of hidden states, and 12 attention heads. ## Training Data The models are trained on the Japanese version of Wikipedia. The training corpus is generated from the Wikipedia Cirrussearch dump file as of August 31, 2020. The generated corpus files are 4.0GB in total, containing approximately 30M sentences. We used the [MeCab](https://taku910.github.io/mecab/) morphological parser with [mecab-ipadic-NEologd](https://github.com/neologd/mecab-ipadic-neologd) dictionary to split texts into sentences. ## Tokenization The texts are first tokenized by MeCab with the Unidic 2.1.2 dictionary and then split into characters. The vocabulary size is 6144. We used [`fugashi`](https://github.com/polm/fugashi) and [`unidic-lite`](https://github.com/polm/unidic-lite) packages for the tokenization. ## Training The models are trained with the same configuration as the original BERT; 512 tokens per instance, 256 instances per batch, and 1M training steps. For training of the MLM (masked language modeling) objective, we introduced whole word masking in which all of the subword tokens corresponding to a single word (tokenized by MeCab) are masked at once. For training of each model, we used a v3-8 instance of Cloud TPUs provided by [TensorFlow Research Cloud program](https://www.tensorflow.org/tfrc/). The training took about 5 days to finish. ## Licenses The pretrained models are distributed under the terms of the [Creative Commons Attribution-ShareAlike 3.0](https://creativecommons.org/licenses/by-sa/3.0/). ## Acknowledgments This model is trained with Cloud TPUs provided by [TensorFlow Research Cloud](https://www.tensorflow.org/tfrc/) program.
{"language": "ja", "license": "cc-by-sa-4.0", "datasets": ["wikipedia"], "widget": [{"text": "\u6771\u5317\u5927\u5b66\u3067[MASK]\u306e\u7814\u7a76\u3092\u3057\u3066\u3044\u307e\u3059\u3002"}]}
fill-mask
tohoku-nlp/bert-base-japanese-char-v2
[ "transformers", "pytorch", "tf", "jax", "bert", "fill-mask", "ja", "dataset:wikipedia", "license:cc-by-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[ "ja" ]
TAGS #transformers #pytorch #tf #jax #bert #fill-mask #ja #dataset-wikipedia #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
# BERT base Japanese (character-level tokenization with whole word masking, jawiki-20200831) This is a BERT model pretrained on texts in the Japanese language. This version of the model processes input texts with word-level tokenization based on the Unidic 2.1.2 dictionary (available in unidic-lite package), followed by character-level tokenization. Additionally, the model is trained with the whole word masking enabled for the masked language modeling (MLM) objective. The codes for the pretraining are available at cl-tohoku/bert-japanese. ## Model architecture The model architecture is the same as the original BERT base model; 12 layers, 768 dimensions of hidden states, and 12 attention heads. ## Training Data The models are trained on the Japanese version of Wikipedia. The training corpus is generated from the Wikipedia Cirrussearch dump file as of August 31, 2020. The generated corpus files are 4.0GB in total, containing approximately 30M sentences. We used the MeCab morphological parser with mecab-ipadic-NEologd dictionary to split texts into sentences. ## Tokenization The texts are first tokenized by MeCab with the Unidic 2.1.2 dictionary and then split into characters. The vocabulary size is 6144. We used 'fugashi' and 'unidic-lite' packages for the tokenization. ## Training The models are trained with the same configuration as the original BERT; 512 tokens per instance, 256 instances per batch, and 1M training steps. For training of the MLM (masked language modeling) objective, we introduced whole word masking in which all of the subword tokens corresponding to a single word (tokenized by MeCab) are masked at once. For training of each model, we used a v3-8 instance of Cloud TPUs provided by TensorFlow Research Cloud program. The training took about 5 days to finish. ## Licenses The pretrained models are distributed under the terms of the Creative Commons Attribution-ShareAlike 3.0. ## Acknowledgments This model is trained with Cloud TPUs provided by TensorFlow Research Cloud program.
[ "# BERT base Japanese (character-level tokenization with whole word masking, jawiki-20200831)\n\nThis is a BERT model pretrained on texts in the Japanese language.\n\nThis version of the model processes input texts with word-level tokenization based on the Unidic 2.1.2 dictionary (available in unidic-lite package), followed by character-level tokenization.\nAdditionally, the model is trained with the whole word masking enabled for the masked language modeling (MLM) objective.\n\nThe codes for the pretraining are available at cl-tohoku/bert-japanese.", "## Model architecture\n\nThe model architecture is the same as the original BERT base model; 12 layers, 768 dimensions of hidden states, and 12 attention heads.", "## Training Data\n\nThe models are trained on the Japanese version of Wikipedia.\nThe training corpus is generated from the Wikipedia Cirrussearch dump file as of August 31, 2020.\n\nThe generated corpus files are 4.0GB in total, containing approximately 30M sentences.\nWe used the MeCab morphological parser with mecab-ipadic-NEologd dictionary to split texts into sentences.", "## Tokenization\n\nThe texts are first tokenized by MeCab with the Unidic 2.1.2 dictionary and then split into characters.\nThe vocabulary size is 6144.\n\nWe used 'fugashi' and 'unidic-lite' packages for the tokenization.", "## Training\n\nThe models are trained with the same configuration as the original BERT; 512 tokens per instance, 256 instances per batch, and 1M training steps.\nFor training of the MLM (masked language modeling) objective, we introduced whole word masking in which all of the subword tokens corresponding to a single word (tokenized by MeCab) are masked at once.\n\nFor training of each model, we used a v3-8 instance of Cloud TPUs provided by TensorFlow Research Cloud program.\nThe training took about 5 days to finish.", "## Licenses\n\nThe pretrained models are distributed under the terms of the Creative Commons Attribution-ShareAlike 3.0.", "## Acknowledgments\n\nThis model is trained with Cloud TPUs provided by TensorFlow Research Cloud program." ]
[ "TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #ja #dataset-wikipedia #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# BERT base Japanese (character-level tokenization with whole word masking, jawiki-20200831)\n\nThis is a BERT model pretrained on texts in the Japanese language.\n\nThis version of the model processes input texts with word-level tokenization based on the Unidic 2.1.2 dictionary (available in unidic-lite package), followed by character-level tokenization.\nAdditionally, the model is trained with the whole word masking enabled for the masked language modeling (MLM) objective.\n\nThe codes for the pretraining are available at cl-tohoku/bert-japanese.", "## Model architecture\n\nThe model architecture is the same as the original BERT base model; 12 layers, 768 dimensions of hidden states, and 12 attention heads.", "## Training Data\n\nThe models are trained on the Japanese version of Wikipedia.\nThe training corpus is generated from the Wikipedia Cirrussearch dump file as of August 31, 2020.\n\nThe generated corpus files are 4.0GB in total, containing approximately 30M sentences.\nWe used the MeCab morphological parser with mecab-ipadic-NEologd dictionary to split texts into sentences.", "## Tokenization\n\nThe texts are first tokenized by MeCab with the Unidic 2.1.2 dictionary and then split into characters.\nThe vocabulary size is 6144.\n\nWe used 'fugashi' and 'unidic-lite' packages for the tokenization.", "## Training\n\nThe models are trained with the same configuration as the original BERT; 512 tokens per instance, 256 instances per batch, and 1M training steps.\nFor training of the MLM (masked language modeling) objective, we introduced whole word masking in which all of the subword tokens corresponding to a single word (tokenized by MeCab) are masked at once.\n\nFor training of each model, we used a v3-8 instance of Cloud TPUs provided by TensorFlow Research Cloud program.\nThe training took about 5 days to finish.", "## Licenses\n\nThe pretrained models are distributed under the terms of the Creative Commons Attribution-ShareAlike 3.0.", "## Acknowledgments\n\nThis model is trained with Cloud TPUs provided by TensorFlow Research Cloud program." ]
[ 60, 141, 36, 86, 60, 124, 23, 26 ]
[ "passage: TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #ja #dataset-wikipedia #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n# BERT base Japanese (character-level tokenization with whole word masking, jawiki-20200831)\n\nThis is a BERT model pretrained on texts in the Japanese language.\n\nThis version of the model processes input texts with word-level tokenization based on the Unidic 2.1.2 dictionary (available in unidic-lite package), followed by character-level tokenization.\nAdditionally, the model is trained with the whole word masking enabled for the masked language modeling (MLM) objective.\n\nThe codes for the pretraining are available at cl-tohoku/bert-japanese.## Model architecture\n\nThe model architecture is the same as the original BERT base model; 12 layers, 768 dimensions of hidden states, and 12 attention heads.## Training Data\n\nThe models are trained on the Japanese version of Wikipedia.\nThe training corpus is generated from the Wikipedia Cirrussearch dump file as of August 31, 2020.\n\nThe generated corpus files are 4.0GB in total, containing approximately 30M sentences.\nWe used the MeCab morphological parser with mecab-ipadic-NEologd dictionary to split texts into sentences.## Tokenization\n\nThe texts are first tokenized by MeCab with the Unidic 2.1.2 dictionary and then split into characters.\nThe vocabulary size is 6144.\n\nWe used 'fugashi' and 'unidic-lite' packages for the tokenization.## Training\n\nThe models are trained with the same configuration as the original BERT; 512 tokens per instance, 256 instances per batch, and 1M training steps.\nFor training of the MLM (masked language modeling) objective, we introduced whole word masking in which all of the subword tokens corresponding to a single word (tokenized by MeCab) are masked at once.\n\nFor training of each model, we used a v3-8 instance of Cloud TPUs provided by TensorFlow Research Cloud program.\nThe training took about 5 days to finish." ]
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null
null
transformers
# BERT base Japanese (character tokenization, whole word masking enabled) This is a [BERT](https://github.com/google-research/bert) model pretrained on texts in the Japanese language. This version of the model processes input texts with word-level tokenization based on the IPA dictionary, followed by character-level tokenization. Additionally, the model is trained with the whole word masking enabled for the masked language modeling (MLM) objective. The codes for the pretraining are available at [cl-tohoku/bert-japanese](https://github.com/cl-tohoku/bert-japanese/tree/v1.0). ## Model architecture The model architecture is the same as the original BERT base model; 12 layers, 768 dimensions of hidden states, and 12 attention heads. ## Training Data The model is trained on Japanese Wikipedia as of September 1, 2019. To generate the training corpus, [WikiExtractor](https://github.com/attardi/wikiextractor) is used to extract plain texts from a dump file of Wikipedia articles. The text files used for the training are 2.6GB in size, consisting of approximately 17M sentences. ## Tokenization The texts are first tokenized by [MeCab](https://taku910.github.io/mecab/) morphological parser with the IPA dictionary and then split into characters. The vocabulary size is 4000. ## Training The model is trained with the same configuration as the original BERT; 512 tokens per instance, 256 instances per batch, and 1M training steps. For the training of the MLM (masked language modeling) objective, we introduced the **Whole Word Masking** in which all of the subword tokens corresponding to a single word (tokenized by MeCab) are masked at once. ## Licenses The pretrained models are distributed under the terms of the [Creative Commons Attribution-ShareAlike 3.0](https://creativecommons.org/licenses/by-sa/3.0/). ## Acknowledgments For training models, we used Cloud TPUs provided by [TensorFlow Research Cloud](https://www.tensorflow.org/tfrc/) program.
{"language": "ja", "license": "cc-by-sa-4.0", "datasets": ["wikipedia"], "widget": [{"text": "\u4ed9\u53f0\u306f\u300c[MASK]\u306e\u90fd\u300d\u3068\u547c\u3070\u308c\u3066\u3044\u308b\u3002"}]}
fill-mask
tohoku-nlp/bert-base-japanese-char-whole-word-masking
[ "transformers", "pytorch", "tf", "jax", "bert", "fill-mask", "ja", "dataset:wikipedia", "license:cc-by-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[ "ja" ]
TAGS #transformers #pytorch #tf #jax #bert #fill-mask #ja #dataset-wikipedia #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
# BERT base Japanese (character tokenization, whole word masking enabled) This is a BERT model pretrained on texts in the Japanese language. This version of the model processes input texts with word-level tokenization based on the IPA dictionary, followed by character-level tokenization. Additionally, the model is trained with the whole word masking enabled for the masked language modeling (MLM) objective. The codes for the pretraining are available at cl-tohoku/bert-japanese. ## Model architecture The model architecture is the same as the original BERT base model; 12 layers, 768 dimensions of hidden states, and 12 attention heads. ## Training Data The model is trained on Japanese Wikipedia as of September 1, 2019. To generate the training corpus, WikiExtractor is used to extract plain texts from a dump file of Wikipedia articles. The text files used for the training are 2.6GB in size, consisting of approximately 17M sentences. ## Tokenization The texts are first tokenized by MeCab morphological parser with the IPA dictionary and then split into characters. The vocabulary size is 4000. ## Training The model is trained with the same configuration as the original BERT; 512 tokens per instance, 256 instances per batch, and 1M training steps. For the training of the MLM (masked language modeling) objective, we introduced the Whole Word Masking in which all of the subword tokens corresponding to a single word (tokenized by MeCab) are masked at once. ## Licenses The pretrained models are distributed under the terms of the Creative Commons Attribution-ShareAlike 3.0. ## Acknowledgments For training models, we used Cloud TPUs provided by TensorFlow Research Cloud program.
[ "# BERT base Japanese (character tokenization, whole word masking enabled)\n\nThis is a BERT model pretrained on texts in the Japanese language.\n\nThis version of the model processes input texts with word-level tokenization based on the IPA dictionary, followed by character-level tokenization.\nAdditionally, the model is trained with the whole word masking enabled for the masked language modeling (MLM) objective.\n\nThe codes for the pretraining are available at cl-tohoku/bert-japanese.", "## Model architecture\n\nThe model architecture is the same as the original BERT base model; 12 layers, 768 dimensions of hidden states, and 12 attention heads.", "## Training Data\n\nThe model is trained on Japanese Wikipedia as of September 1, 2019.\nTo generate the training corpus, WikiExtractor is used to extract plain texts from a dump file of Wikipedia articles.\nThe text files used for the training are 2.6GB in size, consisting of approximately 17M sentences.", "## Tokenization\n\nThe texts are first tokenized by MeCab morphological parser with the IPA dictionary and then split into characters.\nThe vocabulary size is 4000.", "## Training\n\nThe model is trained with the same configuration as the original BERT; 512 tokens per instance, 256 instances per batch, and 1M training steps.\n\nFor the training of the MLM (masked language modeling) objective, we introduced the Whole Word Masking in which all of the subword tokens corresponding to a single word (tokenized by MeCab) are masked at once.", "## Licenses\n\nThe pretrained models are distributed under the terms of the Creative Commons Attribution-ShareAlike 3.0.", "## Acknowledgments\n\nFor training models, we used Cloud TPUs provided by TensorFlow Research Cloud program." ]
[ "TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #ja #dataset-wikipedia #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# BERT base Japanese (character tokenization, whole word masking enabled)\n\nThis is a BERT model pretrained on texts in the Japanese language.\n\nThis version of the model processes input texts with word-level tokenization based on the IPA dictionary, followed by character-level tokenization.\nAdditionally, the model is trained with the whole word masking enabled for the masked language modeling (MLM) objective.\n\nThe codes for the pretraining are available at cl-tohoku/bert-japanese.", "## Model architecture\n\nThe model architecture is the same as the original BERT base model; 12 layers, 768 dimensions of hidden states, and 12 attention heads.", "## Training Data\n\nThe model is trained on Japanese Wikipedia as of September 1, 2019.\nTo generate the training corpus, WikiExtractor is used to extract plain texts from a dump file of Wikipedia articles.\nThe text files used for the training are 2.6GB in size, consisting of approximately 17M sentences.", "## Tokenization\n\nThe texts are first tokenized by MeCab morphological parser with the IPA dictionary and then split into characters.\nThe vocabulary size is 4000.", "## Training\n\nThe model is trained with the same configuration as the original BERT; 512 tokens per instance, 256 instances per batch, and 1M training steps.\n\nFor the training of the MLM (masked language modeling) objective, we introduced the Whole Word Masking in which all of the subword tokens corresponding to a single word (tokenized by MeCab) are masked at once.", "## Licenses\n\nThe pretrained models are distributed under the terms of the Creative Commons Attribution-ShareAlike 3.0.", "## Acknowledgments\n\nFor training models, we used Cloud TPUs provided by TensorFlow Research Cloud program." ]
[ 60, 123, 36, 65, 41, 91, 23, 26 ]
[ "passage: TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #ja #dataset-wikipedia #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n# BERT base Japanese (character tokenization, whole word masking enabled)\n\nThis is a BERT model pretrained on texts in the Japanese language.\n\nThis version of the model processes input texts with word-level tokenization based on the IPA dictionary, followed by character-level tokenization.\nAdditionally, the model is trained with the whole word masking enabled for the masked language modeling (MLM) objective.\n\nThe codes for the pretraining are available at cl-tohoku/bert-japanese.## Model architecture\n\nThe model architecture is the same as the original BERT base model; 12 layers, 768 dimensions of hidden states, and 12 attention heads.## Training Data\n\nThe model is trained on Japanese Wikipedia as of September 1, 2019.\nTo generate the training corpus, WikiExtractor is used to extract plain texts from a dump file of Wikipedia articles.\nThe text files used for the training are 2.6GB in size, consisting of approximately 17M sentences.## Tokenization\n\nThe texts are first tokenized by MeCab morphological parser with the IPA dictionary and then split into characters.\nThe vocabulary size is 4000.## Training\n\nThe model is trained with the same configuration as the original BERT; 512 tokens per instance, 256 instances per batch, and 1M training steps.\n\nFor the training of the MLM (masked language modeling) objective, we introduced the Whole Word Masking in which all of the subword tokens corresponding to a single word (tokenized by MeCab) are masked at once.## Licenses\n\nThe pretrained models are distributed under the terms of the Creative Commons Attribution-ShareAlike 3.0.## Acknowledgments\n\nFor training models, we used Cloud TPUs provided by TensorFlow Research Cloud program." ]
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null
null
transformers
# BERT base Japanese (character tokenization) This is a [BERT](https://github.com/google-research/bert) model pretrained on texts in the Japanese language. This version of the model processes input texts with word-level tokenization based on the IPA dictionary, followed by character-level tokenization. The codes for the pretraining are available at [cl-tohoku/bert-japanese](https://github.com/cl-tohoku/bert-japanese/tree/v1.0). ## Model architecture The model architecture is the same as the original BERT base model; 12 layers, 768 dimensions of hidden states, and 12 attention heads. ## Training Data The model is trained on Japanese Wikipedia as of September 1, 2019. To generate the training corpus, [WikiExtractor](https://github.com/attardi/wikiextractor) is used to extract plain texts from a dump file of Wikipedia articles. The text files used for the training are 2.6GB in size, consisting of approximately 17M sentences. ## Tokenization The texts are first tokenized by [MeCab](https://taku910.github.io/mecab/) morphological parser with the IPA dictionary and then split into characters. The vocabulary size is 4000. ## Training The model is trained with the same configuration as the original BERT; 512 tokens per instance, 256 instances per batch, and 1M training steps. ## Licenses The pretrained models are distributed under the terms of the [Creative Commons Attribution-ShareAlike 3.0](https://creativecommons.org/licenses/by-sa/3.0/). ## Acknowledgments For training models, we used Cloud TPUs provided by [TensorFlow Research Cloud](https://www.tensorflow.org/tfrc/) program.
{"language": "ja", "license": "cc-by-sa-4.0", "datasets": ["wikipedia"], "widget": [{"text": "\u4ed9\u53f0\u306f\u300c[MASK]\u306e\u90fd\u300d\u3068\u547c\u3070\u308c\u3066\u3044\u308b\u3002"}]}
fill-mask
tohoku-nlp/bert-base-japanese-char
[ "transformers", "pytorch", "tf", "jax", "bert", "fill-mask", "ja", "dataset:wikipedia", "license:cc-by-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[ "ja" ]
TAGS #transformers #pytorch #tf #jax #bert #fill-mask #ja #dataset-wikipedia #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
# BERT base Japanese (character tokenization) This is a BERT model pretrained on texts in the Japanese language. This version of the model processes input texts with word-level tokenization based on the IPA dictionary, followed by character-level tokenization. The codes for the pretraining are available at cl-tohoku/bert-japanese. ## Model architecture The model architecture is the same as the original BERT base model; 12 layers, 768 dimensions of hidden states, and 12 attention heads. ## Training Data The model is trained on Japanese Wikipedia as of September 1, 2019. To generate the training corpus, WikiExtractor is used to extract plain texts from a dump file of Wikipedia articles. The text files used for the training are 2.6GB in size, consisting of approximately 17M sentences. ## Tokenization The texts are first tokenized by MeCab morphological parser with the IPA dictionary and then split into characters. The vocabulary size is 4000. ## Training The model is trained with the same configuration as the original BERT; 512 tokens per instance, 256 instances per batch, and 1M training steps. ## Licenses The pretrained models are distributed under the terms of the Creative Commons Attribution-ShareAlike 3.0. ## Acknowledgments For training models, we used Cloud TPUs provided by TensorFlow Research Cloud program.
[ "# BERT base Japanese (character tokenization)\n\nThis is a BERT model pretrained on texts in the Japanese language.\n\nThis version of the model processes input texts with word-level tokenization based on the IPA dictionary, followed by character-level tokenization.\n\nThe codes for the pretraining are available at cl-tohoku/bert-japanese.", "## Model architecture\n\nThe model architecture is the same as the original BERT base model; 12 layers, 768 dimensions of hidden states, and 12 attention heads.", "## Training Data\n\nThe model is trained on Japanese Wikipedia as of September 1, 2019.\nTo generate the training corpus, WikiExtractor is used to extract plain texts from a dump file of Wikipedia articles.\nThe text files used for the training are 2.6GB in size, consisting of approximately 17M sentences.", "## Tokenization\n\nThe texts are first tokenized by MeCab morphological parser with the IPA dictionary and then split into characters.\nThe vocabulary size is 4000.", "## Training\n\nThe model is trained with the same configuration as the original BERT; 512 tokens per instance, 256 instances per batch, and 1M training steps.", "## Licenses\n\nThe pretrained models are distributed under the terms of the Creative Commons Attribution-ShareAlike 3.0.", "## Acknowledgments\n\nFor training models, we used Cloud TPUs provided by TensorFlow Research Cloud program." ]
[ "TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #ja #dataset-wikipedia #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# BERT base Japanese (character tokenization)\n\nThis is a BERT model pretrained on texts in the Japanese language.\n\nThis version of the model processes input texts with word-level tokenization based on the IPA dictionary, followed by character-level tokenization.\n\nThe codes for the pretraining are available at cl-tohoku/bert-japanese.", "## Model architecture\n\nThe model architecture is the same as the original BERT base model; 12 layers, 768 dimensions of hidden states, and 12 attention heads.", "## Training Data\n\nThe model is trained on Japanese Wikipedia as of September 1, 2019.\nTo generate the training corpus, WikiExtractor is used to extract plain texts from a dump file of Wikipedia articles.\nThe text files used for the training are 2.6GB in size, consisting of approximately 17M sentences.", "## Tokenization\n\nThe texts are first tokenized by MeCab morphological parser with the IPA dictionary and then split into characters.\nThe vocabulary size is 4000.", "## Training\n\nThe model is trained with the same configuration as the original BERT; 512 tokens per instance, 256 instances per batch, and 1M training steps.", "## Licenses\n\nThe pretrained models are distributed under the terms of the Creative Commons Attribution-ShareAlike 3.0.", "## Acknowledgments\n\nFor training models, we used Cloud TPUs provided by TensorFlow Research Cloud program." ]
[ 60, 87, 36, 65, 41, 36, 23, 26 ]
[ "passage: TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #ja #dataset-wikipedia #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n# BERT base Japanese (character tokenization)\n\nThis is a BERT model pretrained on texts in the Japanese language.\n\nThis version of the model processes input texts with word-level tokenization based on the IPA dictionary, followed by character-level tokenization.\n\nThe codes for the pretraining are available at cl-tohoku/bert-japanese.## Model architecture\n\nThe model architecture is the same as the original BERT base model; 12 layers, 768 dimensions of hidden states, and 12 attention heads.## Training Data\n\nThe model is trained on Japanese Wikipedia as of September 1, 2019.\nTo generate the training corpus, WikiExtractor is used to extract plain texts from a dump file of Wikipedia articles.\nThe text files used for the training are 2.6GB in size, consisting of approximately 17M sentences.## Tokenization\n\nThe texts are first tokenized by MeCab morphological parser with the IPA dictionary and then split into characters.\nThe vocabulary size is 4000.## Training\n\nThe model is trained with the same configuration as the original BERT; 512 tokens per instance, 256 instances per batch, and 1M training steps.## Licenses\n\nThe pretrained models are distributed under the terms of the Creative Commons Attribution-ShareAlike 3.0.## Acknowledgments\n\nFor training models, we used Cloud TPUs provided by TensorFlow Research Cloud program." ]
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null
null
transformers
# BERT base Japanese (unidic-lite with whole word masking, jawiki-20200831) This is a [BERT](https://github.com/google-research/bert) model pretrained on texts in the Japanese language. This version of the model processes input texts with word-level tokenization based on the Unidic 2.1.2 dictionary (available in [unidic-lite](https://pypi.org/project/unidic-lite/) package), followed by the WordPiece subword tokenization. Additionally, the model is trained with the whole word masking enabled for the masked language modeling (MLM) objective. The codes for the pretraining are available at [cl-tohoku/bert-japanese](https://github.com/cl-tohoku/bert-japanese/tree/v2.0). ## Model architecture The model architecture is the same as the original BERT base model; 12 layers, 768 dimensions of hidden states, and 12 attention heads. ## Training Data The models are trained on the Japanese version of Wikipedia. The training corpus is generated from the Wikipedia Cirrussearch dump file as of August 31, 2020. The generated corpus files are 4.0GB in total, containing approximately 30M sentences. We used the [MeCab](https://taku910.github.io/mecab/) morphological parser with [mecab-ipadic-NEologd](https://github.com/neologd/mecab-ipadic-neologd) dictionary to split texts into sentences. ## Tokenization The texts are first tokenized by MeCab with the Unidic 2.1.2 dictionary and then split into subwords by the WordPiece algorithm. The vocabulary size is 32768. We used [`fugashi`](https://github.com/polm/fugashi) and [`unidic-lite`](https://github.com/polm/unidic-lite) packages for the tokenization. ## Training The models are trained with the same configuration as the original BERT; 512 tokens per instance, 256 instances per batch, and 1M training steps. For training of the MLM (masked language modeling) objective, we introduced whole word masking in which all of the subword tokens corresponding to a single word (tokenized by MeCab) are masked at once. For training of each model, we used a v3-8 instance of Cloud TPUs provided by [TensorFlow Research Cloud program](https://www.tensorflow.org/tfrc/). The training took about 5 days to finish. ## Licenses The pretrained models are distributed under the terms of the [Creative Commons Attribution-ShareAlike 3.0](https://creativecommons.org/licenses/by-sa/3.0/). ## Acknowledgments This model is trained with Cloud TPUs provided by [TensorFlow Research Cloud](https://www.tensorflow.org/tfrc/) program.
{"language": "ja", "license": "cc-by-sa-4.0", "datasets": ["wikipedia"], "widget": [{"text": "\u6771\u5317\u5927\u5b66\u3067[MASK]\u306e\u7814\u7a76\u3092\u3057\u3066\u3044\u307e\u3059\u3002"}]}
fill-mask
tohoku-nlp/bert-base-japanese-v2
[ "transformers", "pytorch", "tf", "jax", "bert", "fill-mask", "ja", "dataset:wikipedia", "license:cc-by-sa-4.0", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[ "ja" ]
TAGS #transformers #pytorch #tf #jax #bert #fill-mask #ja #dataset-wikipedia #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
# BERT base Japanese (unidic-lite with whole word masking, jawiki-20200831) This is a BERT model pretrained on texts in the Japanese language. This version of the model processes input texts with word-level tokenization based on the Unidic 2.1.2 dictionary (available in unidic-lite package), followed by the WordPiece subword tokenization. Additionally, the model is trained with the whole word masking enabled for the masked language modeling (MLM) objective. The codes for the pretraining are available at cl-tohoku/bert-japanese. ## Model architecture The model architecture is the same as the original BERT base model; 12 layers, 768 dimensions of hidden states, and 12 attention heads. ## Training Data The models are trained on the Japanese version of Wikipedia. The training corpus is generated from the Wikipedia Cirrussearch dump file as of August 31, 2020. The generated corpus files are 4.0GB in total, containing approximately 30M sentences. We used the MeCab morphological parser with mecab-ipadic-NEologd dictionary to split texts into sentences. ## Tokenization The texts are first tokenized by MeCab with the Unidic 2.1.2 dictionary and then split into subwords by the WordPiece algorithm. The vocabulary size is 32768. We used 'fugashi' and 'unidic-lite' packages for the tokenization. ## Training The models are trained with the same configuration as the original BERT; 512 tokens per instance, 256 instances per batch, and 1M training steps. For training of the MLM (masked language modeling) objective, we introduced whole word masking in which all of the subword tokens corresponding to a single word (tokenized by MeCab) are masked at once. For training of each model, we used a v3-8 instance of Cloud TPUs provided by TensorFlow Research Cloud program. The training took about 5 days to finish. ## Licenses The pretrained models are distributed under the terms of the Creative Commons Attribution-ShareAlike 3.0. ## Acknowledgments This model is trained with Cloud TPUs provided by TensorFlow Research Cloud program.
[ "# BERT base Japanese (unidic-lite with whole word masking, jawiki-20200831)\n\nThis is a BERT model pretrained on texts in the Japanese language.\n\nThis version of the model processes input texts with word-level tokenization based on the Unidic 2.1.2 dictionary (available in unidic-lite package), followed by the WordPiece subword tokenization.\nAdditionally, the model is trained with the whole word masking enabled for the masked language modeling (MLM) objective.\n\nThe codes for the pretraining are available at cl-tohoku/bert-japanese.", "## Model architecture\n\nThe model architecture is the same as the original BERT base model; 12 layers, 768 dimensions of hidden states, and 12 attention heads.", "## Training Data\n\nThe models are trained on the Japanese version of Wikipedia.\nThe training corpus is generated from the Wikipedia Cirrussearch dump file as of August 31, 2020.\n\nThe generated corpus files are 4.0GB in total, containing approximately 30M sentences.\nWe used the MeCab morphological parser with mecab-ipadic-NEologd dictionary to split texts into sentences.", "## Tokenization\n\nThe texts are first tokenized by MeCab with the Unidic 2.1.2 dictionary and then split into subwords by the WordPiece algorithm.\nThe vocabulary size is 32768.\n\nWe used 'fugashi' and 'unidic-lite' packages for the tokenization.", "## Training\n\nThe models are trained with the same configuration as the original BERT; 512 tokens per instance, 256 instances per batch, and 1M training steps.\nFor training of the MLM (masked language modeling) objective, we introduced whole word masking in which all of the subword tokens corresponding to a single word (tokenized by MeCab) are masked at once.\n\nFor training of each model, we used a v3-8 instance of Cloud TPUs provided by TensorFlow Research Cloud program.\nThe training took about 5 days to finish.", "## Licenses\n\nThe pretrained models are distributed under the terms of the Creative Commons Attribution-ShareAlike 3.0.", "## Acknowledgments\n\nThis model is trained with Cloud TPUs provided by TensorFlow Research Cloud program." ]
[ "TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #ja #dataset-wikipedia #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n", "# BERT base Japanese (unidic-lite with whole word masking, jawiki-20200831)\n\nThis is a BERT model pretrained on texts in the Japanese language.\n\nThis version of the model processes input texts with word-level tokenization based on the Unidic 2.1.2 dictionary (available in unidic-lite package), followed by the WordPiece subword tokenization.\nAdditionally, the model is trained with the whole word masking enabled for the masked language modeling (MLM) objective.\n\nThe codes for the pretraining are available at cl-tohoku/bert-japanese.", "## Model architecture\n\nThe model architecture is the same as the original BERT base model; 12 layers, 768 dimensions of hidden states, and 12 attention heads.", "## Training Data\n\nThe models are trained on the Japanese version of Wikipedia.\nThe training corpus is generated from the Wikipedia Cirrussearch dump file as of August 31, 2020.\n\nThe generated corpus files are 4.0GB in total, containing approximately 30M sentences.\nWe used the MeCab morphological parser with mecab-ipadic-NEologd dictionary to split texts into sentences.", "## Tokenization\n\nThe texts are first tokenized by MeCab with the Unidic 2.1.2 dictionary and then split into subwords by the WordPiece algorithm.\nThe vocabulary size is 32768.\n\nWe used 'fugashi' and 'unidic-lite' packages for the tokenization.", "## Training\n\nThe models are trained with the same configuration as the original BERT; 512 tokens per instance, 256 instances per batch, and 1M training steps.\nFor training of the MLM (masked language modeling) objective, we introduced whole word masking in which all of the subword tokens corresponding to a single word (tokenized by MeCab) are masked at once.\n\nFor training of each model, we used a v3-8 instance of Cloud TPUs provided by TensorFlow Research Cloud program.\nThe training took about 5 days to finish.", "## Licenses\n\nThe pretrained models are distributed under the terms of the Creative Commons Attribution-ShareAlike 3.0.", "## Acknowledgments\n\nThis model is trained with Cloud TPUs provided by TensorFlow Research Cloud program." ]
[ 64, 140, 36, 86, 68, 124, 23, 26 ]
[ "passage: TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #ja #dataset-wikipedia #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n# BERT base Japanese (unidic-lite with whole word masking, jawiki-20200831)\n\nThis is a BERT model pretrained on texts in the Japanese language.\n\nThis version of the model processes input texts with word-level tokenization based on the Unidic 2.1.2 dictionary (available in unidic-lite package), followed by the WordPiece subword tokenization.\nAdditionally, the model is trained with the whole word masking enabled for the masked language modeling (MLM) objective.\n\nThe codes for the pretraining are available at cl-tohoku/bert-japanese.## Model architecture\n\nThe model architecture is the same as the original BERT base model; 12 layers, 768 dimensions of hidden states, and 12 attention heads.## Training Data\n\nThe models are trained on the Japanese version of Wikipedia.\nThe training corpus is generated from the Wikipedia Cirrussearch dump file as of August 31, 2020.\n\nThe generated corpus files are 4.0GB in total, containing approximately 30M sentences.\nWe used the MeCab morphological parser with mecab-ipadic-NEologd dictionary to split texts into sentences.## Tokenization\n\nThe texts are first tokenized by MeCab with the Unidic 2.1.2 dictionary and then split into subwords by the WordPiece algorithm.\nThe vocabulary size is 32768.\n\nWe used 'fugashi' and 'unidic-lite' packages for the tokenization." ]
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null
null
transformers
# BERT base Japanese (IPA dictionary, whole word masking enabled) This is a [BERT](https://github.com/google-research/bert) model pretrained on texts in the Japanese language. This version of the model processes input texts with word-level tokenization based on the IPA dictionary, followed by the WordPiece subword tokenization. Additionally, the model is trained with the whole word masking enabled for the masked language modeling (MLM) objective. The codes for the pretraining are available at [cl-tohoku/bert-japanese](https://github.com/cl-tohoku/bert-japanese/tree/v1.0). ## Model architecture The model architecture is the same as the original BERT base model; 12 layers, 768 dimensions of hidden states, and 12 attention heads. ## Training Data The model is trained on Japanese Wikipedia as of September 1, 2019. To generate the training corpus, [WikiExtractor](https://github.com/attardi/wikiextractor) is used to extract plain texts from a dump file of Wikipedia articles. The text files used for the training are 2.6GB in size, consisting of approximately 17M sentences. ## Tokenization The texts are first tokenized by [MeCab](https://taku910.github.io/mecab/) morphological parser with the IPA dictionary and then split into subwords by the WordPiece algorithm. The vocabulary size is 32000. ## Training The model is trained with the same configuration as the original BERT; 512 tokens per instance, 256 instances per batch, and 1M training steps. For the training of the MLM (masked language modeling) objective, we introduced the **Whole Word Masking** in which all of the subword tokens corresponding to a single word (tokenized by MeCab) are masked at once. ## Licenses The pretrained models are distributed under the terms of the [Creative Commons Attribution-ShareAlike 3.0](https://creativecommons.org/licenses/by-sa/3.0/). ## Acknowledgments For training models, we used Cloud TPUs provided by [TensorFlow Research Cloud](https://www.tensorflow.org/tfrc/) program.
{"language": "ja", "license": "cc-by-sa-4.0", "datasets": ["wikipedia"], "widget": [{"text": "\u6771\u5317\u5927\u5b66\u3067[MASK]\u306e\u7814\u7a76\u3092\u3057\u3066\u3044\u307e\u3059\u3002"}]}
fill-mask
tohoku-nlp/bert-base-japanese-whole-word-masking
[ "transformers", "pytorch", "tf", "jax", "bert", "fill-mask", "ja", "dataset:wikipedia", "license:cc-by-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[ "ja" ]
TAGS #transformers #pytorch #tf #jax #bert #fill-mask #ja #dataset-wikipedia #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
# BERT base Japanese (IPA dictionary, whole word masking enabled) This is a BERT model pretrained on texts in the Japanese language. This version of the model processes input texts with word-level tokenization based on the IPA dictionary, followed by the WordPiece subword tokenization. Additionally, the model is trained with the whole word masking enabled for the masked language modeling (MLM) objective. The codes for the pretraining are available at cl-tohoku/bert-japanese. ## Model architecture The model architecture is the same as the original BERT base model; 12 layers, 768 dimensions of hidden states, and 12 attention heads. ## Training Data The model is trained on Japanese Wikipedia as of September 1, 2019. To generate the training corpus, WikiExtractor is used to extract plain texts from a dump file of Wikipedia articles. The text files used for the training are 2.6GB in size, consisting of approximately 17M sentences. ## Tokenization The texts are first tokenized by MeCab morphological parser with the IPA dictionary and then split into subwords by the WordPiece algorithm. The vocabulary size is 32000. ## Training The model is trained with the same configuration as the original BERT; 512 tokens per instance, 256 instances per batch, and 1M training steps. For the training of the MLM (masked language modeling) objective, we introduced the Whole Word Masking in which all of the subword tokens corresponding to a single word (tokenized by MeCab) are masked at once. ## Licenses The pretrained models are distributed under the terms of the Creative Commons Attribution-ShareAlike 3.0. ## Acknowledgments For training models, we used Cloud TPUs provided by TensorFlow Research Cloud program.
[ "# BERT base Japanese (IPA dictionary, whole word masking enabled)\n\nThis is a BERT model pretrained on texts in the Japanese language.\n\nThis version of the model processes input texts with word-level tokenization based on the IPA dictionary, followed by the WordPiece subword tokenization.\nAdditionally, the model is trained with the whole word masking enabled for the masked language modeling (MLM) objective.\n\nThe codes for the pretraining are available at cl-tohoku/bert-japanese.", "## Model architecture\n\nThe model architecture is the same as the original BERT base model; 12 layers, 768 dimensions of hidden states, and 12 attention heads.", "## Training Data\n\nThe model is trained on Japanese Wikipedia as of September 1, 2019.\nTo generate the training corpus, WikiExtractor is used to extract plain texts from a dump file of Wikipedia articles.\nThe text files used for the training are 2.6GB in size, consisting of approximately 17M sentences.", "## Tokenization\n\nThe texts are first tokenized by MeCab morphological parser with the IPA dictionary and then split into subwords by the WordPiece algorithm.\nThe vocabulary size is 32000.", "## Training\n\nThe model is trained with the same configuration as the original BERT; 512 tokens per instance, 256 instances per batch, and 1M training steps.\n\nFor the training of the MLM (masked language modeling) objective, we introduced the Whole Word Masking in which all of the subword tokens corresponding to a single word (tokenized by MeCab) are masked at once.", "## Licenses\n\nThe pretrained models are distributed under the terms of the Creative Commons Attribution-ShareAlike 3.0.", "## Acknowledgments\n\nFor training models, we used Cloud TPUs provided by TensorFlow Research Cloud program." ]
[ "TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #ja #dataset-wikipedia #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# BERT base Japanese (IPA dictionary, whole word masking enabled)\n\nThis is a BERT model pretrained on texts in the Japanese language.\n\nThis version of the model processes input texts with word-level tokenization based on the IPA dictionary, followed by the WordPiece subword tokenization.\nAdditionally, the model is trained with the whole word masking enabled for the masked language modeling (MLM) objective.\n\nThe codes for the pretraining are available at cl-tohoku/bert-japanese.", "## Model architecture\n\nThe model architecture is the same as the original BERT base model; 12 layers, 768 dimensions of hidden states, and 12 attention heads.", "## Training Data\n\nThe model is trained on Japanese Wikipedia as of September 1, 2019.\nTo generate the training corpus, WikiExtractor is used to extract plain texts from a dump file of Wikipedia articles.\nThe text files used for the training are 2.6GB in size, consisting of approximately 17M sentences.", "## Tokenization\n\nThe texts are first tokenized by MeCab morphological parser with the IPA dictionary and then split into subwords by the WordPiece algorithm.\nThe vocabulary size is 32000.", "## Training\n\nThe model is trained with the same configuration as the original BERT; 512 tokens per instance, 256 instances per batch, and 1M training steps.\n\nFor the training of the MLM (masked language modeling) objective, we introduced the Whole Word Masking in which all of the subword tokens corresponding to a single word (tokenized by MeCab) are masked at once.", "## Licenses\n\nThe pretrained models are distributed under the terms of the Creative Commons Attribution-ShareAlike 3.0.", "## Acknowledgments\n\nFor training models, we used Cloud TPUs provided by TensorFlow Research Cloud program." ]
[ 60, 124, 36, 65, 50, 91, 23, 26 ]
[ "passage: TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #ja #dataset-wikipedia #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n# BERT base Japanese (IPA dictionary, whole word masking enabled)\n\nThis is a BERT model pretrained on texts in the Japanese language.\n\nThis version of the model processes input texts with word-level tokenization based on the IPA dictionary, followed by the WordPiece subword tokenization.\nAdditionally, the model is trained with the whole word masking enabled for the masked language modeling (MLM) objective.\n\nThe codes for the pretraining are available at cl-tohoku/bert-japanese.## Model architecture\n\nThe model architecture is the same as the original BERT base model; 12 layers, 768 dimensions of hidden states, and 12 attention heads.## Training Data\n\nThe model is trained on Japanese Wikipedia as of September 1, 2019.\nTo generate the training corpus, WikiExtractor is used to extract plain texts from a dump file of Wikipedia articles.\nThe text files used for the training are 2.6GB in size, consisting of approximately 17M sentences.## Tokenization\n\nThe texts are first tokenized by MeCab morphological parser with the IPA dictionary and then split into subwords by the WordPiece algorithm.\nThe vocabulary size is 32000.## Training\n\nThe model is trained with the same configuration as the original BERT; 512 tokens per instance, 256 instances per batch, and 1M training steps.\n\nFor the training of the MLM (masked language modeling) objective, we introduced the Whole Word Masking in which all of the subword tokens corresponding to a single word (tokenized by MeCab) are masked at once.## Licenses\n\nThe pretrained models are distributed under the terms of the Creative Commons Attribution-ShareAlike 3.0.## Acknowledgments\n\nFor training models, we used Cloud TPUs provided by TensorFlow Research Cloud program." ]
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null
null
transformers
# BERT base Japanese (IPA dictionary) This is a [BERT](https://github.com/google-research/bert) model pretrained on texts in the Japanese language. This version of the model processes input texts with word-level tokenization based on the IPA dictionary, followed by the WordPiece subword tokenization. The codes for the pretraining are available at [cl-tohoku/bert-japanese](https://github.com/cl-tohoku/bert-japanese/tree/v1.0). ## Model architecture The model architecture is the same as the original BERT base model; 12 layers, 768 dimensions of hidden states, and 12 attention heads. ## Training Data The model is trained on Japanese Wikipedia as of September 1, 2019. To generate the training corpus, [WikiExtractor](https://github.com/attardi/wikiextractor) is used to extract plain texts from a dump file of Wikipedia articles. The text files used for the training are 2.6GB in size, consisting of approximately 17M sentences. ## Tokenization The texts are first tokenized by [MeCab](https://taku910.github.io/mecab/) morphological parser with the IPA dictionary and then split into subwords by the WordPiece algorithm. The vocabulary size is 32000. ## Training The model is trained with the same configuration as the original BERT; 512 tokens per instance, 256 instances per batch, and 1M training steps. ## Licenses The pretrained models are distributed under the terms of the [Creative Commons Attribution-ShareAlike 3.0](https://creativecommons.org/licenses/by-sa/3.0/). ## Acknowledgments For training models, we used Cloud TPUs provided by [TensorFlow Research Cloud](https://www.tensorflow.org/tfrc/) program.
{"language": "ja", "license": "cc-by-sa-4.0", "datasets": ["wikipedia"], "widget": [{"text": "\u6771\u5317\u5927\u5b66\u3067[MASK]\u306e\u7814\u7a76\u3092\u3057\u3066\u3044\u307e\u3059\u3002"}]}
fill-mask
tohoku-nlp/bert-base-japanese
[ "transformers", "pytorch", "tf", "jax", "bert", "fill-mask", "ja", "dataset:wikipedia", "license:cc-by-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[ "ja" ]
TAGS #transformers #pytorch #tf #jax #bert #fill-mask #ja #dataset-wikipedia #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
# BERT base Japanese (IPA dictionary) This is a BERT model pretrained on texts in the Japanese language. This version of the model processes input texts with word-level tokenization based on the IPA dictionary, followed by the WordPiece subword tokenization. The codes for the pretraining are available at cl-tohoku/bert-japanese. ## Model architecture The model architecture is the same as the original BERT base model; 12 layers, 768 dimensions of hidden states, and 12 attention heads. ## Training Data The model is trained on Japanese Wikipedia as of September 1, 2019. To generate the training corpus, WikiExtractor is used to extract plain texts from a dump file of Wikipedia articles. The text files used for the training are 2.6GB in size, consisting of approximately 17M sentences. ## Tokenization The texts are first tokenized by MeCab morphological parser with the IPA dictionary and then split into subwords by the WordPiece algorithm. The vocabulary size is 32000. ## Training The model is trained with the same configuration as the original BERT; 512 tokens per instance, 256 instances per batch, and 1M training steps. ## Licenses The pretrained models are distributed under the terms of the Creative Commons Attribution-ShareAlike 3.0. ## Acknowledgments For training models, we used Cloud TPUs provided by TensorFlow Research Cloud program.
[ "# BERT base Japanese (IPA dictionary)\n\nThis is a BERT model pretrained on texts in the Japanese language.\n\nThis version of the model processes input texts with word-level tokenization based on the IPA dictionary, followed by the WordPiece subword tokenization.\n\nThe codes for the pretraining are available at cl-tohoku/bert-japanese.", "## Model architecture\n\nThe model architecture is the same as the original BERT base model; 12 layers, 768 dimensions of hidden states, and 12 attention heads.", "## Training Data\n\nThe model is trained on Japanese Wikipedia as of September 1, 2019.\nTo generate the training corpus, WikiExtractor is used to extract plain texts from a dump file of Wikipedia articles.\nThe text files used for the training are 2.6GB in size, consisting of approximately 17M sentences.", "## Tokenization\n\nThe texts are first tokenized by MeCab morphological parser with the IPA dictionary and then split into subwords by the WordPiece algorithm.\nThe vocabulary size is 32000.", "## Training\n\nThe model is trained with the same configuration as the original BERT; 512 tokens per instance, 256 instances per batch, and 1M training steps.", "## Licenses\n\nThe pretrained models are distributed under the terms of the Creative Commons Attribution-ShareAlike 3.0.", "## Acknowledgments\n\nFor training models, we used Cloud TPUs provided by TensorFlow Research Cloud program." ]
[ "TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #ja #dataset-wikipedia #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# BERT base Japanese (IPA dictionary)\n\nThis is a BERT model pretrained on texts in the Japanese language.\n\nThis version of the model processes input texts with word-level tokenization based on the IPA dictionary, followed by the WordPiece subword tokenization.\n\nThe codes for the pretraining are available at cl-tohoku/bert-japanese.", "## Model architecture\n\nThe model architecture is the same as the original BERT base model; 12 layers, 768 dimensions of hidden states, and 12 attention heads.", "## Training Data\n\nThe model is trained on Japanese Wikipedia as of September 1, 2019.\nTo generate the training corpus, WikiExtractor is used to extract plain texts from a dump file of Wikipedia articles.\nThe text files used for the training are 2.6GB in size, consisting of approximately 17M sentences.", "## Tokenization\n\nThe texts are first tokenized by MeCab morphological parser with the IPA dictionary and then split into subwords by the WordPiece algorithm.\nThe vocabulary size is 32000.", "## Training\n\nThe model is trained with the same configuration as the original BERT; 512 tokens per instance, 256 instances per batch, and 1M training steps.", "## Licenses\n\nThe pretrained models are distributed under the terms of the Creative Commons Attribution-ShareAlike 3.0.", "## Acknowledgments\n\nFor training models, we used Cloud TPUs provided by TensorFlow Research Cloud program." ]
[ 60, 88, 36, 65, 50, 36, 23, 26 ]
[ "passage: TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #ja #dataset-wikipedia #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n# BERT base Japanese (IPA dictionary)\n\nThis is a BERT model pretrained on texts in the Japanese language.\n\nThis version of the model processes input texts with word-level tokenization based on the IPA dictionary, followed by the WordPiece subword tokenization.\n\nThe codes for the pretraining are available at cl-tohoku/bert-japanese.## Model architecture\n\nThe model architecture is the same as the original BERT base model; 12 layers, 768 dimensions of hidden states, and 12 attention heads.## Training Data\n\nThe model is trained on Japanese Wikipedia as of September 1, 2019.\nTo generate the training corpus, WikiExtractor is used to extract plain texts from a dump file of Wikipedia articles.\nThe text files used for the training are 2.6GB in size, consisting of approximately 17M sentences.## Tokenization\n\nThe texts are first tokenized by MeCab morphological parser with the IPA dictionary and then split into subwords by the WordPiece algorithm.\nThe vocabulary size is 32000.## Training\n\nThe model is trained with the same configuration as the original BERT; 512 tokens per instance, 256 instances per batch, and 1M training steps.## Licenses\n\nThe pretrained models are distributed under the terms of the Creative Commons Attribution-ShareAlike 3.0.## Acknowledgments\n\nFor training models, we used Cloud TPUs provided by TensorFlow Research Cloud program." ]
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null
null
transformers
# BERT large Japanese (character-level tokenization with whole word masking, jawiki-20200831) This is a [BERT](https://github.com/google-research/bert) model pretrained on texts in the Japanese language. This version of the model processes input texts with word-level tokenization based on the Unidic 2.1.2 dictionary (available in [unidic-lite](https://pypi.org/project/unidic-lite/) package), followed by character-level tokenization. Additionally, the model is trained with the whole word masking enabled for the masked language modeling (MLM) objective. The codes for the pretraining are available at [cl-tohoku/bert-japanese](https://github.com/cl-tohoku/bert-japanese/tree/v2.0). ## Model architecture The model architecture is the same as the original BERT large model; 24 layers, 1024 dimensions of hidden states, and 16 attention heads. ## Training Data The models are trained on the Japanese version of Wikipedia. The training corpus is generated from the Wikipedia Cirrussearch dump file as of August 31, 2020. The generated corpus files are 4.0GB in total, containing approximately 30M sentences. We used the [MeCab](https://taku910.github.io/mecab/) morphological parser with [mecab-ipadic-NEologd](https://github.com/neologd/mecab-ipadic-neologd) dictionary to split texts into sentences. ## Tokenization The texts are first tokenized by MeCab with the Unidic 2.1.2 dictionary and then split into characters. The vocabulary size is 6144. We used [`fugashi`](https://github.com/polm/fugashi) and [`unidic-lite`](https://github.com/polm/unidic-lite) packages for the tokenization. ## Training The models are trained with the same configuration as the original BERT; 512 tokens per instance, 256 instances per batch, and 1M training steps. For training of the MLM (masked language modeling) objective, we introduced whole word masking in which all of the subword tokens corresponding to a single word (tokenized by MeCab) are masked at once. For training of each model, we used a v3-8 instance of Cloud TPUs provided by [TensorFlow Research Cloud program](https://www.tensorflow.org/tfrc/). The training took about 5 days to finish. ## Licenses The pretrained models are distributed under the terms of the [Creative Commons Attribution-ShareAlike 3.0](https://creativecommons.org/licenses/by-sa/3.0/). ## Acknowledgments This model is trained with Cloud TPUs provided by [TensorFlow Research Cloud](https://www.tensorflow.org/tfrc/) program.
{"language": "ja", "license": "cc-by-sa-4.0", "datasets": ["wikipedia"], "widget": [{"text": "\u6771\u5317\u5927\u5b66\u3067[MASK]\u306e\u7814\u7a76\u3092\u3057\u3066\u3044\u307e\u3059\u3002"}]}
fill-mask
tohoku-nlp/bert-large-japanese-char
[ "transformers", "pytorch", "tf", "jax", "bert", "fill-mask", "ja", "dataset:wikipedia", "license:cc-by-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[ "ja" ]
TAGS #transformers #pytorch #tf #jax #bert #fill-mask #ja #dataset-wikipedia #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
# BERT large Japanese (character-level tokenization with whole word masking, jawiki-20200831) This is a BERT model pretrained on texts in the Japanese language. This version of the model processes input texts with word-level tokenization based on the Unidic 2.1.2 dictionary (available in unidic-lite package), followed by character-level tokenization. Additionally, the model is trained with the whole word masking enabled for the masked language modeling (MLM) objective. The codes for the pretraining are available at cl-tohoku/bert-japanese. ## Model architecture The model architecture is the same as the original BERT large model; 24 layers, 1024 dimensions of hidden states, and 16 attention heads. ## Training Data The models are trained on the Japanese version of Wikipedia. The training corpus is generated from the Wikipedia Cirrussearch dump file as of August 31, 2020. The generated corpus files are 4.0GB in total, containing approximately 30M sentences. We used the MeCab morphological parser with mecab-ipadic-NEologd dictionary to split texts into sentences. ## Tokenization The texts are first tokenized by MeCab with the Unidic 2.1.2 dictionary and then split into characters. The vocabulary size is 6144. We used 'fugashi' and 'unidic-lite' packages for the tokenization. ## Training The models are trained with the same configuration as the original BERT; 512 tokens per instance, 256 instances per batch, and 1M training steps. For training of the MLM (masked language modeling) objective, we introduced whole word masking in which all of the subword tokens corresponding to a single word (tokenized by MeCab) are masked at once. For training of each model, we used a v3-8 instance of Cloud TPUs provided by TensorFlow Research Cloud program. The training took about 5 days to finish. ## Licenses The pretrained models are distributed under the terms of the Creative Commons Attribution-ShareAlike 3.0. ## Acknowledgments This model is trained with Cloud TPUs provided by TensorFlow Research Cloud program.
[ "# BERT large Japanese (character-level tokenization with whole word masking, jawiki-20200831)\n\nThis is a BERT model pretrained on texts in the Japanese language.\n\nThis version of the model processes input texts with word-level tokenization based on the Unidic 2.1.2 dictionary (available in unidic-lite package), followed by character-level tokenization.\nAdditionally, the model is trained with the whole word masking enabled for the masked language modeling (MLM) objective.\n\nThe codes for the pretraining are available at cl-tohoku/bert-japanese.", "## Model architecture\n\nThe model architecture is the same as the original BERT large model; 24 layers, 1024 dimensions of hidden states, and 16 attention heads.", "## Training Data\n\nThe models are trained on the Japanese version of Wikipedia.\nThe training corpus is generated from the Wikipedia Cirrussearch dump file as of August 31, 2020.\n\nThe generated corpus files are 4.0GB in total, containing approximately 30M sentences.\nWe used the MeCab morphological parser with mecab-ipadic-NEologd dictionary to split texts into sentences.", "## Tokenization\n\nThe texts are first tokenized by MeCab with the Unidic 2.1.2 dictionary and then split into characters.\nThe vocabulary size is 6144.\n\nWe used 'fugashi' and 'unidic-lite' packages for the tokenization.", "## Training\n\nThe models are trained with the same configuration as the original BERT; 512 tokens per instance, 256 instances per batch, and 1M training steps.\nFor training of the MLM (masked language modeling) objective, we introduced whole word masking in which all of the subword tokens corresponding to a single word (tokenized by MeCab) are masked at once.\n\nFor training of each model, we used a v3-8 instance of Cloud TPUs provided by TensorFlow Research Cloud program.\nThe training took about 5 days to finish.", "## Licenses\n\nThe pretrained models are distributed under the terms of the Creative Commons Attribution-ShareAlike 3.0.", "## Acknowledgments\n\nThis model is trained with Cloud TPUs provided by TensorFlow Research Cloud program." ]
[ "TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #ja #dataset-wikipedia #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# BERT large Japanese (character-level tokenization with whole word masking, jawiki-20200831)\n\nThis is a BERT model pretrained on texts in the Japanese language.\n\nThis version of the model processes input texts with word-level tokenization based on the Unidic 2.1.2 dictionary (available in unidic-lite package), followed by character-level tokenization.\nAdditionally, the model is trained with the whole word masking enabled for the masked language modeling (MLM) objective.\n\nThe codes for the pretraining are available at cl-tohoku/bert-japanese.", "## Model architecture\n\nThe model architecture is the same as the original BERT large model; 24 layers, 1024 dimensions of hidden states, and 16 attention heads.", "## Training Data\n\nThe models are trained on the Japanese version of Wikipedia.\nThe training corpus is generated from the Wikipedia Cirrussearch dump file as of August 31, 2020.\n\nThe generated corpus files are 4.0GB in total, containing approximately 30M sentences.\nWe used the MeCab morphological parser with mecab-ipadic-NEologd dictionary to split texts into sentences.", "## Tokenization\n\nThe texts are first tokenized by MeCab with the Unidic 2.1.2 dictionary and then split into characters.\nThe vocabulary size is 6144.\n\nWe used 'fugashi' and 'unidic-lite' packages for the tokenization.", "## Training\n\nThe models are trained with the same configuration as the original BERT; 512 tokens per instance, 256 instances per batch, and 1M training steps.\nFor training of the MLM (masked language modeling) objective, we introduced whole word masking in which all of the subword tokens corresponding to a single word (tokenized by MeCab) are masked at once.\n\nFor training of each model, we used a v3-8 instance of Cloud TPUs provided by TensorFlow Research Cloud program.\nThe training took about 5 days to finish.", "## Licenses\n\nThe pretrained models are distributed under the terms of the Creative Commons Attribution-ShareAlike 3.0.", "## Acknowledgments\n\nThis model is trained with Cloud TPUs provided by TensorFlow Research Cloud program." ]
[ 60, 141, 35, 86, 60, 124, 23, 26 ]
[ "passage: TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #ja #dataset-wikipedia #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n# BERT large Japanese (character-level tokenization with whole word masking, jawiki-20200831)\n\nThis is a BERT model pretrained on texts in the Japanese language.\n\nThis version of the model processes input texts with word-level tokenization based on the Unidic 2.1.2 dictionary (available in unidic-lite package), followed by character-level tokenization.\nAdditionally, the model is trained with the whole word masking enabled for the masked language modeling (MLM) objective.\n\nThe codes for the pretraining are available at cl-tohoku/bert-japanese.## Model architecture\n\nThe model architecture is the same as the original BERT large model; 24 layers, 1024 dimensions of hidden states, and 16 attention heads.## Training Data\n\nThe models are trained on the Japanese version of Wikipedia.\nThe training corpus is generated from the Wikipedia Cirrussearch dump file as of August 31, 2020.\n\nThe generated corpus files are 4.0GB in total, containing approximately 30M sentences.\nWe used the MeCab morphological parser with mecab-ipadic-NEologd dictionary to split texts into sentences.## Tokenization\n\nThe texts are first tokenized by MeCab with the Unidic 2.1.2 dictionary and then split into characters.\nThe vocabulary size is 6144.\n\nWe used 'fugashi' and 'unidic-lite' packages for the tokenization.## Training\n\nThe models are trained with the same configuration as the original BERT; 512 tokens per instance, 256 instances per batch, and 1M training steps.\nFor training of the MLM (masked language modeling) objective, we introduced whole word masking in which all of the subword tokens corresponding to a single word (tokenized by MeCab) are masked at once.\n\nFor training of each model, we used a v3-8 instance of Cloud TPUs provided by TensorFlow Research Cloud program.\nThe training took about 5 days to finish." ]
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null
null
transformers
# BERT large Japanese (unidic-lite with whole word masking, jawiki-20200831) This is a [BERT](https://github.com/google-research/bert) model pretrained on texts in the Japanese language. This version of the model processes input texts with word-level tokenization based on the Unidic 2.1.2 dictionary (available in [unidic-lite](https://pypi.org/project/unidic-lite/) package), followed by the WordPiece subword tokenization. Additionally, the model is trained with the whole word masking enabled for the masked language modeling (MLM) objective. The codes for the pretraining are available at [cl-tohoku/bert-japanese](https://github.com/cl-tohoku/bert-japanese/tree/v2.0). ## Model architecture The model architecture is the same as the original BERT large model; 24 layers, 1024 dimensions of hidden states, and 16 attention heads. ## Training Data The models are trained on the Japanese version of Wikipedia. The training corpus is generated from the Wikipedia Cirrussearch dump file as of August 31, 2020. The generated corpus files are 4.0GB in total, containing approximately 30M sentences. We used the [MeCab](https://taku910.github.io/mecab/) morphological parser with [mecab-ipadic-NEologd](https://github.com/neologd/mecab-ipadic-neologd) dictionary to split texts into sentences. ## Tokenization The texts are first tokenized by MeCab with the Unidic 2.1.2 dictionary and then split into subwords by the WordPiece algorithm. The vocabulary size is 32768. We used [`fugashi`](https://github.com/polm/fugashi) and [`unidic-lite`](https://github.com/polm/unidic-lite) packages for the tokenization. ## Training The models are trained with the same configuration as the original BERT; 512 tokens per instance, 256 instances per batch, and 1M training steps. For training of the MLM (masked language modeling) objective, we introduced whole word masking in which all of the subword tokens corresponding to a single word (tokenized by MeCab) are masked at once. For training of each model, we used a v3-8 instance of Cloud TPUs provided by [TensorFlow Research Cloud program](https://www.tensorflow.org/tfrc/). The training took about 5 days to finish. ## Licenses The pretrained models are distributed under the terms of the [Creative Commons Attribution-ShareAlike 3.0](https://creativecommons.org/licenses/by-sa/3.0/). ## Acknowledgments This model is trained with Cloud TPUs provided by [TensorFlow Research Cloud](https://www.tensorflow.org/tfrc/) program.
{"language": "ja", "license": "cc-by-sa-4.0", "datasets": ["wikipedia"], "widget": [{"text": "\u6771\u5317\u5927\u5b66\u3067[MASK]\u306e\u7814\u7a76\u3092\u3057\u3066\u3044\u307e\u3059\u3002"}]}
fill-mask
tohoku-nlp/bert-large-japanese
[ "transformers", "pytorch", "tf", "jax", "bert", "fill-mask", "ja", "dataset:wikipedia", "license:cc-by-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[ "ja" ]
TAGS #transformers #pytorch #tf #jax #bert #fill-mask #ja #dataset-wikipedia #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
# BERT large Japanese (unidic-lite with whole word masking, jawiki-20200831) This is a BERT model pretrained on texts in the Japanese language. This version of the model processes input texts with word-level tokenization based on the Unidic 2.1.2 dictionary (available in unidic-lite package), followed by the WordPiece subword tokenization. Additionally, the model is trained with the whole word masking enabled for the masked language modeling (MLM) objective. The codes for the pretraining are available at cl-tohoku/bert-japanese. ## Model architecture The model architecture is the same as the original BERT large model; 24 layers, 1024 dimensions of hidden states, and 16 attention heads. ## Training Data The models are trained on the Japanese version of Wikipedia. The training corpus is generated from the Wikipedia Cirrussearch dump file as of August 31, 2020. The generated corpus files are 4.0GB in total, containing approximately 30M sentences. We used the MeCab morphological parser with mecab-ipadic-NEologd dictionary to split texts into sentences. ## Tokenization The texts are first tokenized by MeCab with the Unidic 2.1.2 dictionary and then split into subwords by the WordPiece algorithm. The vocabulary size is 32768. We used 'fugashi' and 'unidic-lite' packages for the tokenization. ## Training The models are trained with the same configuration as the original BERT; 512 tokens per instance, 256 instances per batch, and 1M training steps. For training of the MLM (masked language modeling) objective, we introduced whole word masking in which all of the subword tokens corresponding to a single word (tokenized by MeCab) are masked at once. For training of each model, we used a v3-8 instance of Cloud TPUs provided by TensorFlow Research Cloud program. The training took about 5 days to finish. ## Licenses The pretrained models are distributed under the terms of the Creative Commons Attribution-ShareAlike 3.0. ## Acknowledgments This model is trained with Cloud TPUs provided by TensorFlow Research Cloud program.
[ "# BERT large Japanese (unidic-lite with whole word masking, jawiki-20200831)\n\nThis is a BERT model pretrained on texts in the Japanese language.\n\nThis version of the model processes input texts with word-level tokenization based on the Unidic 2.1.2 dictionary (available in unidic-lite package), followed by the WordPiece subword tokenization.\nAdditionally, the model is trained with the whole word masking enabled for the masked language modeling (MLM) objective.\n\nThe codes for the pretraining are available at cl-tohoku/bert-japanese.", "## Model architecture\n\nThe model architecture is the same as the original BERT large model; 24 layers, 1024 dimensions of hidden states, and 16 attention heads.", "## Training Data\n\nThe models are trained on the Japanese version of Wikipedia.\nThe training corpus is generated from the Wikipedia Cirrussearch dump file as of August 31, 2020.\n\nThe generated corpus files are 4.0GB in total, containing approximately 30M sentences.\nWe used the MeCab morphological parser with mecab-ipadic-NEologd dictionary to split texts into sentences.", "## Tokenization\n\nThe texts are first tokenized by MeCab with the Unidic 2.1.2 dictionary and then split into subwords by the WordPiece algorithm.\nThe vocabulary size is 32768.\n\nWe used 'fugashi' and 'unidic-lite' packages for the tokenization.", "## Training\n\nThe models are trained with the same configuration as the original BERT; 512 tokens per instance, 256 instances per batch, and 1M training steps.\nFor training of the MLM (masked language modeling) objective, we introduced whole word masking in which all of the subword tokens corresponding to a single word (tokenized by MeCab) are masked at once.\n\nFor training of each model, we used a v3-8 instance of Cloud TPUs provided by TensorFlow Research Cloud program.\nThe training took about 5 days to finish.", "## Licenses\n\nThe pretrained models are distributed under the terms of the Creative Commons Attribution-ShareAlike 3.0.", "## Acknowledgments\n\nThis model is trained with Cloud TPUs provided by TensorFlow Research Cloud program." ]
[ "TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #ja #dataset-wikipedia #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# BERT large Japanese (unidic-lite with whole word masking, jawiki-20200831)\n\nThis is a BERT model pretrained on texts in the Japanese language.\n\nThis version of the model processes input texts with word-level tokenization based on the Unidic 2.1.2 dictionary (available in unidic-lite package), followed by the WordPiece subword tokenization.\nAdditionally, the model is trained with the whole word masking enabled for the masked language modeling (MLM) objective.\n\nThe codes for the pretraining are available at cl-tohoku/bert-japanese.", "## Model architecture\n\nThe model architecture is the same as the original BERT large model; 24 layers, 1024 dimensions of hidden states, and 16 attention heads.", "## Training Data\n\nThe models are trained on the Japanese version of Wikipedia.\nThe training corpus is generated from the Wikipedia Cirrussearch dump file as of August 31, 2020.\n\nThe generated corpus files are 4.0GB in total, containing approximately 30M sentences.\nWe used the MeCab morphological parser with mecab-ipadic-NEologd dictionary to split texts into sentences.", "## Tokenization\n\nThe texts are first tokenized by MeCab with the Unidic 2.1.2 dictionary and then split into subwords by the WordPiece algorithm.\nThe vocabulary size is 32768.\n\nWe used 'fugashi' and 'unidic-lite' packages for the tokenization.", "## Training\n\nThe models are trained with the same configuration as the original BERT; 512 tokens per instance, 256 instances per batch, and 1M training steps.\nFor training of the MLM (masked language modeling) objective, we introduced whole word masking in which all of the subword tokens corresponding to a single word (tokenized by MeCab) are masked at once.\n\nFor training of each model, we used a v3-8 instance of Cloud TPUs provided by TensorFlow Research Cloud program.\nThe training took about 5 days to finish.", "## Licenses\n\nThe pretrained models are distributed under the terms of the Creative Commons Attribution-ShareAlike 3.0.", "## Acknowledgments\n\nThis model is trained with Cloud TPUs provided by TensorFlow Research Cloud program." ]
[ 60, 140, 35, 86, 68, 124, 23, 26 ]
[ "passage: TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #ja #dataset-wikipedia #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n# BERT large Japanese (unidic-lite with whole word masking, jawiki-20200831)\n\nThis is a BERT model pretrained on texts in the Japanese language.\n\nThis version of the model processes input texts with word-level tokenization based on the Unidic 2.1.2 dictionary (available in unidic-lite package), followed by the WordPiece subword tokenization.\nAdditionally, the model is trained with the whole word masking enabled for the masked language modeling (MLM) objective.\n\nThe codes for the pretraining are available at cl-tohoku/bert-japanese.## Model architecture\n\nThe model architecture is the same as the original BERT large model; 24 layers, 1024 dimensions of hidden states, and 16 attention heads.## Training Data\n\nThe models are trained on the Japanese version of Wikipedia.\nThe training corpus is generated from the Wikipedia Cirrussearch dump file as of August 31, 2020.\n\nThe generated corpus files are 4.0GB in total, containing approximately 30M sentences.\nWe used the MeCab morphological parser with mecab-ipadic-NEologd dictionary to split texts into sentences.## Tokenization\n\nThe texts are first tokenized by MeCab with the Unidic 2.1.2 dictionary and then split into subwords by the WordPiece algorithm.\nThe vocabulary size is 32768.\n\nWe used 'fugashi' and 'unidic-lite' packages for the tokenization." ]
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null
null
transformers
# A somewhat positive chatbot
{"tags": ["conversational"]}
text-generation
clairesb/kindness_bot
[ "transformers", "pytorch", "gpt2", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[]
TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
# A somewhat positive chatbot
[ "# A somewhat positive chatbot" ]
[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# A somewhat positive chatbot" ]
[ 51, 6 ]
[ "passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# A somewhat positive chatbot" ]
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null
null
transformers
# Affirmation Bot
{"tags": ["conversational"]}
text-generation
clairesb/kindness_bot_repo
[ "transformers", "pytorch", "gpt2", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[]
TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
# Affirmation Bot
[ "# Affirmation Bot" ]
[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Affirmation Bot" ]
[ 51, 5 ]
[ "passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Affirmation Bot" ]
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null
null
transformers
# Multi-lingual sentiment prediction trained from COVID19-related tweets Repository: [https://github.com/clampert/multilingual-sentiment-analysis/](https://github.com/clampert/multilingual-sentiment-analysis/) Model trained on a large-scale (18437530 examples) dataset of multi-lingual tweets that was collected between March 2020 and November 2021 using Twitter’s Streaming API with varying COVID19-related keywords. Labels were auto-general based on the presence of positive and negative emoticons. For details on the dataset, see our IEEE BigData 2021 publication. Base model is [sentence-transformers/stsb-xlm-r-multilingual](https://huggingface.co/sentence-transformers/stsb-xlm-r-multilingual). It was finetuned for sequence classification with `positive` and `negative` labels for two epochs (48 hours on 8xP100 GPUs). ## Citation If you use our model your work, please cite: ``` @inproceedings{lampert2021overcoming, title={Overcoming Rare-Language Discrimination in Multi-Lingual Sentiment Analysis}, author={Jasmin Lampert and Christoph H. Lampert}, booktitle={IEEE International Conference on Big Data (BigData)}, year={2021}, note={Special Session: Machine Learning on Big Data}, } ``` Enjoy!
{"language": "multilingual", "license": "apache-2.0", "tags": ["sentiment-analysis", "multilingual"], "pipeline_tag": "text-classification", "widget": [{"text": "I am very happy.", "example_title": "English"}, {"text": "Heute bin ich schlecht drauf.", "example_title": "Deutsch"}, {"text": "Quel cauchemard!", "example_title": "Francais"}, {"text": "\u0e09\u0e31\u0e19\u0e23\u0e31\u0e01\u0e24\u0e14\u0e39\u0e43\u0e1a\u0e44\u0e21\u0e49\u0e1c\u0e25\u0e34", "example_title": "\u0e20\u0e32\u0e29\u0e32\u0e44\u0e17\u0e22"}]}
text-classification
clampert/multilingual-sentiment-covid19
[ "transformers", "pytorch", "xlm-roberta", "text-classification", "sentiment-analysis", "multilingual", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[ "multilingual" ]
TAGS #transformers #pytorch #xlm-roberta #text-classification #sentiment-analysis #multilingual #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
# Multi-lingual sentiment prediction trained from COVID19-related tweets Repository: URL Model trained on a large-scale (18437530 examples) dataset of multi-lingual tweets that was collected between March 2020 and November 2021 using Twitter’s Streaming API with varying COVID19-related keywords. Labels were auto-general based on the presence of positive and negative emoticons. For details on the dataset, see our IEEE BigData 2021 publication. Base model is sentence-transformers/stsb-xlm-r-multilingual. It was finetuned for sequence classification with 'positive' and 'negative' labels for two epochs (48 hours on 8xP100 GPUs). If you use our model your work, please cite: Enjoy!
[ "# Multi-lingual sentiment prediction trained from COVID19-related tweets\n\nRepository: URL\n\nModel trained on a large-scale (18437530 examples) dataset of \nmulti-lingual tweets that was collected between March 2020 \nand November 2021 using Twitter’s Streaming API with varying\nCOVID19-related keywords. Labels were auto-general based on \nthe presence of positive and negative emoticons. For details\non the dataset, see our IEEE BigData 2021 publication. \n\nBase model is sentence-transformers/stsb-xlm-r-multilingual.\nIt was finetuned for sequence classification with 'positive' \nand 'negative' labels for two epochs (48 hours on 8xP100 GPUs). \n\nIf you use our model your work, please cite:\n\n\n\nEnjoy!" ]
[ "TAGS\n#transformers #pytorch #xlm-roberta #text-classification #sentiment-analysis #multilingual #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# Multi-lingual sentiment prediction trained from COVID19-related tweets\n\nRepository: URL\n\nModel trained on a large-scale (18437530 examples) dataset of \nmulti-lingual tweets that was collected between March 2020 \nand November 2021 using Twitter’s Streaming API with varying\nCOVID19-related keywords. Labels were auto-general based on \nthe presence of positive and negative emoticons. For details\non the dataset, see our IEEE BigData 2021 publication. \n\nBase model is sentence-transformers/stsb-xlm-r-multilingual.\nIt was finetuned for sequence classification with 'positive' \nand 'negative' labels for two epochs (48 hours on 8xP100 GPUs). \n\nIf you use our model your work, please cite:\n\n\n\nEnjoy!" ]
[ 58, 182 ]
[ "passage: TAGS\n#transformers #pytorch #xlm-roberta #text-classification #sentiment-analysis #multilingual #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# Multi-lingual sentiment prediction trained from COVID19-related tweets\n\nRepository: URL\n\nModel trained on a large-scale (18437530 examples) dataset of \nmulti-lingual tweets that was collected between March 2020 \nand November 2021 using Twitter’s Streaming API with varying\nCOVID19-related keywords. Labels were auto-general based on \nthe presence of positive and negative emoticons. For details\non the dataset, see our IEEE BigData 2021 publication. \n\nBase model is sentence-transformers/stsb-xlm-r-multilingual.\nIt was finetuned for sequence classification with 'positive' \nand 'negative' labels for two epochs (48 hours on 8xP100 GPUs). \n\nIf you use our model your work, please cite:\n\n\n\nEnjoy!" ]
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# KGR10 FastText Polish word embeddings Distributional language model (both textual and binary) for Polish (word embeddings) trained on KGR10 corpus (over 4 billion of words) using Fasttext with the following variants (all possible combinations): - dimension: 100, 300 - method: skipgram, cbow - tool: FastText, Magnitude - source text: plain, plain.lower, plain.lemma, plain.lemma.lower ## Models In the repository you can find 4 selected models, that were examined in the paper (see Citation). A model that performed the best is the default model/config (see `default_config.json`). ## Usage To use these embedding models easily, it is required to install [embeddings](https://github.com/CLARIN-PL/embeddings). ```bash pip install clarinpl-embeddings ``` ### Utilising the default model (the easiest way) Word embedding: ```python from embeddings.embedding.auto_flair import AutoFlairWordEmbedding from flair.data import Sentence sentence = Sentence("Myśl z duszy leci bystro, Nim się w słowach złamie.") embedding = AutoFlairWordEmbedding.from_hub("clarin-pl/fastText-kgr10") embedding.embed([sentence]) for token in sentence: print(token) print(token.embedding) ``` Document embedding (averaged over words): ```python from embeddings.embedding.auto_flair import AutoFlairDocumentEmbedding from flair.data import Sentence sentence = Sentence("Myśl z duszy leci bystro, Nim się w słowach złamie.") embedding = AutoFlairDocumentEmbedding.from_hub("clarin-pl/fastText-kgr10") embedding.embed([sentence]) print(sentence.embedding) ``` ### Customisable way Word embedding: ```python from embeddings.embedding.static.embedding import AutoStaticWordEmbedding from embeddings.embedding.static.fasttext import KGR10FastTextConfig from flair.data import Sentence config = KGR10FastTextConfig(method='cbow', dimension=100) embedding = AutoStaticWordEmbedding.from_config(config) sentence = Sentence("Myśl z duszy leci bystro, Nim się w słowach złamie.") embedding.embed([sentence]) for token in sentence: print(token) print(token.embedding) ``` Document embedding (averaged over words): ```python from embeddings.embedding.static.embedding import AutoStaticDocumentEmbedding from embeddings.embedding.static.fasttext import KGR10FastTextConfig from flair.data import Sentence config = KGR10FastTextConfig(method='cbow', dimension=100) embedding = AutoStaticDocumentEmbedding.from_config(config) sentence = Sentence("Myśl z duszy leci bystro, Nim się w słowach złamie.") embedding.embed([sentence]) print(sentence.embedding) ``` ## Citation The link below leads to the NextCloud directory with all variants of embeddings. If you use it, please cite the following article: ``` @article{kocon2018embeddings, author = {Koco\'{n}, Jan and Gawor, Micha{\l}}, title = {Evaluating {KGR10} {P}olish word embeddings in the recognition of temporal expressions using {BiLSTM-CRF}}, journal = {Schedae Informaticae}, volume = {27}, year = {2018}, url = {http://www.ejournals.eu/Schedae-Informaticae/2018/Volume-27/art/13931/}, doi = {10.4467/20838476SI.18.008.10413} } ```
{"language": "pl", "tags": ["fastText"], "datasets": ["kgr10"]}
null
clarin-pl/fastText-kgr10
[ "fastText", "pl", "dataset:kgr10", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[ "pl" ]
TAGS #fastText #pl #dataset-kgr10 #region-us
# KGR10 FastText Polish word embeddings Distributional language model (both textual and binary) for Polish (word embeddings) trained on KGR10 corpus (over 4 billion of words) using Fasttext with the following variants (all possible combinations): - dimension: 100, 300 - method: skipgram, cbow - tool: FastText, Magnitude - source text: plain, URL, URL, URL ## Models In the repository you can find 4 selected models, that were examined in the paper (see Citation). A model that performed the best is the default model/config (see 'default_config.json'). ## Usage To use these embedding models easily, it is required to install embeddings. ### Utilising the default model (the easiest way) Word embedding: Document embedding (averaged over words): ### Customisable way Word embedding: Document embedding (averaged over words): The link below leads to the NextCloud directory with all variants of embeddings. If you use it, please cite the following article:
[ "# KGR10 FastText Polish word embeddings\n\nDistributional language model (both textual and binary) for Polish (word embeddings) trained on KGR10 corpus (over 4 billion of words) using Fasttext with the following variants (all possible combinations):\n- dimension: 100, 300\n- method: skipgram, cbow\n- tool: FastText, Magnitude\n- source text: plain, URL, URL, URL", "## Models\n\nIn the repository you can find 4 selected models, that were examined in the paper (see Citation). \nA model that performed the best is the default model/config (see 'default_config.json').", "## Usage\n\nTo use these embedding models easily, it is required to install embeddings.", "### Utilising the default model (the easiest way)\n\nWord embedding:\n\n\n\nDocument embedding (averaged over words):", "### Customisable way\n\nWord embedding:\n\n\n\nDocument embedding (averaged over words):\n\n\n\n\nThe link below leads to the NextCloud directory with all variants of embeddings. If you use it, please cite the following article:" ]
[ "TAGS\n#fastText #pl #dataset-kgr10 #region-us \n", "# KGR10 FastText Polish word embeddings\n\nDistributional language model (both textual and binary) for Polish (word embeddings) trained on KGR10 corpus (over 4 billion of words) using Fasttext with the following variants (all possible combinations):\n- dimension: 100, 300\n- method: skipgram, cbow\n- tool: FastText, Magnitude\n- source text: plain, URL, URL, URL", "## Models\n\nIn the repository you can find 4 selected models, that were examined in the paper (see Citation). \nA model that performed the best is the default model/config (see 'default_config.json').", "## Usage\n\nTo use these embedding models easily, it is required to install embeddings.", "### Utilising the default model (the easiest way)\n\nWord embedding:\n\n\n\nDocument embedding (averaged over words):", "### Customisable way\n\nWord embedding:\n\n\n\nDocument embedding (averaged over words):\n\n\n\n\nThe link below leads to the NextCloud directory with all variants of embeddings. If you use it, please cite the following article:" ]
[ 18, 99, 51, 22, 30, 54 ]
[ "passage: TAGS\n#fastText #pl #dataset-kgr10 #region-us \n# KGR10 FastText Polish word embeddings\n\nDistributional language model (both textual and binary) for Polish (word embeddings) trained on KGR10 corpus (over 4 billion of words) using Fasttext with the following variants (all possible combinations):\n- dimension: 100, 300\n- method: skipgram, cbow\n- tool: FastText, Magnitude\n- source text: plain, URL, URL, URL## Models\n\nIn the repository you can find 4 selected models, that were examined in the paper (see Citation). \nA model that performed the best is the default model/config (see 'default_config.json').## Usage\n\nTo use these embedding models easily, it is required to install embeddings.### Utilising the default model (the easiest way)\n\nWord embedding:\n\n\n\nDocument embedding (averaged over words):### Customisable way\n\nWord embedding:\n\n\n\nDocument embedding (averaged over words):\n\n\n\n\nThe link below leads to the NextCloud directory with all variants of embeddings. If you use it, please cite the following article:" ]
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null
null
transformers
# Work in Progress Polish RoBERTa The model has been trained for about 5% time of the target. We will publish new increments as they will be trained. The model pre-trained on KGR10 corpora. More about model at [CLARIN-dspace](https://huggingface.co/clarin/roberta-polish-v1) ## Usage ## Huggingface model hub ## Acknowledgments [CLARIN-PL and CLARIN-BIZ project](https://clarin-pl.eu/)
{}
fill-mask
clarin-pl/roberta-polish-kgr10
[ "transformers", "pytorch", "jax", "roberta", "fill-mask", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[]
TAGS #transformers #pytorch #jax #roberta #fill-mask #autotrain_compatible #endpoints_compatible #has_space #region-us
# Work in Progress Polish RoBERTa The model has been trained for about 5% time of the target. We will publish new increments as they will be trained. The model pre-trained on KGR10 corpora. More about model at CLARIN-dspace ## Usage ## Huggingface model hub ## Acknowledgments CLARIN-PL and CLARIN-BIZ project
[ "# Work in Progress Polish RoBERTa \n\nThe model has been trained for about 5% time of the target. We will publish new increments as they will be trained. \n\nThe model pre-trained on KGR10 corpora.\n\nMore about model at CLARIN-dspace", "## Usage", "## Huggingface model hub", "## Acknowledgments\n\nCLARIN-PL and CLARIN-BIZ project" ]
[ "TAGS\n#transformers #pytorch #jax #roberta #fill-mask #autotrain_compatible #endpoints_compatible #has_space #region-us \n", "# Work in Progress Polish RoBERTa \n\nThe model has been trained for about 5% time of the target. We will publish new increments as they will be trained. \n\nThe model pre-trained on KGR10 corpora.\n\nMore about model at CLARIN-dspace", "## Usage", "## Huggingface model hub", "## Acknowledgments\n\nCLARIN-PL and CLARIN-BIZ project" ]
[ 44, 59, 3, 6, 16 ]
[ "passage: TAGS\n#transformers #pytorch #jax #roberta #fill-mask #autotrain_compatible #endpoints_compatible #has_space #region-us \n# Work in Progress Polish RoBERTa \n\nThe model has been trained for about 5% time of the target. We will publish new increments as they will be trained. \n\nThe model pre-trained on KGR10 corpora.\n\nMore about model at CLARIN-dspace## Usage## Huggingface model hub## Acknowledgments\n\nCLARIN-PL and CLARIN-BIZ project" ]
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# KGR10 word2vec Polish word embeddings Distributional language models for Polish trained on the KGR10 corpora. ## Models In the repository you can find two selected models, that were selected after evaluation (see table below). A model that performed the best is the default model/config (see `default_config.json`). |method|dimension|hs|mwe|| |---|---|---|---| --- | |cbow|300|false|true| <-- default | |skipgram|300|true|true| ## Usage To use these embedding models easily, it is required to install [embeddings](https://github.com/CLARIN-PL/embeddings). ```bash pip install clarinpl-embeddings ``` ### Utilising the default model (the easiest way) Word embedding: ```python from embeddings.embedding.auto_flair import AutoFlairWordEmbedding from flair.data import Sentence sentence = Sentence("Myśl z duszy leci bystro, Nim się w słowach złamie.") embedding = AutoFlairWordEmbedding.from_hub("clarin-pl/word2vec-kgr10") embedding.embed([sentence]) for token in sentence: print(token) print(token.embedding) ``` Document embedding (averaged over words): ```python from embeddings.embedding.auto_flair import AutoFlairDocumentEmbedding from flair.data import Sentence sentence = Sentence("Myśl z duszy leci bystro, Nim się w słowach złamie.") embedding = AutoFlairDocumentEmbedding.from_hub("clarin-pl/word2vec-kgr10") embedding.embed([sentence]) print(sentence.embedding) ``` ### Customisable way Word embedding: ```python from embeddings.embedding.static.embedding import AutoStaticWordEmbedding from embeddings.embedding.static.word2vec import KGR10Word2VecConfig from flair.data import Sentence config = KGR10Word2VecConfig(method='skipgram', hs=False) embedding = AutoStaticWordEmbedding.from_config(config) sentence = Sentence("Myśl z duszy leci bystro, Nim się w słowach złamie.") embedding.embed([sentence]) for token in sentence: print(token) print(token.embedding) ``` Document embedding (averaged over words): ```python from embeddings.embedding.static.embedding import AutoStaticDocumentEmbedding from embeddings.embedding.static.word2vec import KGR10Word2VecConfig from flair.data import Sentence config = KGR10Word2VecConfig(method='skipgram', hs=False) embedding = AutoStaticDocumentEmbedding.from_config(config) sentence = Sentence("Myśl z duszy leci bystro, Nim się w słowach złamie.") embedding.embed([sentence]) print(sentence.embedding) ``` ## Citation ``` Piasecki, Maciej; Janz, Arkadiusz; Kaszewski, Dominik; et al., 2017, Word Embeddings for Polish, CLARIN-PL digital repository, http://hdl.handle.net/11321/442. ``` or ``` @misc{11321/442, title = {Word Embeddings for Polish}, author = {Piasecki, Maciej and Janz, Arkadiusz and Kaszewski, Dominik and Czachor, Gabriela}, url = {http://hdl.handle.net/11321/442}, note = {{CLARIN}-{PL} digital repository}, copyright = {{GNU} {GPL3}}, year = {2017} } ```
{"language": "pl", "tags": ["word2vec"], "datasets": ["KGR10"]}
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clarin-pl/word2vec-kgr10
[ "word2vec", "pl", "dataset:KGR10", "has_space", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[ "pl" ]
TAGS #word2vec #pl #dataset-KGR10 #has_space #region-us
KGR10 word2vec Polish word embeddings ===================================== Distributional language models for Polish trained on the KGR10 corpora. Models ------ In the repository you can find two selected models, that were selected after evaluation (see table below). A model that performed the best is the default model/config (see 'default\_config.json'). Usage ----- To use these embedding models easily, it is required to install embeddings. ### Utilising the default model (the easiest way) Word embedding: Document embedding (averaged over words): ### Customisable way Word embedding: Document embedding (averaged over words): or
[ "### Utilising the default model (the easiest way)\n\n\nWord embedding:\n\n\nDocument embedding (averaged over words):", "### Customisable way\n\n\nWord embedding:\n\n\nDocument embedding (averaged over words):\n\n\nor" ]
[ "TAGS\n#word2vec #pl #dataset-KGR10 #has_space #region-us \n", "### Utilising the default model (the easiest way)\n\n\nWord embedding:\n\n\nDocument embedding (averaged over words):", "### Customisable way\n\n\nWord embedding:\n\n\nDocument embedding (averaged over words):\n\n\nor" ]
[ 23, 30, 23 ]
[ "passage: TAGS\n#word2vec #pl #dataset-KGR10 #has_space #region-us \n### Utilising the default model (the easiest way)\n\n\nWord embedding:\n\n\nDocument embedding (averaged over words):### Customisable way\n\n\nWord embedding:\n\n\nDocument embedding (averaged over words):\n\n\nor" ]
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null
null
transformers
# bcms-bertic-frenk-hate Text classification model based on [`classla/bcms-bertic`](https://huggingface.co/classla/bcms-bertic) and fine-tuned on the [FRENK dataset](https://www.clarin.si/repository/xmlui/handle/11356/1433) comprising of LGBT and migrant hatespeech. Only the Croatian subset of the data was used for fine-tuning and the dataset has been relabeled for binary classification (offensive or acceptable). ## Fine-tuning hyperparameters Fine-tuning was performed with `simpletransformers`. Beforehand a brief hyperparameter optimisation was performed and the presumed optimal hyperparameters are: ```python model_args = { "num_train_epochs": 12, "learning_rate": 1e-5, "train_batch_size": 74} ``` ## Performance The same pipeline was run with two other transformer models and `fasttext` for comparison. Accuracy and macro F1 score were recorded for each of the 6 fine-tuning sessions and post festum analyzed. | model | average accuracy | average macro F1 | |----------------------------|------------------|------------------| | bcms-bertic-frenk-hate | 0.8313 | 0.8219 | | EMBEDDIA/crosloengual-bert | 0.8054 | 0.796 | | xlm-roberta-base | 0.7175 | 0.7049 | | fasttext | 0.771 | 0.754 | From recorded accuracies and macro F1 scores p-values were also calculated: Comparison with `crosloengual-bert`: | test | accuracy p-value | macro F1 p-value | |----------------|------------------|------------------| | Wilcoxon | 0.00781 | 0.00781 | | Mann Whithney | 0.00108 | 0.00108 | | Student t-test | 2.43e-10 | 1.27e-10 | Comparison with `xlm-roberta-base`: | test | accuracy p-value | macro F1 p-value | |----------------|------------------|------------------| | Wilcoxon | 0.00781 | 0.00781 | | Mann Whithney | 0.00107 | 0.00108 | | Student t-test | 4.83e-11 | 5.61e-11 | ## Use examples ```python from simpletransformers.classification import ClassificationModel model = ClassificationModel( "bert", "5roop/bcms-bertic-frenk-hate", use_cuda=True, ) predictions, logit_output = model.predict(['Ne odbacujem da će RH primiti još migranata iz Afganistana, no neće biti novog vala', "Potpredsjednik Vlade i ministar branitelja Tomo Medved komentirao je Vladine planove za zakonsku zabranu pozdrava 'za dom spremni' "]) predictions ### Output: ### array([0, 0]) ``` ## Citation If you use the model, please cite the following paper on which the original model is based: ``` @inproceedings{ljubesic-lauc-2021-bertic, title = "{BERT}i{\'c} - The Transformer Language Model for {B}osnian, {C}roatian, {M}ontenegrin and {S}erbian", author = "Ljube{\v{s}}i{\'c}, Nikola and Lauc, Davor", booktitle = "Proceedings of the 8th Workshop on Balto-Slavic Natural Language Processing", month = apr, year = "2021", address = "Kiyv, Ukraine", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2021.bsnlp-1.5", pages = "37--42", } ``` and the dataset used for fine-tuning: ``` @misc{ljubešić2019frenk, title={The FRENK Datasets of Socially Unacceptable Discourse in Slovene and English}, author={Nikola Ljubešić and Darja Fišer and Tomaž Erjavec}, year={2019}, eprint={1906.02045}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/1906.02045} } ```
{"language": "hr", "license": "cc-by-sa-4.0", "tags": ["text-classification", "hate-speech"], "widget": [{"text": "Potpredsjednik Vlade i ministar branitelja Tomo Medved komentirao je Vladine planove za zakonsku zabranu pozdrava 'za dom spremni'."}]}
text-classification
classla/bcms-bertic-frenk-hate
[ "transformers", "pytorch", "safetensors", "bert", "text-classification", "hate-speech", "hr", "arxiv:1906.02045", "license:cc-by-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[ "1906.02045" ]
[ "hr" ]
TAGS #transformers #pytorch #safetensors #bert #text-classification #hate-speech #hr #arxiv-1906.02045 #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
bcms-bertic-frenk-hate ====================== Text classification model based on 'classla/bcms-bertic' and fine-tuned on the FRENK dataset comprising of LGBT and migrant hatespeech. Only the Croatian subset of the data was used for fine-tuning and the dataset has been relabeled for binary classification (offensive or acceptable). Fine-tuning hyperparameters --------------------------- Fine-tuning was performed with 'simpletransformers'. Beforehand a brief hyperparameter optimisation was performed and the presumed optimal hyperparameters are: Performance ----------- The same pipeline was run with two other transformer models and 'fasttext' for comparison. Accuracy and macro F1 score were recorded for each of the 6 fine-tuning sessions and post festum analyzed. model: bcms-bertic-frenk-hate, average accuracy: 0.8313, average macro F1: 0.8219 model: EMBEDDIA/crosloengual-bert, average accuracy: 0.8054, average macro F1: 0.796 model: xlm-roberta-base, average accuracy: 0.7175, average macro F1: 0.7049 model: fasttext, average accuracy: 0.771, average macro F1: 0.754 From recorded accuracies and macro F1 scores p-values were also calculated: Comparison with 'crosloengual-bert': test: Wilcoxon, accuracy p-value: 0.00781, macro F1 p-value: 0.00781 test: Mann Whithney, accuracy p-value: 0.00108, macro F1 p-value: 0.00108 test: Student t-test, accuracy p-value: 2.43e-10, macro F1 p-value: 1.27e-10 Comparison with 'xlm-roberta-base': test: Wilcoxon, accuracy p-value: 0.00781, macro F1 p-value: 0.00781 test: Mann Whithney, accuracy p-value: 0.00107, macro F1 p-value: 0.00108 test: Student t-test, accuracy p-value: 4.83e-11, macro F1 p-value: 5.61e-11 Use examples ------------ If you use the model, please cite the following paper on which the original model is based: and the dataset used for fine-tuning:
[]
[ "TAGS\n#transformers #pytorch #safetensors #bert #text-classification #hate-speech #hr #arxiv-1906.02045 #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n" ]
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[ "passage: TAGS\n#transformers #pytorch #safetensors #bert #text-classification #hate-speech #hr #arxiv-1906.02045 #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n" ]
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null
null
transformers
# BERTić&ast; [bert-ich] /bɜrtitʃ/ - A transformer language model for Bosnian, Croatian, Montenegrin and Serbian &ast; The name should resemble the facts (1) that the model was trained in Zagreb, Croatia, where diminutives ending in -ić (as in fotić, smajlić, hengić etc.) are very popular, and (2) that most surnames in the countries where these languages are spoken end in -ić (with diminutive etymology as well). This is the smaller generator of the main [discriminator model](https://huggingface.co/classla/bcms-bertic), useful if you want to continue pre-training the discriminator model. If you use the model, please cite the following paper: ``` @inproceedings{ljubesic-lauc-2021-bertic, title = "{BERT}i{\'c} - The Transformer Language Model for {B}osnian, {C}roatian, {M}ontenegrin and {S}erbian", author = "Ljube{\v{s}}i{\'c}, Nikola and Lauc, Davor", booktitle = "Proceedings of the 8th Workshop on Balto-Slavic Natural Language Processing", month = apr, year = "2021", address = "Kiyv, Ukraine", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2021.bsnlp-1.5", pages = "37--42", } ```
{"language": ["hr", "bs", "sr", "cnr", "hbs"], "license": "apache-2.0", "tags": ["masked-lm"], "widget": [{"text": "Zovem se Marko i radim u [MASK]."}]}
null
classla/bcms-bertic-generator
[ "transformers", "pytorch", "electra", "pretraining", "masked-lm", "hr", "bs", "sr", "cnr", "hbs", "license:apache-2.0", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[ "hr", "bs", "sr", "cnr", "hbs" ]
TAGS #transformers #pytorch #electra #pretraining #masked-lm #hr #bs #sr #cnr #hbs #license-apache-2.0 #endpoints_compatible #region-us
# BERTić&ast; [bert-ich] /bɜrtitʃ/ - A transformer language model for Bosnian, Croatian, Montenegrin and Serbian &ast; The name should resemble the facts (1) that the model was trained in Zagreb, Croatia, where diminutives ending in -ić (as in fotić, smajlić, hengić etc.) are very popular, and (2) that most surnames in the countries where these languages are spoken end in -ić (with diminutive etymology as well). This is the smaller generator of the main discriminator model, useful if you want to continue pre-training the discriminator model. If you use the model, please cite the following paper:
[ "# BERTić&ast; [bert-ich] /bɜrtitʃ/ - A transformer language model for Bosnian, Croatian, Montenegrin and Serbian\n\n&ast; The name should resemble the facts (1) that the model was trained in Zagreb, Croatia, where diminutives ending in -ić (as in fotić, smajlić, hengić etc.) are very popular, and (2) that most surnames in the countries where these languages are spoken end in -ić (with diminutive etymology as well).\n\nThis is the smaller generator of the main discriminator model, useful if you want to continue pre-training the discriminator model.\n\nIf you use the model, please cite the following paper:" ]
[ "TAGS\n#transformers #pytorch #electra #pretraining #masked-lm #hr #bs #sr #cnr #hbs #license-apache-2.0 #endpoints_compatible #region-us \n", "# BERTić&ast; [bert-ich] /bɜrtitʃ/ - A transformer language model for Bosnian, Croatian, Montenegrin and Serbian\n\n&ast; The name should resemble the facts (1) that the model was trained in Zagreb, Croatia, where diminutives ending in -ić (as in fotić, smajlić, hengić etc.) are very popular, and (2) that most surnames in the countries where these languages are spoken end in -ić (with diminutive etymology as well).\n\nThis is the smaller generator of the main discriminator model, useful if you want to continue pre-training the discriminator model.\n\nIf you use the model, please cite the following paper:" ]
[ 53, 159 ]
[ "passage: TAGS\n#transformers #pytorch #electra #pretraining #masked-lm #hr #bs #sr #cnr #hbs #license-apache-2.0 #endpoints_compatible #region-us \n# BERTić&ast; [bert-ich] /bɜrtitʃ/ - A transformer language model for Bosnian, Croatian, Montenegrin and Serbian\n\n&ast; The name should resemble the facts (1) that the model was trained in Zagreb, Croatia, where diminutives ending in -ić (as in fotić, smajlić, hengić etc.) are very popular, and (2) that most surnames in the countries where these languages are spoken end in -ić (with diminutive etymology as well).\n\nThis is the smaller generator of the main discriminator model, useful if you want to continue pre-training the discriminator model.\n\nIf you use the model, please cite the following paper:" ]
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null
null
transformers
# The [BERTić](https://huggingface.co/classla/bcms-bertic)&ast; [bert-ich] /bɜrtitʃ/ model fine-tuned for the task of named entity recognition in Bosnian, Croatian, Montenegrin and Serbian (BCMS) &ast; The name should resemble the facts (1) that the model was trained in Zagreb, Croatia, where diminutives ending in -ić (as in fotić, smajlić, hengić etc.) are very popular, and (2) that most surnames in the countries where these languages are spoken end in -ić (with diminutive etymology as well). This is a fine-tuned version of the [BERTić](https://huggingface.co/classla/bcms-bertic) model for the task of named entity recognition (PER, LOC, ORG, MISC). The fine-tuning was performed on the following datasets: - the [hr500k](http://hdl.handle.net/11356/1183) dataset, 500 thousand tokens in size, standard Croatian - the [SETimes.SR](http://hdl.handle.net/11356/1200) dataset, 87 thousand tokens in size, standard Serbian - the [ReLDI-hr](http://hdl.handle.net/11356/1241) dataset, 89 thousand tokens in size, Internet (Twitter) Croatian - the [ReLDI-sr](http://hdl.handle.net/11356/1240) dataset, 92 thousand tokens in size, Internet (Twitter) Serbian The data was augmented with missing diacritics and standard data was additionally over-represented. The F1 obtained on dev data (train and test was merged into train) is 91.38. For a more detailed per-dataset evaluation of the BERTić model on the NER task have a look at the [main model page](https://huggingface.co/classla/bcms-bertic). If you use this fine-tuned model, please cite the following paper: ``` @inproceedings{ljubesic-lauc-2021-bertic, title = "{BERT}i{\'c} - The Transformer Language Model for {B}osnian, {C}roatian, {M}ontenegrin and {S}erbian", author = "Ljube{\v{s}}i{\'c}, Nikola and Lauc, Davor", booktitle = "Proceedings of the 8th Workshop on Balto-Slavic Natural Language Processing", month = apr, year = "2021", address = "Kiyv, Ukraine", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2021.bsnlp-1.5", pages = "37--42", } ``` When running the model in `simpletransformers`, the order of labels has to be set as well. ``` from simpletransformers.ner import NERModel, NERArgs model_args = NERArgs() model_args.labels_list = ['B-LOC','B-MISC','B-ORG','B-PER','I-LOC','I-MISC','I-ORG','I-PER','O'] model = NERModel('electra', 'classla/bcms-bertic-ner', args=model_args) ```
{"language": ["hr", "bs", "sr", "cnr", "hbs"], "license": "apache-2.0", "widget": [{"text": "Zovem se Marko i \u017eivim u Zagrebu. Studirao sam u Beogradu na Filozofskom fakultetu. Obo\u017eavam album Moanin."}]}
token-classification
classla/bcms-bertic-ner
[ "transformers", "pytorch", "safetensors", "electra", "token-classification", "hr", "bs", "sr", "cnr", "hbs", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[ "hr", "bs", "sr", "cnr", "hbs" ]
TAGS #transformers #pytorch #safetensors #electra #token-classification #hr #bs #sr #cnr #hbs #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
# The BERTić&ast; [bert-ich] /bɜrtitʃ/ model fine-tuned for the task of named entity recognition in Bosnian, Croatian, Montenegrin and Serbian (BCMS) &ast; The name should resemble the facts (1) that the model was trained in Zagreb, Croatia, where diminutives ending in -ić (as in fotić, smajlić, hengić etc.) are very popular, and (2) that most surnames in the countries where these languages are spoken end in -ić (with diminutive etymology as well). This is a fine-tuned version of the BERTić model for the task of named entity recognition (PER, LOC, ORG, MISC). The fine-tuning was performed on the following datasets: - the hr500k dataset, 500 thousand tokens in size, standard Croatian - the SETimes.SR dataset, 87 thousand tokens in size, standard Serbian - the ReLDI-hr dataset, 89 thousand tokens in size, Internet (Twitter) Croatian - the ReLDI-sr dataset, 92 thousand tokens in size, Internet (Twitter) Serbian The data was augmented with missing diacritics and standard data was additionally over-represented. The F1 obtained on dev data (train and test was merged into train) is 91.38. For a more detailed per-dataset evaluation of the BERTić model on the NER task have a look at the main model page. If you use this fine-tuned model, please cite the following paper: When running the model in 'simpletransformers', the order of labels has to be set as well.
[ "# The BERTić&ast; [bert-ich] /bɜrtitʃ/ model fine-tuned for the task of named entity recognition in Bosnian, Croatian, Montenegrin and Serbian (BCMS)\n\n&ast; The name should resemble the facts (1) that the model was trained in Zagreb, Croatia, where diminutives ending in -ić (as in fotić, smajlić, hengić etc.) are very popular, and (2) that most surnames in the countries where these languages are spoken end in -ić (with diminutive etymology as well).\n\nThis is a fine-tuned version of the BERTić model for the task of named entity recognition (PER, LOC, ORG, MISC). The fine-tuning was performed on the following datasets:\n\n- the hr500k dataset, 500 thousand tokens in size, standard Croatian\n- the SETimes.SR dataset, 87 thousand tokens in size, standard Serbian\n- the ReLDI-hr dataset, 89 thousand tokens in size, Internet (Twitter) Croatian\n- the ReLDI-sr dataset, 92 thousand tokens in size, Internet (Twitter) Serbian\n\nThe data was augmented with missing diacritics and standard data was additionally over-represented. The F1 obtained on dev data (train and test was merged into train) is 91.38. For a more detailed per-dataset evaluation of the BERTić model on the NER task have a look at the main model page.\n\nIf you use this fine-tuned model, please cite the following paper:\n\n\n\nWhen running the model in 'simpletransformers', the order of labels has to be set as well." ]
[ "TAGS\n#transformers #pytorch #safetensors #electra #token-classification #hr #bs #sr #cnr #hbs #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n", "# The BERTić&ast; [bert-ich] /bɜrtitʃ/ model fine-tuned for the task of named entity recognition in Bosnian, Croatian, Montenegrin and Serbian (BCMS)\n\n&ast; The name should resemble the facts (1) that the model was trained in Zagreb, Croatia, where diminutives ending in -ić (as in fotić, smajlić, hengić etc.) are very popular, and (2) that most surnames in the countries where these languages are spoken end in -ić (with diminutive etymology as well).\n\nThis is a fine-tuned version of the BERTić model for the task of named entity recognition (PER, LOC, ORG, MISC). The fine-tuning was performed on the following datasets:\n\n- the hr500k dataset, 500 thousand tokens in size, standard Croatian\n- the SETimes.SR dataset, 87 thousand tokens in size, standard Serbian\n- the ReLDI-hr dataset, 89 thousand tokens in size, Internet (Twitter) Croatian\n- the ReLDI-sr dataset, 92 thousand tokens in size, Internet (Twitter) Serbian\n\nThe data was augmented with missing diacritics and standard data was additionally over-represented. The F1 obtained on dev data (train and test was merged into train) is 91.38. For a more detailed per-dataset evaluation of the BERTić model on the NER task have a look at the main model page.\n\nIf you use this fine-tuned model, please cite the following paper:\n\n\n\nWhen running the model in 'simpletransformers', the order of labels has to be set as well." ]
[ 68, 384 ]
[ "passage: TAGS\n#transformers #pytorch #safetensors #electra #token-classification #hr #bs #sr #cnr #hbs #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n# The BERTić&ast; [bert-ich] /bɜrtitʃ/ model fine-tuned for the task of named entity recognition in Bosnian, Croatian, Montenegrin and Serbian (BCMS)\n\n&ast; The name should resemble the facts (1) that the model was trained in Zagreb, Croatia, where diminutives ending in -ić (as in fotić, smajlić, hengić etc.) are very popular, and (2) that most surnames in the countries where these languages are spoken end in -ić (with diminutive etymology as well).\n\nThis is a fine-tuned version of the BERTić model for the task of named entity recognition (PER, LOC, ORG, MISC). The fine-tuning was performed on the following datasets:\n\n- the hr500k dataset, 500 thousand tokens in size, standard Croatian\n- the SETimes.SR dataset, 87 thousand tokens in size, standard Serbian\n- the ReLDI-hr dataset, 89 thousand tokens in size, Internet (Twitter) Croatian\n- the ReLDI-sr dataset, 92 thousand tokens in size, Internet (Twitter) Serbian\n\nThe data was augmented with missing diacritics and standard data was additionally over-represented. The F1 obtained on dev data (train and test was merged into train) is 91.38. For a more detailed per-dataset evaluation of the BERTić model on the NER task have a look at the main model page.\n\nIf you use this fine-tuned model, please cite the following paper:\n\n\n\nWhen running the model in 'simpletransformers', the order of labels has to be set as well." ]
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null
null
transformers
# BERTić&ast; [bert-ich] /bɜrtitʃ/ - A transformer language model for Bosnian, Croatian, Montenegrin and Serbian &ast; The name should resemble the facts (1) that the model was trained in Zagreb, Croatia, where diminutives ending in -ić (as in fotić, smajlić, hengić etc.) are very popular, and (2) that most surnames in the countries where these languages are spoken end in -ić (with diminutive etymology as well). This Electra model was trained on more than 8 billion tokens of Bosnian, Croatian, Montenegrin and Serbian text. **&ast;new&ast;** We have published a version of this model fine-tuned on the named entity recognition task ([bcms-bertic-ner](https://huggingface.co/classla/bcms-bertic-ner)) and on the hate speech detection task ([bcms-bertic-frenk-hate](https://huggingface.co/classla/bcms-bertic-frenk-hate)). If you use the model, please cite the following paper: ``` @inproceedings{ljubesic-lauc-2021-bertic, title = "{BERT}i{\'c} - The Transformer Language Model for {B}osnian, {C}roatian, {M}ontenegrin and {S}erbian", author = "Ljube{\v{s}}i{\'c}, Nikola and Lauc, Davor", booktitle = "Proceedings of the 8th Workshop on Balto-Slavic Natural Language Processing", month = apr, year = "2021", address = "Kiyv, Ukraine", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2021.bsnlp-1.5", pages = "37--42", } ``` ## Benchmarking Comparing this model to [multilingual BERT](https://huggingface.co/bert-base-multilingual-cased) and [CroSloEngual BERT](https://huggingface.co/EMBEDDIA/crosloengual-bert) on the tasks of (1) part-of-speech tagging, (2) named entity recognition, (3) geolocation prediction, and (4) commonsense causal reasoning, shows the BERTić model to be superior to the other two. ### Part-of-speech tagging Evaluation metric is (seqeval) microF1. Reported are means of five runs. Best results are presented in bold. Statistical significance is calculated between two best-performing systems via a two-tailed t-test (&ast; p<=0.05, &ast;&ast; p<=0.01, &ast;&ast;&ast; p<=0.001, &ast;&ast;&ast;&ast;&ast; p<=0.0001). Dataset | Language | Variety | CLASSLA | mBERT | cseBERT | BERTić ---|---|---|---|---|---|--- hr500k | Croatian | standard | 93.87 | 94.60 | 95.74 | **95.81&ast;&ast;&ast;** reldi-hr | Croatian | internet non-standard | - | 88.87 | 91.63 | **92.28&ast;&ast;&ast;** SETimes.SR | Serbian | standard | 95.00 | 95.50 | **96.41** | 96.31 reldi-sr | Serbian | internet non-standard | - | 91.26 | 93.54 | **93.90&ast;&ast;&ast;** ### Named entity recognition Evaluation metric is (seqeval) microF1. Reported are means of five runs. Best results are presented in bold. Statistical significance is calculated between two best-performing systems via a two-tailed t-test (&ast; p<=0.05, &ast;&ast; p<=0.01, &ast;&ast;&ast; p<=0.001, &ast;&ast;&ast;&ast;&ast; p<=0.0001). Dataset | Language | Variety | CLASSLA | mBERT | cseBERT | BERTić ---|---|---|---|---|---|--- hr500k | Croatian | standard | 80.13 | 85.67 | 88.98 | **89.21&ast;&ast;&ast;&ast;** reldi-hr | Croatian | internet non-standard | - | 76.06 | 81.38 | **83.05&ast;&ast;&ast;&ast;** SETimes.SR | Serbian | standard | 84.64 | **92.41** | 92.28 | 92.02 reldi-sr | Serbian | internet non-standard | - | 81.29 | 82.76 | **87.92&ast;&ast;&ast;&ast;** ### Geolocation prediction The dataset comes from the VarDial 2020 evaluation campaign's shared task on [Social Media variety Geolocation prediction](https://sites.google.com/view/vardial2020/evaluation-campaign). The task is to predict the latitude and longitude of a tweet given its text. Evaluation metrics are median and mean of distance between gold and predicted geolocations (lower is better). No statistical significance is computed due to large test set (39,723 instances). Centroid baseline predicts each text to be created in the centroid of the training dataset. System | Median | Mean ---|---|--- centroid | 107.10 | 145.72 mBERT | 42.25 | 82.05 cseBERT | 40.76 | 81.88 BERTić | **37.96** | **79.30** ### Choice Of Plausible Alternatives The dataset is a translation of the [COPA dataset](https://people.ict.usc.edu/~gordon/copa.html) into Croatian ([link to the dataset](http://hdl.handle.net/11356/1404)). Evaluation metric is accuracy. Reported are means of five runs. Best results are presented in bold. Statistical significance is calculated between two best-performing systems via a two-tailed t-test (&ast; p<=0.05, &ast;&ast; p<=0.01, &ast;&ast;&ast; p<=0.001, &ast;&ast;&ast;&ast;&ast; p<=0.0001). System | Accuracy ---|--- random | 50.00 mBERT | 54.12 cseBERT | 61.80 BERTić | **65.76&ast;&ast;**
{"language": ["hr", "bs", "sr", "cnr", "hbs"], "license": "apache-2.0"}
null
classla/bcms-bertic
[ "transformers", "pytorch", "electra", "pretraining", "hr", "bs", "sr", "cnr", "hbs", "license:apache-2.0", "endpoints_compatible", "has_space", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[ "hr", "bs", "sr", "cnr", "hbs" ]
TAGS #transformers #pytorch #electra #pretraining #hr #bs #sr #cnr #hbs #license-apache-2.0 #endpoints_compatible #has_space #region-us
BERTić\* [bert-ich] /bɜrtitʃ/ - A transformer language model for Bosnian, Croatian, Montenegrin and Serbian =========================================================================================================== \* The name should resemble the facts (1) that the model was trained in Zagreb, Croatia, where diminutives ending in -ić (as in fotić, smajlić, hengić etc.) are very popular, and (2) that most surnames in the countries where these languages are spoken end in -ić (with diminutive etymology as well). This Electra model was trained on more than 8 billion tokens of Bosnian, Croatian, Montenegrin and Serbian text. \*new\* We have published a version of this model fine-tuned on the named entity recognition task (bcms-bertic-ner) and on the hate speech detection task (bcms-bertic-frenk-hate). If you use the model, please cite the following paper: Benchmarking ------------ Comparing this model to multilingual BERT and CroSloEngual BERT on the tasks of (1) part-of-speech tagging, (2) named entity recognition, (3) geolocation prediction, and (4) commonsense causal reasoning, shows the BERTić model to be superior to the other two. ### Part-of-speech tagging Evaluation metric is (seqeval) microF1. Reported are means of five runs. Best results are presented in bold. Statistical significance is calculated between two best-performing systems via a two-tailed t-test (\* p<=0.05, \*\* p<=0.01, \*\*\* p<=0.001, \*\*\*\*\* p<=0.0001). ### Named entity recognition Evaluation metric is (seqeval) microF1. Reported are means of five runs. Best results are presented in bold. Statistical significance is calculated between two best-performing systems via a two-tailed t-test (\* p<=0.05, \*\* p<=0.01, \*\*\* p<=0.001, \*\*\*\*\* p<=0.0001). ### Geolocation prediction The dataset comes from the VarDial 2020 evaluation campaign's shared task on Social Media variety Geolocation prediction. The task is to predict the latitude and longitude of a tweet given its text. Evaluation metrics are median and mean of distance between gold and predicted geolocations (lower is better). No statistical significance is computed due to large test set (39,723 instances). Centroid baseline predicts each text to be created in the centroid of the training dataset. System: centroid, Median: 107.10, Mean: 145.72 System: mBERT, Median: 42.25, Mean: 82.05 System: cseBERT, Median: 40.76, Mean: 81.88 System: BERTić, Median: 37.96, Mean: 79.30 ### Choice Of Plausible Alternatives The dataset is a translation of the COPA dataset into Croatian (link to the dataset). Evaluation metric is accuracy. Reported are means of five runs. Best results are presented in bold. Statistical significance is calculated between two best-performing systems via a two-tailed t-test (\* p<=0.05, \*\* p<=0.01, \*\*\* p<=0.001, \*\*\*\*\* p<=0.0001).
[ "### Part-of-speech tagging\n\n\nEvaluation metric is (seqeval) microF1. Reported are means of five runs. Best results are presented in bold. Statistical significance is calculated between two best-performing systems via a two-tailed t-test (\\* p<=0.05, \\*\\* p<=0.01, \\*\\*\\* p<=0.001, \\*\\*\\*\\*\\* p<=0.0001).", "### Named entity recognition\n\n\nEvaluation metric is (seqeval) microF1. Reported are means of five runs. Best results are presented in bold. Statistical significance is calculated between two best-performing systems via a two-tailed t-test (\\* p<=0.05, \\*\\* p<=0.01, \\*\\*\\* p<=0.001, \\*\\*\\*\\*\\* p<=0.0001).", "### Geolocation prediction\n\n\nThe dataset comes from the VarDial 2020 evaluation campaign's shared task on Social Media variety Geolocation prediction. The task is to predict the latitude and longitude of a tweet given its text.\n\n\nEvaluation metrics are median and mean of distance between gold and predicted geolocations (lower is better). No statistical significance is computed due to large test set (39,723 instances). Centroid baseline predicts each text to be created in the centroid of the training dataset.\n\n\nSystem: centroid, Median: 107.10, Mean: 145.72\nSystem: mBERT, Median: 42.25, Mean: 82.05\nSystem: cseBERT, Median: 40.76, Mean: 81.88\nSystem: BERTić, Median: 37.96, Mean: 79.30", "### Choice Of Plausible Alternatives\n\n\nThe dataset is a translation of the COPA dataset into Croatian (link to the dataset).\n\n\nEvaluation metric is accuracy. Reported are means of five runs. Best results are presented in bold. Statistical significance is calculated between two best-performing systems via a two-tailed t-test (\\* p<=0.05, \\*\\* p<=0.01, \\*\\*\\* p<=0.001, \\*\\*\\*\\*\\* p<=0.0001)." ]
[ "TAGS\n#transformers #pytorch #electra #pretraining #hr #bs #sr #cnr #hbs #license-apache-2.0 #endpoints_compatible #has_space #region-us \n", "### Part-of-speech tagging\n\n\nEvaluation metric is (seqeval) microF1. Reported are means of five runs. Best results are presented in bold. Statistical significance is calculated between two best-performing systems via a two-tailed t-test (\\* p<=0.05, \\*\\* p<=0.01, \\*\\*\\* p<=0.001, \\*\\*\\*\\*\\* p<=0.0001).", "### Named entity recognition\n\n\nEvaluation metric is (seqeval) microF1. Reported are means of five runs. Best results are presented in bold. Statistical significance is calculated between two best-performing systems via a two-tailed t-test (\\* p<=0.05, \\*\\* p<=0.01, \\*\\*\\* p<=0.001, \\*\\*\\*\\*\\* p<=0.0001).", "### Geolocation prediction\n\n\nThe dataset comes from the VarDial 2020 evaluation campaign's shared task on Social Media variety Geolocation prediction. The task is to predict the latitude and longitude of a tweet given its text.\n\n\nEvaluation metrics are median and mean of distance between gold and predicted geolocations (lower is better). No statistical significance is computed due to large test set (39,723 instances). Centroid baseline predicts each text to be created in the centroid of the training dataset.\n\n\nSystem: centroid, Median: 107.10, Mean: 145.72\nSystem: mBERT, Median: 42.25, Mean: 82.05\nSystem: cseBERT, Median: 40.76, Mean: 81.88\nSystem: BERTić, Median: 37.96, Mean: 79.30", "### Choice Of Plausible Alternatives\n\n\nThe dataset is a translation of the COPA dataset into Croatian (link to the dataset).\n\n\nEvaluation metric is accuracy. Reported are means of five runs. Best results are presented in bold. Statistical significance is calculated between two best-performing systems via a two-tailed t-test (\\* p<=0.05, \\*\\* p<=0.01, \\*\\*\\* p<=0.001, \\*\\*\\*\\*\\* p<=0.0001)." ]
[ 52, 114, 111, 192, 130 ]
[ "passage: TAGS\n#transformers #pytorch #electra #pretraining #hr #bs #sr #cnr #hbs #license-apache-2.0 #endpoints_compatible #has_space #region-us \n### Part-of-speech tagging\n\n\nEvaluation metric is (seqeval) microF1. Reported are means of five runs. Best results are presented in bold. Statistical significance is calculated between two best-performing systems via a two-tailed t-test (\\* p<=0.05, \\*\\* p<=0.01, \\*\\*\\* p<=0.001, \\*\\*\\*\\*\\* p<=0.0001).### Named entity recognition\n\n\nEvaluation metric is (seqeval) microF1. Reported are means of five runs. Best results are presented in bold. Statistical significance is calculated between two best-performing systems via a two-tailed t-test (\\* p<=0.05, \\*\\* p<=0.01, \\*\\*\\* p<=0.001, \\*\\*\\*\\*\\* p<=0.0001).### Geolocation prediction\n\n\nThe dataset comes from the VarDial 2020 evaluation campaign's shared task on Social Media variety Geolocation prediction. The task is to predict the latitude and longitude of a tweet given its text.\n\n\nEvaluation metrics are median and mean of distance between gold and predicted geolocations (lower is better). No statistical significance is computed due to large test set (39,723 instances). Centroid baseline predicts each text to be created in the centroid of the training dataset.\n\n\nSystem: centroid, Median: 107.10, Mean: 145.72\nSystem: mBERT, Median: 42.25, Mean: 82.05\nSystem: cseBERT, Median: 40.76, Mean: 81.88\nSystem: BERTić, Median: 37.96, Mean: 79.30" ]
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null
transformers
# roberta-base-frenk-hate Text classification model based on [`roberta-base`](https://huggingface.co/roberta-base) and fine-tuned on the [FRENK dataset](https://www.clarin.si/repository/xmlui/handle/11356/1433) comprising of LGBT and migrant hatespeech. Only the English subset of the data was used for fine-tuning and the dataset has been relabeled for binary classification (offensive or acceptable). ## Fine-tuning hyperparameters Fine-tuning was performed with `simpletransformers`. Beforehand a brief hyperparameter optimisation was performed and the presumed optimal hyperparameters are: ```python model_args = { "num_train_epochs": 6, "learning_rate": 3e-6, "train_batch_size": 69} ``` ## Performance The same pipeline was run with two other transformer models and `fasttext` for comparison. Accuracy and macro F1 score were recorded for each of the 6 fine-tuning sessions and post festum analyzed. | model | average accuracy | average macro F1| |---|---|---| |roberta-base-frenk-hate|0.7915|0.7785| |xlm-roberta-large |0.7904|0.77876| |xlm-roberta-base |0.7577|0.7402| |fasttext|0.725 |0.707 | From recorded accuracies and macro F1 scores p-values were also calculated: Comparison with `xlm-roberta-base`: | test | accuracy p-value | macro F1 p-value| | --- | --- | --- | |Wilcoxon|0.00781|0.00781| |Mann Whithney U-test|0.00108|0.00108| |Student t-test | 1.35e-08 | 1.05e-07| Comparison with `xlm-roberta-large` yielded inconclusive results. `roberta-base` has average accuracy 0.7915, while `xlm-roberta-large` has average accuracy of 0.7904. If macro F1 scores were to be compared, `roberta-base` actually has lower average than `xlm-roberta-large`: 0.77852 vs 0.77876 respectively. The same statistical tests were performed with the premise that `roberta-base` has greater metrics, and the results are given below. | test | accuracy p-value | macro F1 p-value| | --- | --- | --- | |Wilcoxon|0.188|0.406| |Mann Whithey|0.375|0.649| |Student t-test | 0.681| 0.934| With reversed premise (i.e., that `xlm-roberta-large` has greater statistics) the Wilcoxon p-value for macro F1 scores for this case reaches 0.656, Mann-Whithey p-value is 0.399, and of course the Student p-value stays the same. It was therefore concluded that performance of the two models are not statistically significantly different from one another. ## Use examples ```python from simpletransformers.classification import ClassificationModel model_args = { "num_train_epochs": 6, "learning_rate": 3e-6, "train_batch_size": 69} model = ClassificationModel( "roberta", "5roop/roberta-base-frenk-hate", use_cuda=True, args=model_args ) predictions, logit_output = model.predict(["Build the wall", "Build the wall of trust"] ) predictions ### Output: ### array([1, 0]) ``` ## Citation If you use the model, please cite the following paper on which the original model is based: ``` @article{DBLP:journals/corr/abs-1907-11692, author = {Yinhan Liu and Myle Ott and Naman Goyal and Jingfei Du and Mandar Joshi and Danqi Chen and Omer Levy and Mike Lewis and Luke Zettlemoyer and Veselin Stoyanov}, title = {RoBERTa: {A} Robustly Optimized {BERT} Pretraining Approach}, journal = {CoRR}, volume = {abs/1907.11692}, year = {2019}, url = {http://arxiv.org/abs/1907.11692}, archivePrefix = {arXiv}, eprint = {1907.11692}, timestamp = {Thu, 01 Aug 2019 08:59:33 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-1907-11692.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ``` and the dataset used for fine-tuning: ``` @misc{ljubešić2019frenk, title={The FRENK Datasets of Socially Unacceptable Discourse in Slovene and English}, author={Nikola Ljubešić and Darja Fišer and Tomaž Erjavec}, year={2019}, eprint={1906.02045}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/1906.02045} } ```
{"language": "en", "license": "cc-by-sa-4.0", "tags": ["text-classification", "hate-speech"], "widget": [{"text": "Gay is okay."}]}
text-classification
classla/roberta-base-frenk-hate
[ "transformers", "pytorch", "safetensors", "roberta", "text-classification", "hate-speech", "en", "arxiv:1907.11692", "arxiv:1906.02045", "license:cc-by-sa-4.0", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]
2022-03-02T23:29:05+00:00
[ "1907.11692", "1906.02045" ]
[ "en" ]
TAGS #transformers #pytorch #safetensors #roberta #text-classification #hate-speech #en #arxiv-1907.11692 #arxiv-1906.02045 #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
roberta-base-frenk-hate ======================= Text classification model based on 'roberta-base' and fine-tuned on the FRENK dataset comprising of LGBT and migrant hatespeech. Only the English subset of the data was used for fine-tuning and the dataset has been relabeled for binary classification (offensive or acceptable). Fine-tuning hyperparameters --------------------------- Fine-tuning was performed with 'simpletransformers'. Beforehand a brief hyperparameter optimisation was performed and the presumed optimal hyperparameters are: Performance ----------- The same pipeline was run with two other transformer models and 'fasttext' for comparison. Accuracy and macro F1 score were recorded for each of the 6 fine-tuning sessions and post festum analyzed. model: roberta-base-frenk-hate, average accuracy: 0.7915, average macro F1: 0.7785 model: xlm-roberta-large, average accuracy: 0.7904, average macro F1: 0.77876 model: xlm-roberta-base, average accuracy: 0.7577, average macro F1: 0.7402 model: fasttext, average accuracy: 0.725, average macro F1: 0.707 From recorded accuracies and macro F1 scores p-values were also calculated: Comparison with 'xlm-roberta-base': test: Wilcoxon, accuracy p-value: 0.00781, macro F1 p-value: 0.00781 test: Mann Whithney U-test, accuracy p-value: 0.00108, macro F1 p-value: 0.00108 test: Student t-test, accuracy p-value: 1.35e-08, macro F1 p-value: 1.05e-07 Comparison with 'xlm-roberta-large' yielded inconclusive results. 'roberta-base' has average accuracy 0.7915, while 'xlm-roberta-large' has average accuracy of 0.7904. If macro F1 scores were to be compared, 'roberta-base' actually has lower average than 'xlm-roberta-large': 0.77852 vs 0.77876 respectively. The same statistical tests were performed with the premise that 'roberta-base' has greater metrics, and the results are given below. test: Wilcoxon, accuracy p-value: 0.188, macro F1 p-value: 0.406 test: Mann Whithey, accuracy p-value: 0.375, macro F1 p-value: 0.649 test: Student t-test, accuracy p-value: 0.681, macro F1 p-value: 0.934 With reversed premise (i.e., that 'xlm-roberta-large' has greater statistics) the Wilcoxon p-value for macro F1 scores for this case reaches 0.656, Mann-Whithey p-value is 0.399, and of course the Student p-value stays the same. It was therefore concluded that performance of the two models are not statistically significantly different from one another. Use examples ------------ If you use the model, please cite the following paper on which the original model is based: and the dataset used for fine-tuning:
[]
[ "TAGS\n#transformers #pytorch #safetensors #roberta #text-classification #hate-speech #en #arxiv-1907.11692 #arxiv-1906.02045 #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n" ]
[ 81 ]
[ "passage: TAGS\n#transformers #pytorch #safetensors #roberta #text-classification #hate-speech #en #arxiv-1907.11692 #arxiv-1906.02045 #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n" ]
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null
null
transformers
Text classification model based on `EMBEDDIA/sloberta` and fine-tuned on the [FRENK dataset](https://www.clarin.si/repository/xmlui/handle/11356/1433) comprising of LGBT and migrant hatespeech. Only the slovenian subset of the data was used for fine-tuning and the dataset has been relabeled for binary classification (offensive or acceptable). ## Fine-tuning hyperparameters Fine-tuning was performed with `simpletransformers`. Beforehand a brief hyperparameter optimisation was performed and the presumed optimal hyperparameters are: ```python model_args = { "num_train_epochs": 14, "learning_rate": 1e-5, "train_batch_size": 21, } ``` ## Performance The same pipeline was run with two other transformer models and `fasttext` for comparison. Accuracy and macro F1 score were recorded for each of the 6 fine-tuning sessions and post festum analyzed. | model | average accuracy | average macro F1| |---|---|---| |sloberta-frenk-hate|0.7785|0.7764| |EMBEDDIA/crosloengual-bert |0.7616|0.7585| |xlm-roberta-base |0.686|0.6827| |fasttext|0.709 |0.701 | From recorded accuracies and macro F1 scores p-values were also calculated: Comparison with `crosloengual-bert`: | test | accuracy p-value | macro F1 p-value| | --- | --- | --- | |Wilcoxon|0.00781|0.00781| |Mann Whithney U test|0.00163|0.00108| |Student t-test |0.000101|3.95e-05| Comparison with `xlm-roberta-base`: | test | accuracy p-value | macro F1 p-value| | --- | --- | --- | |Wilcoxon|0.00781|0.00781| |Mann Whithney U test|0.00108|0.00108| |Student t-test |9.46e-11|6.94e-11| ## Use examples ```python from simpletransformers.classification import ClassificationModel model_args = { "num_train_epochs": 6, "learning_rate": 3e-6, "train_batch_size": 69} model = ClassificationModel( "camembert", "5roop/sloberta-frenk-hate", use_cuda=True, args=model_args ) predictions, logit_output = model.predict(["Silva, ti si grda in neprijazna", "Naša hiša ima dimnik"]) predictions ### Output: ### array([1, 0]) ``` ## Citation If you use the model, please cite the following paper on which the original model is based: ``` @article{DBLP:journals/corr/abs-1907-11692, author = {Yinhan Liu and Myle Ott and Naman Goyal and Jingfei Du and Mandar Joshi and Danqi Chen and Omer Levy and Mike Lewis and Luke Zettlemoyer and Veselin Stoyanov}, title = {RoBERTa: {A} Robustly Optimized {BERT} Pretraining Approach}, journal = {CoRR}, volume = {abs/1907.11692}, year = {2019}, url = {http://arxiv.org/abs/1907.11692}, archivePrefix = {arXiv}, eprint = {1907.11692}, timestamp = {Thu, 01 Aug 2019 08:59:33 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-1907-11692.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ``` and the dataset used for fine-tuning: ``` @misc{ljubešić2019frenk, title={The FRENK Datasets of Socially Unacceptable Discourse in Slovene and English}, author={Nikola Ljubešić and Darja Fišer and Tomaž Erjavec}, year={2019}, eprint={1906.02045}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/1906.02045} } ```
{"language": "sl", "license": "cc-by-sa-4.0", "tags": ["text-classification", "hate-speech"], "widget": [{"text": "Silva, ti si grda in neprijazna"}]}
text-classification
classla/sloberta-frenk-hate
[ "transformers", "pytorch", "safetensors", "camembert", "text-classification", "hate-speech", "sl", "arxiv:1907.11692", "arxiv:1906.02045", "license:cc-by-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[ "1907.11692", "1906.02045" ]
[ "sl" ]
TAGS #transformers #pytorch #safetensors #camembert #text-classification #hate-speech #sl #arxiv-1907.11692 #arxiv-1906.02045 #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
Text classification model based on 'EMBEDDIA/sloberta' and fine-tuned on the FRENK dataset comprising of LGBT and migrant hatespeech. Only the slovenian subset of the data was used for fine-tuning and the dataset has been relabeled for binary classification (offensive or acceptable). Fine-tuning hyperparameters --------------------------- Fine-tuning was performed with 'simpletransformers'. Beforehand a brief hyperparameter optimisation was performed and the presumed optimal hyperparameters are: Performance ----------- The same pipeline was run with two other transformer models and 'fasttext' for comparison. Accuracy and macro F1 score were recorded for each of the 6 fine-tuning sessions and post festum analyzed. model: sloberta-frenk-hate, average accuracy: 0.7785, average macro F1: 0.7764 model: EMBEDDIA/crosloengual-bert, average accuracy: 0.7616, average macro F1: 0.7585 model: xlm-roberta-base, average accuracy: 0.686, average macro F1: 0.6827 model: fasttext, average accuracy: 0.709, average macro F1: 0.701 From recorded accuracies and macro F1 scores p-values were also calculated: Comparison with 'crosloengual-bert': test: Wilcoxon, accuracy p-value: 0.00781, macro F1 p-value: 0.00781 test: Mann Whithney U test, accuracy p-value: 0.00163, macro F1 p-value: 0.00108 test: Student t-test, accuracy p-value: 0.000101, macro F1 p-value: 3.95e-05 Comparison with 'xlm-roberta-base': test: Wilcoxon, accuracy p-value: 0.00781, macro F1 p-value: 0.00781 test: Mann Whithney U test, accuracy p-value: 0.00108, macro F1 p-value: 0.00108 test: Student t-test, accuracy p-value: 9.46e-11, macro F1 p-value: 6.94e-11 Use examples ------------ If you use the model, please cite the following paper on which the original model is based: and the dataset used for fine-tuning:
[]
[ "TAGS\n#transformers #pytorch #safetensors #camembert #text-classification #hate-speech #sl #arxiv-1907.11692 #arxiv-1906.02045 #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n" ]
[ 78 ]
[ "passage: TAGS\n#transformers #pytorch #safetensors #camembert #text-classification #hate-speech #sl #arxiv-1907.11692 #arxiv-1906.02045 #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n" ]
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transformers
# wav2vec2-xls-r-parlaspeech-hr This model for Croatian ASR is based on the [facebook/wav2vec2-xls-r-300m model](https://huggingface.co/facebook/wav2vec2-xls-r-300m) and was fine-tuned with 300 hours of recordings and transcripts from the ASR Croatian parliament dataset [ParlaSpeech-HR v1.0](http://hdl.handle.net/11356/1494). If you use this model, please cite the following paper: Nikola Ljubešić, Danijel Koržinek, Peter Rupnik, Ivo-Pavao Jazbec. ParlaSpeech-HR -- a freely available ASR dataset for Croatian bootstrapped from the ParlaMint corpus. http://www.lrec-conf.org/proceedings/lrec2022/workshops/ParlaCLARINIII/pdf/2022.parlaclariniii-1.16.pdf ## Metrics Evaluation is performed on the dev and test portions of the [ParlaSpeech-HR v1.0](http://hdl.handle.net/11356/1494) dataset. |split|CER|WER| |---|---|---| |dev|0.0335|0.1046| |test|0.0234|0.0761| There are multiple models available, and in terms of CER and WER, the best-performing model is [wav2vec2-large-slavic-parlaspeech-hr-lm](https://huggingface.co/classla/wav2vec2-large-slavic-parlaspeech-hr-lm). ## Usage in `transformers` ```python from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC import soundfile as sf import torch import os device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") # load model and tokenizer processor = Wav2Vec2Processor.from_pretrained( "classla/wav2vec2-xls-r-parlaspeech-hr") model = Wav2Vec2ForCTC.from_pretrained("classla/wav2vec2-xls-r-parlaspeech-hr") # download the example wav files: os.system("wget https://huggingface.co/classla/wav2vec2-xls-r-parlaspeech-hr/raw/main/00020570a.flac.wav") # read the wav file speech, sample_rate = sf.read("00020570a.flac.wav") input_values = processor(speech, sampling_rate=sample_rate, return_tensors="pt").input_values.to(device) # remove the raw wav file os.system("rm 00020570a.flac.wav") # retrieve logits logits = model.to(device)(input_values).logits # take argmax and decode predicted_ids = torch.argmax(logits, dim=-1) transcription = processor.decode(predicted_ids[0]).lower() # transcription: 'veliki broj poslovnih subjekata posluje sa minusom velik dio' ``` ## Training hyperparameters In fine-tuning, the following arguments were used: | arg | value | |-------------------------------|-------| | `per_device_train_batch_size` | 16 | | `gradient_accumulation_steps` | 4 | | `num_train_epochs` | 8 | | `learning_rate` | 3e-4 | | `warmup_steps` | 500 |
{"language": "hr", "tags": ["audio", "automatic-speech-recognition", "parlaspeech"], "datasets": ["parlaspeech-hr"], "widget": [{"example_title": "example 1", "src": "https://huggingface.co/classla/wav2vec2-xls-r-parlaspeech-hr/raw/main/1800.m4a"}, {"example_title": "example 2", "src": "https://huggingface.co/classla/wav2vec2-xls-r-parlaspeech-hr/raw/main/00020578b.flac.wav"}]}
automatic-speech-recognition
classla/wav2vec2-xls-r-parlaspeech-hr
[ "transformers", "pytorch", "safetensors", "wav2vec2", "automatic-speech-recognition", "audio", "parlaspeech", "hr", "dataset:parlaspeech-hr", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[ "hr" ]
TAGS #transformers #pytorch #safetensors #wav2vec2 #automatic-speech-recognition #audio #parlaspeech #hr #dataset-parlaspeech-hr #endpoints_compatible #region-us
wav2vec2-xls-r-parlaspeech-hr ============================= This model for Croatian ASR is based on the facebook/wav2vec2-xls-r-300m model and was fine-tuned with 300 hours of recordings and transcripts from the ASR Croatian parliament dataset ParlaSpeech-HR v1.0. If you use this model, please cite the following paper: Nikola Ljubešić, Danijel Koržinek, Peter Rupnik, Ivo-Pavao Jazbec. ParlaSpeech-HR -- a freely available ASR dataset for Croatian bootstrapped from the ParlaMint corpus. URL Metrics ------- Evaluation is performed on the dev and test portions of the ParlaSpeech-HR v1.0 dataset. split: dev, CER: 0.0335, WER: 0.1046 split: test, CER: 0.0234, WER: 0.0761 There are multiple models available, and in terms of CER and WER, the best-performing model is wav2vec2-large-slavic-parlaspeech-hr-lm. Usage in 'transformers' ----------------------- Training hyperparameters ------------------------ In fine-tuning, the following arguments were used:
[]
[ "TAGS\n#transformers #pytorch #safetensors #wav2vec2 #automatic-speech-recognition #audio #parlaspeech #hr #dataset-parlaspeech-hr #endpoints_compatible #region-us \n" ]
[ 62 ]
[ "passage: TAGS\n#transformers #pytorch #safetensors #wav2vec2 #automatic-speech-recognition #audio #parlaspeech #hr #dataset-parlaspeech-hr #endpoints_compatible #region-us \n" ]
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null
null
transformers
# hiccupBot medium GPT
{"tags": ["conversational"]}
text-generation
clayfox/DialoGPT-medium-Hiccup
[ "transformers", "pytorch", "gpt2", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[]
TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
# hiccupBot medium GPT
[ "# hiccupBot medium GPT" ]
[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# hiccupBot medium GPT" ]
[ 51, 7 ]
[ "passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# hiccupBot medium GPT" ]
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null
null
transformers
# HiccupBot DialoGPT Model
{"tags": ["conversational"]}
text-generation
clayfox/DialoGPT-small-Hiccup
[ "transformers", "pytorch", "gpt2", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[]
TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
# HiccupBot DialoGPT Model
[ "# HiccupBot DialoGPT Model" ]
[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# HiccupBot DialoGPT Model" ]
[ 51, 9 ]
[ "passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# HiccupBot DialoGPT Model" ]
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null
null
transformers
# Model Trained Using AutoNLP - Problem type: Binary Classification - Model ID: 2101779 ## Validation Metrics - Loss: 0.282466858625412 - Accuracy: 1.0 - Precision: 1.0 - Recall: 1.0 - AUC: 1.0 - F1: 1.0 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP"}' https://api-inference.huggingface.co/models/clem/autonlp-test3-2101779 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("clem/autonlp-test3-2101779", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("clem/autonlp-test3-2101779", use_auth_token=True) inputs = tokenizer("I love AutoNLP", return_tensors="pt") outputs = model(**inputs) ```
{"language": "en", "tags": "autonlp", "datasets": ["clem/autonlp-data-test3"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}]}
text-classification
clem/autonlp-test3-2101779
[ "transformers", "pytorch", "bert", "text-classification", "autonlp", "en", "dataset:clem/autonlp-data-test3", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[ "en" ]
TAGS #transformers #pytorch #bert #text-classification #autonlp #en #dataset-clem/autonlp-data-test3 #autotrain_compatible #endpoints_compatible #region-us
# Model Trained Using AutoNLP - Problem type: Binary Classification - Model ID: 2101779 ## Validation Metrics - Loss: 0.282466858625412 - Accuracy: 1.0 - Precision: 1.0 - Recall: 1.0 - AUC: 1.0 - F1: 1.0 ## Usage You can use cURL to access this model: Or Python API:
[ "# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 2101779", "## Validation Metrics\n\n- Loss: 0.282466858625412\n- Accuracy: 1.0\n- Precision: 1.0\n- Recall: 1.0\n- AUC: 1.0\n- F1: 1.0", "## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:" ]
[ "TAGS\n#transformers #pytorch #bert #text-classification #autonlp #en #dataset-clem/autonlp-data-test3 #autotrain_compatible #endpoints_compatible #region-us \n", "# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 2101779", "## Validation Metrics\n\n- Loss: 0.282466858625412\n- Accuracy: 1.0\n- Precision: 1.0\n- Recall: 1.0\n- AUC: 1.0\n- F1: 1.0", "## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:" ]
[ 57, 24, 43, 17 ]
[ "passage: TAGS\n#transformers #pytorch #bert #text-classification #autonlp #en #dataset-clem/autonlp-data-test3 #autotrain_compatible #endpoints_compatible #region-us \n# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 2101779## Validation Metrics\n\n- Loss: 0.282466858625412\n- Accuracy: 1.0\n- Precision: 1.0\n- Recall: 1.0\n- AUC: 1.0\n- F1: 1.0## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:" ]
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null
null
transformers
# Model Trained Using AutoNLP - Problem type: Binary Classification - Model ID: 2101782 ## Validation Metrics - Loss: 0.015991805121302605 - Accuracy: 1.0 - Precision: 1.0 - Recall: 1.0 - AUC: 1.0 - F1: 1.0 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP"}' https://api-inference.huggingface.co/models/clem/autonlp-test3-2101782 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("clem/autonlp-test3-2101782", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("clem/autonlp-test3-2101782", use_auth_token=True) inputs = tokenizer("I love AutoNLP", return_tensors="pt") outputs = model(**inputs) ```
{"language": "en", "tags": "autonlp", "datasets": ["clem/autonlp-data-test3"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}]}
text-classification
clem/autonlp-test3-2101782
[ "transformers", "pytorch", "bert", "text-classification", "autonlp", "en", "dataset:clem/autonlp-data-test3", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[ "en" ]
TAGS #transformers #pytorch #bert #text-classification #autonlp #en #dataset-clem/autonlp-data-test3 #autotrain_compatible #endpoints_compatible #region-us
# Model Trained Using AutoNLP - Problem type: Binary Classification - Model ID: 2101782 ## Validation Metrics - Loss: 0.015991805121302605 - Accuracy: 1.0 - Precision: 1.0 - Recall: 1.0 - AUC: 1.0 - F1: 1.0 ## Usage You can use cURL to access this model: Or Python API:
[ "# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 2101782", "## Validation Metrics\n\n- Loss: 0.015991805121302605\n- Accuracy: 1.0\n- Precision: 1.0\n- Recall: 1.0\n- AUC: 1.0\n- F1: 1.0", "## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:" ]
[ "TAGS\n#transformers #pytorch #bert #text-classification #autonlp #en #dataset-clem/autonlp-data-test3 #autotrain_compatible #endpoints_compatible #region-us \n", "# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 2101782", "## Validation Metrics\n\n- Loss: 0.015991805121302605\n- Accuracy: 1.0\n- Precision: 1.0\n- Recall: 1.0\n- AUC: 1.0\n- F1: 1.0", "## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:" ]
[ 57, 24, 44, 17 ]
[ "passage: TAGS\n#transformers #pytorch #bert #text-classification #autonlp #en #dataset-clem/autonlp-data-test3 #autotrain_compatible #endpoints_compatible #region-us \n# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 2101782## Validation Metrics\n\n- Loss: 0.015991805121302605\n- Accuracy: 1.0\n- Precision: 1.0\n- Recall: 1.0\n- AUC: 1.0\n- F1: 1.0## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:" ]
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null
null
transformers
# Model Trained Using AutoNLP - Problem type: Binary Classification Urgent/Not Urgent ## Validation Metrics - Loss: 0.08956164121627808 - Accuracy: 1.0 - Precision: 1.0 - Recall: 1.0 - AUC: 1.0 - F1: 1.0 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP"}' https://api-inference.huggingface.co/models/clem/autonlp-test3-2101787 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("clem/autonlp-test3-2101787", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("clem/autonlp-test3-2101787", use_auth_token=True) inputs = tokenizer("I love AutoNLP", return_tensors="pt") outputs = model(**inputs) ```
{"language": "en", "tags": "autonlp", "datasets": ["clem/autonlp-data-test3"], "widget": [{"text": "this can wait"}]}
text-classification
clem/autonlp-test3-2101787
[ "transformers", "pytorch", "distilbert", "text-classification", "autonlp", "en", "dataset:clem/autonlp-data-test3", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[ "en" ]
TAGS #transformers #pytorch #distilbert #text-classification #autonlp #en #dataset-clem/autonlp-data-test3 #autotrain_compatible #endpoints_compatible #region-us
# Model Trained Using AutoNLP - Problem type: Binary Classification Urgent/Not Urgent ## Validation Metrics - Loss: 0.08956164121627808 - Accuracy: 1.0 - Precision: 1.0 - Recall: 1.0 - AUC: 1.0 - F1: 1.0 ## Usage You can use cURL to access this model: Or Python API:
[ "# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification Urgent/Not Urgent", "## Validation Metrics\n\n- Loss: 0.08956164121627808\n- Accuracy: 1.0\n- Precision: 1.0\n- Recall: 1.0\n- AUC: 1.0\n- F1: 1.0", "## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:" ]
[ "TAGS\n#transformers #pytorch #distilbert #text-classification #autonlp #en #dataset-clem/autonlp-data-test3 #autotrain_compatible #endpoints_compatible #region-us \n", "# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification Urgent/Not Urgent", "## Validation Metrics\n\n- Loss: 0.08956164121627808\n- Accuracy: 1.0\n- Precision: 1.0\n- Recall: 1.0\n- AUC: 1.0\n- F1: 1.0", "## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:" ]
[ 59, 23, 44, 17 ]
[ "passage: TAGS\n#transformers #pytorch #distilbert #text-classification #autonlp #en #dataset-clem/autonlp-data-test3 #autotrain_compatible #endpoints_compatible #region-us \n# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification Urgent/Not Urgent## Validation Metrics\n\n- Loss: 0.08956164121627808\n- Accuracy: 1.0\n- Precision: 1.0\n- Recall: 1.0\n- AUC: 1.0\n- F1: 1.0## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:" ]
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null
null
transformers
# Model Card for distilroberta-base-climate-commitment ## Model Description This is the fine-tuned ClimateBERT language model with a classification head for classifying climate-related paragraphs into paragraphs being about climate commitments and actions and paragraphs not being about climate commitments and actions. Using the [climatebert/distilroberta-base-climate-f](https://huggingface.co/climatebert/distilroberta-base-climate-f) language model as starting point, the distilroberta-base-climate-commitment model is fine-tuned on our [climatebert/climate_commitments_actions](https://huggingface.co/climatebert/climate_commitments_actions) dataset. *Note: This model is trained on paragraphs. It may not perform well on sentences.* ## Citation Information ```bibtex @techreport{bingler2023cheaptalk, title={How Cheap Talk in Climate Disclosures Relates to Climate Initiatives, Corporate Emissions, and Reputation Risk}, author={Bingler, Julia and Kraus, Mathias and Leippold, Markus and Webersinke, Nicolas}, type={Working paper}, institution={Available at SSRN 3998435}, year={2023} } ``` ## How to Get Started With the Model You can use the model with a pipeline for text classification: ```python from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline from transformers.pipelines.pt_utils import KeyDataset import datasets from tqdm.auto import tqdm dataset_name = "climatebert/climate_commitments_actions" model_name = "climatebert/distilroberta-base-climate-commitment" # If you want to use your own data, simply load them as 🤗 Datasets dataset, see https://huggingface.co/docs/datasets/loading dataset = datasets.load_dataset(dataset_name, split="test") model = AutoModelForSequenceClassification.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name, max_len=512) pipe = pipeline("text-classification", model=model, tokenizer=tokenizer, device=0) # See https://huggingface.co/docs/transformers/main_classes/pipelines#transformers.pipeline for out in tqdm(pipe(KeyDataset(dataset, "text"), padding=True, truncation=True)): print(out) ```
{"language": ["en"], "license": "apache-2.0", "datasets": ["climatebert/climate_commitments_actions"], "metrics": ["accuracy"]}
text-classification
climatebert/distilroberta-base-climate-commitment
[ "transformers", "pytorch", "safetensors", "roberta", "text-classification", "en", "dataset:climatebert/climate_commitments_actions", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[ "en" ]
TAGS #transformers #pytorch #safetensors #roberta #text-classification #en #dataset-climatebert/climate_commitments_actions #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
# Model Card for distilroberta-base-climate-commitment ## Model Description This is the fine-tuned ClimateBERT language model with a classification head for classifying climate-related paragraphs into paragraphs being about climate commitments and actions and paragraphs not being about climate commitments and actions. Using the climatebert/distilroberta-base-climate-f language model as starting point, the distilroberta-base-climate-commitment model is fine-tuned on our climatebert/climate_commitments_actions dataset. *Note: This model is trained on paragraphs. It may not perform well on sentences.* ## How to Get Started With the Model You can use the model with a pipeline for text classification:
[ "# Model Card for distilroberta-base-climate-commitment", "## Model Description\n\nThis is the fine-tuned ClimateBERT language model with a classification head for classifying climate-related paragraphs into paragraphs being about climate commitments and actions and paragraphs not being about climate commitments and actions.\n\nUsing the climatebert/distilroberta-base-climate-f language model as starting point, the distilroberta-base-climate-commitment model is fine-tuned on our climatebert/climate_commitments_actions dataset.\n\n*Note: This model is trained on paragraphs. It may not perform well on sentences.*", "## How to Get Started With the Model\n\nYou can use the model with a pipeline for text classification:" ]
[ "TAGS\n#transformers #pytorch #safetensors #roberta #text-classification #en #dataset-climatebert/climate_commitments_actions #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# Model Card for distilroberta-base-climate-commitment", "## Model Description\n\nThis is the fine-tuned ClimateBERT language model with a classification head for classifying climate-related paragraphs into paragraphs being about climate commitments and actions and paragraphs not being about climate commitments and actions.\n\nUsing the climatebert/distilroberta-base-climate-f language model as starting point, the distilroberta-base-climate-commitment model is fine-tuned on our climatebert/climate_commitments_actions dataset.\n\n*Note: This model is trained on paragraphs. It may not perform well on sentences.*", "## How to Get Started With the Model\n\nYou can use the model with a pipeline for text classification:" ]
[ 71, 18, 136, 23 ]
[ "passage: TAGS\n#transformers #pytorch #safetensors #roberta #text-classification #en #dataset-climatebert/climate_commitments_actions #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# Model Card for distilroberta-base-climate-commitment## Model Description\n\nThis is the fine-tuned ClimateBERT language model with a classification head for classifying climate-related paragraphs into paragraphs being about climate commitments and actions and paragraphs not being about climate commitments and actions.\n\nUsing the climatebert/distilroberta-base-climate-f language model as starting point, the distilroberta-base-climate-commitment model is fine-tuned on our climatebert/climate_commitments_actions dataset.\n\n*Note: This model is trained on paragraphs. It may not perform well on sentences.*## How to Get Started With the Model\n\nYou can use the model with a pipeline for text classification:" ]
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null
null
transformers
# Model Card for distilroberta-base-climate-d-s ## Model Description This is the ClimateBERT language model based on the DIV-SELECT and SIM-SELECT sample selection strategy. *Note: We generally recommend choosing the [distilroberta-base-climate-f](https://huggingface.co/climatebert/distilroberta-base-climate-f) language model over this language model (unless you have good reasons not to).* Using the [DistilRoBERTa](https://huggingface.co/distilroberta-base) model as starting point, the ClimateBERT Language Model is additionally pre-trained on a text corpus comprising climate-related research paper abstracts, corporate and general news and reports from companies. The underlying methodology can be found in our [language model research paper](https://arxiv.org/abs/2110.12010). ## Climate performance model card | distilroberta-base-climate-d-s | | |--------------------------------------------------------------------------|----------------| | 1. Is the resulting model publicly available? | Yes | | 2. How much time does the training of the final model take? | 48 hours | | 3. How much time did all experiments take (incl. hyperparameter search)? | 350 hours | | 4. What was the power of GPU and CPU? | 0.7 kW | | 5. At which geo location were the computations performed? | Germany | | 6. What was the energy mix at the geo location? | 470 gCO2eq/kWh | | 7. How much CO2eq was emitted to train the final model? | 15.79 kg | | 8. How much CO2eq was emitted for all experiments? | 115.15 kg | | 9. What is the average CO2eq emission for the inference of one sample? | 0.62 mg | | 10. Which positive environmental impact can be expected from this work? | This work can be categorized as a building block tools following Jin et al (2021). It supports the training of NLP models in the field of climate change and, thereby, have a positive environmental impact in the future. | | 11. Comments | Block pruning could decrease CO2eq emissions | ## Citation Information ```bibtex @inproceedings{wkbl2022climatebert, title={{ClimateBERT: A Pretrained Language Model for Climate-Related Text}}, author={Webersinke, Nicolas and Kraus, Mathias and Bingler, Julia and Leippold, Markus}, booktitle={Proceedings of AAAI 2022 Fall Symposium: The Role of AI in Responding to Climate Challenges}, year={2022}, doi={https://doi.org/10.48550/arXiv.2212.13631}, } ```
{"language": "en", "license": "apache-2.0", "tags": ["climate"]}
fill-mask
climatebert/distilroberta-base-climate-d-s
[ "transformers", "pytorch", "safetensors", "roberta", "fill-mask", "climate", "en", "arxiv:2110.12010", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[ "2110.12010" ]
[ "en" ]
TAGS #transformers #pytorch #safetensors #roberta #fill-mask #climate #en #arxiv-2110.12010 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
Model Card for distilroberta-base-climate-d-s ============================================= Model Description ----------------- This is the ClimateBERT language model based on the DIV-SELECT and SIM-SELECT sample selection strategy. *Note: We generally recommend choosing the distilroberta-base-climate-f language model over this language model (unless you have good reasons not to).* Using the DistilRoBERTa model as starting point, the ClimateBERT Language Model is additionally pre-trained on a text corpus comprising climate-related research paper abstracts, corporate and general news and reports from companies. The underlying methodology can be found in our language model research paper. Climate performance model card ------------------------------
[]
[ "TAGS\n#transformers #pytorch #safetensors #roberta #fill-mask #climate #en #arxiv-2110.12010 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n" ]
[ 65 ]
[ "passage: TAGS\n#transformers #pytorch #safetensors #roberta #fill-mask #climate #en #arxiv-2110.12010 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n" ]
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null
null
transformers
# Model Card for distilroberta-base-climate-d ## Model Description This is the ClimateBERT language model based on the DIV-SELECT sample selection strategy. *Note: We generally recommend choosing the [distilroberta-base-climate-f](https://huggingface.co/climatebert/distilroberta-base-climate-f) language model over this language model (unless you have good reasons not to).* Using the [DistilRoBERTa](https://huggingface.co/distilroberta-base) model as starting point, the ClimateBERT Language Model is additionally pre-trained on a text corpus comprising climate-related research paper abstracts, corporate and general news and reports from companies. The underlying methodology can be found in our [language model research paper](https://arxiv.org/abs/2110.12010). ## Climate performance model card | distilroberta-base-climate-d | | |--------------------------------------------------------------------------|----------------| | 1. Is the resulting model publicly available? | Yes | | 2. How much time does the training of the final model take? | 48 hours | | 3. How much time did all experiments take (incl. hyperparameter search)? | 350 hours | | 4. What was the power of GPU and CPU? | 0.7 kW | | 5. At which geo location were the computations performed? | Germany | | 6. What was the energy mix at the geo location? | 470 gCO2eq/kWh | | 7. How much CO2eq was emitted to train the final model? | 15.79 kg | | 8. How much CO2eq was emitted for all experiments? | 115.15 kg | | 9. What is the average CO2eq emission for the inference of one sample? | 0.62 mg | | 10. Which positive environmental impact can be expected from this work? | This work can be categorized as a building block tools following Jin et al (2021). It supports the training of NLP models in the field of climate change and, thereby, have a positive environmental impact in the future. | | 11. Comments | Block pruning could decrease CO2eq emissions | ## Citation Information ```bibtex @inproceedings{wkbl2022climatebert, title={{ClimateBERT: A Pretrained Language Model for Climate-Related Text}}, author={Webersinke, Nicolas and Kraus, Mathias and Bingler, Julia and Leippold, Markus}, booktitle={Proceedings of AAAI 2022 Fall Symposium: The Role of AI in Responding to Climate Challenges}, year={2022}, doi={https://doi.org/10.48550/arXiv.2212.13631}, } ```
{"language": "en", "license": "apache-2.0"}
fill-mask
climatebert/distilroberta-base-climate-d
[ "transformers", "pytorch", "roberta", "fill-mask", "en", "arxiv:2110.12010", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[ "2110.12010" ]
[ "en" ]
TAGS #transformers #pytorch #roberta #fill-mask #en #arxiv-2110.12010 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
Model Card for distilroberta-base-climate-d =========================================== Model Description ----------------- This is the ClimateBERT language model based on the DIV-SELECT sample selection strategy. *Note: We generally recommend choosing the distilroberta-base-climate-f language model over this language model (unless you have good reasons not to).* Using the DistilRoBERTa model as starting point, the ClimateBERT Language Model is additionally pre-trained on a text corpus comprising climate-related research paper abstracts, corporate and general news and reports from companies. The underlying methodology can be found in our language model research paper. Climate performance model card ------------------------------
[]
[ "TAGS\n#transformers #pytorch #roberta #fill-mask #en #arxiv-2110.12010 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n" ]
[ 56 ]
[ "passage: TAGS\n#transformers #pytorch #roberta #fill-mask #en #arxiv-2110.12010 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n" ]
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null
null
transformers
# Model Card for distilroberta-base-climate-detector ## Model Description This is the fine-tuned ClimateBERT language model with a classification head for detecting climate-related paragraphs. Using the [climatebert/distilroberta-base-climate-f](https://huggingface.co/climatebert/distilroberta-base-climate-f) language model as starting point, the distilroberta-base-climate-detector model is fine-tuned on our [climatebert/climate_detection](https://huggingface.co/climatebert/climate_detection) dataset. *Note: This model is trained on paragraphs. It may not perform well on sentences.* ## Citation Information ```bibtex @techreport{bingler2023cheaptalk, title={How Cheap Talk in Climate Disclosures Relates to Climate Initiatives, Corporate Emissions, and Reputation Risk}, author={Bingler, Julia and Kraus, Mathias and Leippold, Markus and Webersinke, Nicolas}, type={Working paper}, institution={Available at SSRN 3998435}, year={2023} } ``` ## How to Get Started With the Model You can use the model with a pipeline for text classification: ```python from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline from transformers.pipelines.pt_utils import KeyDataset import datasets from tqdm.auto import tqdm dataset_name = "climatebert/climate_detection" model_name = "climatebert/distilroberta-base-climate-detector" # If you want to use your own data, simply load them as 🤗 Datasets dataset, see https://huggingface.co/docs/datasets/loading dataset = datasets.load_dataset(dataset_name, split="test") model = AutoModelForSequenceClassification.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name, max_len=512) pipe = pipeline("text-classification", model=model, tokenizer=tokenizer, device=0) # See https://huggingface.co/docs/transformers/main_classes/pipelines#transformers.pipeline for out in tqdm(pipe(KeyDataset(dataset, "text"), padding=True, truncation=True)): print(out) ```
{"language": ["en"], "license": "apache-2.0", "datasets": ["climatebert/climate_detection"], "metrics": ["accuracy"]}
text-classification
climatebert/distilroberta-base-climate-detector
[ "transformers", "pytorch", "safetensors", "roberta", "text-classification", "en", "dataset:climatebert/climate_detection", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[ "en" ]
TAGS #transformers #pytorch #safetensors #roberta #text-classification #en #dataset-climatebert/climate_detection #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
# Model Card for distilroberta-base-climate-detector ## Model Description This is the fine-tuned ClimateBERT language model with a classification head for detecting climate-related paragraphs. Using the climatebert/distilroberta-base-climate-f language model as starting point, the distilroberta-base-climate-detector model is fine-tuned on our climatebert/climate_detection dataset. *Note: This model is trained on paragraphs. It may not perform well on sentences.* ## How to Get Started With the Model You can use the model with a pipeline for text classification:
[ "# Model Card for distilroberta-base-climate-detector", "## Model Description\n\nThis is the fine-tuned ClimateBERT language model with a classification head for detecting climate-related paragraphs.\n\nUsing the climatebert/distilroberta-base-climate-f language model as starting point, the distilroberta-base-climate-detector model is fine-tuned on our climatebert/climate_detection dataset.\n\n*Note: This model is trained on paragraphs. It may not perform well on sentences.*", "## How to Get Started With the Model\n\nYou can use the model with a pipeline for text classification:" ]
[ "TAGS\n#transformers #pytorch #safetensors #roberta #text-classification #en #dataset-climatebert/climate_detection #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n", "# Model Card for distilroberta-base-climate-detector", "## Model Description\n\nThis is the fine-tuned ClimateBERT language model with a classification head for detecting climate-related paragraphs.\n\nUsing the climatebert/distilroberta-base-climate-f language model as starting point, the distilroberta-base-climate-detector model is fine-tuned on our climatebert/climate_detection dataset.\n\n*Note: This model is trained on paragraphs. It may not perform well on sentences.*", "## How to Get Started With the Model\n\nYou can use the model with a pipeline for text classification:" ]
[ 71, 18, 111, 23 ]
[ "passage: TAGS\n#transformers #pytorch #safetensors #roberta #text-classification #en #dataset-climatebert/climate_detection #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n# Model Card for distilroberta-base-climate-detector## Model Description\n\nThis is the fine-tuned ClimateBERT language model with a classification head for detecting climate-related paragraphs.\n\nUsing the climatebert/distilroberta-base-climate-f language model as starting point, the distilroberta-base-climate-detector model is fine-tuned on our climatebert/climate_detection dataset.\n\n*Note: This model is trained on paragraphs. It may not perform well on sentences.*## How to Get Started With the Model\n\nYou can use the model with a pipeline for text classification:" ]
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null
null
transformers
# Model Card for distilroberta-base-climate-f ## Model Description This is the ClimateBERT language model based on the FULL-SELECT sample selection strategy. *Note: We generally recommend choosing this language model over those based on the other sample selection strategies (unless you have good reasons not to). This is also the only language model we will update from time to time.* Using the [DistilRoBERTa](https://huggingface.co/distilroberta-base) model as starting point, the ClimateBERT Language Model is additionally pre-trained on a text corpus comprising climate-related research paper abstracts, corporate and general news and reports from companies. The underlying methodology can be found in our [language model research paper](https://arxiv.org/abs/2110.12010). *Update September 2, 2022: Now additionally pre-trained on an even larger text corpus, comprising >2M paragraphs. If you are looking for the language model before the update (i.e. for reproducibility), just use an older commit like [6be4fbd](https://huggingface.co/climatebert/distilroberta-base-climate-f/tree/6be4fbd3fedfd78ccb3c730c1f166947fbc940ba).* ## Climate performance model card | distilroberta-base-climate-f | | |--------------------------------------------------------------------------|----------------| | 1. Is the resulting model publicly available? | Yes | | 2. How much time does the training of the final model take? | 48 hours | | 3. How much time did all experiments take (incl. hyperparameter search)? | 350 hours | | 4. What was the power of GPU and CPU? | 0.7 kW | | 5. At which geo location were the computations performed? | Germany | | 6. What was the energy mix at the geo location? | 470 gCO2eq/kWh | | 7. How much CO2eq was emitted to train the final model? | 15.79 kg | | 8. How much CO2eq was emitted for all experiments? | 115.15 kg | | 9. What is the average CO2eq emission for the inference of one sample? | 0.62 mg | | 10. Which positive environmental impact can be expected from this work? | This work can be categorized as a building block tools following Jin et al (2021). It supports the training of NLP models in the field of climate change and, thereby, have a positive environmental impact in the future. | | 11. Comments | Block pruning could decrease CO2eq emissions | ## Citation Information ```bibtex @inproceedings{wkbl2022climatebert, title={{ClimateBERT: A Pretrained Language Model for Climate-Related Text}}, author={Webersinke, Nicolas and Kraus, Mathias and Bingler, Julia and Leippold, Markus}, booktitle={Proceedings of AAAI 2022 Fall Symposium: The Role of AI in Responding to Climate Challenges}, year={2022}, doi={https://doi.org/10.48550/arXiv.2212.13631}, } ```
{"language": "en", "license": "apache-2.0", "tags": ["climate"]}
fill-mask
climatebert/distilroberta-base-climate-f
[ "transformers", "pytorch", "safetensors", "roberta", "fill-mask", "climate", "en", "arxiv:2110.12010", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]
2022-03-02T23:29:05+00:00
[ "2110.12010" ]
[ "en" ]
TAGS #transformers #pytorch #safetensors #roberta #fill-mask #climate #en #arxiv-2110.12010 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
Model Card for distilroberta-base-climate-f =========================================== Model Description ----------------- This is the ClimateBERT language model based on the FULL-SELECT sample selection strategy. *Note: We generally recommend choosing this language model over those based on the other sample selection strategies (unless you have good reasons not to). This is also the only language model we will update from time to time.* Using the DistilRoBERTa model as starting point, the ClimateBERT Language Model is additionally pre-trained on a text corpus comprising climate-related research paper abstracts, corporate and general news and reports from companies. The underlying methodology can be found in our language model research paper. *Update September 2, 2022: Now additionally pre-trained on an even larger text corpus, comprising >2M paragraphs. If you are looking for the language model before the update (i.e. for reproducibility), just use an older commit like 6be4fbd.* Climate performance model card ------------------------------
[]
[ "TAGS\n#transformers #pytorch #safetensors #roberta #fill-mask #climate #en #arxiv-2110.12010 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n" ]
[ 69 ]
[ "passage: TAGS\n#transformers #pytorch #safetensors #roberta #fill-mask #climate #en #arxiv-2110.12010 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n" ]
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null
null
transformers
# Model Card for distilroberta-base-climate-s ## Model Description This is the ClimateBERT language model based on the SIM-SELECT sample selection strategy. *Note: We generally recommend choosing the [distilroberta-base-climate-f](https://huggingface.co/climatebert/distilroberta-base-climate-f) language model over this language model (unless you have good reasons not to).* Using the [DistilRoBERTa](https://huggingface.co/distilroberta-base) model as starting point, the ClimateBERT Language Model is additionally pre-trained on a text corpus comprising climate-related research paper abstracts, corporate and general news and reports from companies. The underlying methodology can be found in our [language model research paper](https://arxiv.org/abs/2110.12010). ## Climate performance model card | distilroberta-base-climate-s | | |--------------------------------------------------------------------------|----------------| | 1. Is the resulting model publicly available? | Yes | | 2. How much time does the training of the final model take? | 48 hours | | 3. How much time did all experiments take (incl. hyperparameter search)? | 350 hours | | 4. What was the power of GPU and CPU? | 0.7 kW | | 5. At which geo location were the computations performed? | Germany | | 6. What was the energy mix at the geo location? | 470 gCO2eq/kWh | | 7. How much CO2eq was emitted to train the final model? | 15.79 kg | | 8. How much CO2eq was emitted for all experiments? | 115.15 kg | | 9. What is the average CO2eq emission for the inference of one sample? | 0.62 mg | | 10. Which positive environmental impact can be expected from this work? | This work can be categorized as a building block tools following Jin et al (2021). It supports the training of NLP models in the field of climate change and, thereby, have a positive environmental impact in the future. | | 11. Comments | Block pruning could decrease CO2eq emissions | ## Citation Information ```bibtex @inproceedings{wkbl2022climatebert, title={{ClimateBERT: A Pretrained Language Model for Climate-Related Text}}, author={Webersinke, Nicolas and Kraus, Mathias and Bingler, Julia and Leippold, Markus}, booktitle={Proceedings of AAAI 2022 Fall Symposium: The Role of AI in Responding to Climate Challenges}, year={2022}, doi={https://doi.org/10.48550/arXiv.2212.13631}, } ```
{"language": "en", "license": "apache-2.0"}
fill-mask
climatebert/distilroberta-base-climate-s
[ "transformers", "pytorch", "safetensors", "roberta", "fill-mask", "en", "arxiv:2110.12010", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[ "2110.12010" ]
[ "en" ]
TAGS #transformers #pytorch #safetensors #roberta #fill-mask #en #arxiv-2110.12010 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
Model Card for distilroberta-base-climate-s =========================================== Model Description ----------------- This is the ClimateBERT language model based on the SIM-SELECT sample selection strategy. *Note: We generally recommend choosing the distilroberta-base-climate-f language model over this language model (unless you have good reasons not to).* Using the DistilRoBERTa model as starting point, the ClimateBERT Language Model is additionally pre-trained on a text corpus comprising climate-related research paper abstracts, corporate and general news and reports from companies. The underlying methodology can be found in our language model research paper. Climate performance model card ------------------------------
[]
[ "TAGS\n#transformers #pytorch #safetensors #roberta #fill-mask #en #arxiv-2110.12010 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n" ]
[ 61 ]
[ "passage: TAGS\n#transformers #pytorch #safetensors #roberta #fill-mask #en #arxiv-2110.12010 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n" ]
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null
null
transformers
# Model Card for distilroberta-base-climate-sentiment ## Model Description This is the fine-tuned ClimateBERT language model with a classification head for classifying climate-related paragraphs into the climate-related sentiment classes opportunity, neutral, or risk. Using the [climatebert/distilroberta-base-climate-f](https://huggingface.co/climatebert/distilroberta-base-climate-f) language model as starting point, the distilroberta-base-climate-sentiment model is fine-tuned on our [climatebert/climate_sentiment](https://huggingface.co/climatebert/climate_sentiment) dataset. *Note: This model is trained on paragraphs. It may not perform well on sentences.* ## Citation Information ```bibtex @techreport{bingler2023cheaptalk, title={How Cheap Talk in Climate Disclosures Relates to Climate Initiatives, Corporate Emissions, and Reputation Risk}, author={Bingler, Julia and Kraus, Mathias and Leippold, Markus and Webersinke, Nicolas}, type={Working paper}, institution={Available at SSRN 3998435}, year={2023} } ``` ## How to Get Started With the Model You can use the model with a pipeline for text classification: ```python from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline from transformers.pipelines.pt_utils import KeyDataset import datasets from tqdm.auto import tqdm dataset_name = "climatebert/climate_sentiment" model_name = "climatebert/distilroberta-base-climate-sentiment" # If you want to use your own data, simply load them as 🤗 Datasets dataset, see https://huggingface.co/docs/datasets/loading dataset = datasets.load_dataset(dataset_name, split="test") model = AutoModelForSequenceClassification.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name, max_len=512) pipe = pipeline("text-classification", model=model, tokenizer=tokenizer, device=0) # See https://huggingface.co/docs/transformers/main_classes/pipelines#transformers.pipeline for out in tqdm(pipe(KeyDataset(dataset, "text"), padding=True, truncation=True)): print(out) ```
{"language": ["en"], "license": "apache-2.0", "datasets": ["climatebert/climate_sentiment"], "metrics": ["accuracy"]}
text-classification
climatebert/distilroberta-base-climate-sentiment
[ "transformers", "pytorch", "safetensors", "roberta", "text-classification", "en", "dataset:climatebert/climate_sentiment", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[ "en" ]
TAGS #transformers #pytorch #safetensors #roberta #text-classification #en #dataset-climatebert/climate_sentiment #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
# Model Card for distilroberta-base-climate-sentiment ## Model Description This is the fine-tuned ClimateBERT language model with a classification head for classifying climate-related paragraphs into the climate-related sentiment classes opportunity, neutral, or risk. Using the climatebert/distilroberta-base-climate-f language model as starting point, the distilroberta-base-climate-sentiment model is fine-tuned on our climatebert/climate_sentiment dataset. *Note: This model is trained on paragraphs. It may not perform well on sentences.* ## How to Get Started With the Model You can use the model with a pipeline for text classification:
[ "# Model Card for distilroberta-base-climate-sentiment", "## Model Description\n\nThis is the fine-tuned ClimateBERT language model with a classification head for classifying climate-related paragraphs into the climate-related sentiment classes opportunity, neutral, or risk.\n\nUsing the climatebert/distilroberta-base-climate-f language model as starting point, the distilroberta-base-climate-sentiment model is fine-tuned on our climatebert/climate_sentiment dataset.\n\n*Note: This model is trained on paragraphs. It may not perform well on sentences.*", "## How to Get Started With the Model\n\nYou can use the model with a pipeline for text classification:" ]
[ "TAGS\n#transformers #pytorch #safetensors #roberta #text-classification #en #dataset-climatebert/climate_sentiment #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# Model Card for distilroberta-base-climate-sentiment", "## Model Description\n\nThis is the fine-tuned ClimateBERT language model with a classification head for classifying climate-related paragraphs into the climate-related sentiment classes opportunity, neutral, or risk.\n\nUsing the climatebert/distilroberta-base-climate-f language model as starting point, the distilroberta-base-climate-sentiment model is fine-tuned on our climatebert/climate_sentiment dataset.\n\n*Note: This model is trained on paragraphs. It may not perform well on sentences.*", "## How to Get Started With the Model\n\nYou can use the model with a pipeline for text classification:" ]
[ 67, 17, 123, 23 ]
[ "passage: TAGS\n#transformers #pytorch #safetensors #roberta #text-classification #en #dataset-climatebert/climate_sentiment #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# Model Card for distilroberta-base-climate-sentiment## Model Description\n\nThis is the fine-tuned ClimateBERT language model with a classification head for classifying climate-related paragraphs into the climate-related sentiment classes opportunity, neutral, or risk.\n\nUsing the climatebert/distilroberta-base-climate-f language model as starting point, the distilroberta-base-climate-sentiment model is fine-tuned on our climatebert/climate_sentiment dataset.\n\n*Note: This model is trained on paragraphs. It may not perform well on sentences.*## How to Get Started With the Model\n\nYou can use the model with a pipeline for text classification:" ]
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null
null
transformers
# Model Card for distilroberta-base-climate-specificity ## Model Description This is the fine-tuned ClimateBERT language model with a classification head for classifying climate-related paragraphs into specific and non-specific paragraphs. Using the [climatebert/distilroberta-base-climate-f](https://huggingface.co/climatebert/distilroberta-base-climate-f) language model as starting point, the distilroberta-base-climate-specificity model is fine-tuned on our [climatebert/climate_specificity](https://huggingface.co/climatebert/climate_specificity) dataset. *Note: This model is trained on paragraphs. It may not perform well on sentences.* ## Citation Information ```bibtex @techreport{bingler2023cheaptalk, title={How Cheap Talk in Climate Disclosures Relates to Climate Initiatives, Corporate Emissions, and Reputation Risk}, author={Bingler, Julia and Kraus, Mathias and Leippold, Markus and Webersinke, Nicolas}, type={Working paper}, institution={Available at SSRN 3998435}, year={2023} } ``` ## How to Get Started With the Model You can use the model with a pipeline for text classification: ```python from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline from transformers.pipelines.pt_utils import KeyDataset import datasets from tqdm.auto import tqdm dataset_name = "climatebert/climate_specificity" model_name = "climatebert/distilroberta-base-climate-specificity" # If you want to use your own data, simply load them as 🤗 Datasets dataset, see https://huggingface.co/docs/datasets/loading dataset = datasets.load_dataset(dataset_name, split="test") model = AutoModelForSequenceClassification.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name, max_len=512) pipe = pipeline("text-classification", model=model, tokenizer=tokenizer, device=0) # See https://huggingface.co/docs/transformers/main_classes/pipelines#transformers.pipeline for out in tqdm(pipe(KeyDataset(dataset, "text"), padding=True, truncation=True)): print(out) ```
{"language": ["en"], "license": "apache-2.0", "tags": ["climate"], "datasets": ["climatebert/climate_specificity"], "metrics": ["accuracy"]}
text-classification
climatebert/distilroberta-base-climate-specificity
[ "transformers", "pytorch", "safetensors", "roberta", "text-classification", "climate", "en", "dataset:climatebert/climate_specificity", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[ "en" ]
TAGS #transformers #pytorch #safetensors #roberta #text-classification #climate #en #dataset-climatebert/climate_specificity #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
# Model Card for distilroberta-base-climate-specificity ## Model Description This is the fine-tuned ClimateBERT language model with a classification head for classifying climate-related paragraphs into specific and non-specific paragraphs. Using the climatebert/distilroberta-base-climate-f language model as starting point, the distilroberta-base-climate-specificity model is fine-tuned on our climatebert/climate_specificity dataset. *Note: This model is trained on paragraphs. It may not perform well on sentences.* ## How to Get Started With the Model You can use the model with a pipeline for text classification:
[ "# Model Card for distilroberta-base-climate-specificity", "## Model Description\n\nThis is the fine-tuned ClimateBERT language model with a classification head for classifying climate-related paragraphs into specific and non-specific paragraphs.\n\nUsing the climatebert/distilroberta-base-climate-f language model as starting point, the distilroberta-base-climate-specificity model is fine-tuned on our climatebert/climate_specificity dataset.\n\n*Note: This model is trained on paragraphs. It may not perform well on sentences.*", "## How to Get Started With the Model\n\nYou can use the model with a pipeline for text classification:" ]
[ "TAGS\n#transformers #pytorch #safetensors #roberta #text-classification #climate #en #dataset-climatebert/climate_specificity #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n", "# Model Card for distilroberta-base-climate-specificity", "## Model Description\n\nThis is the fine-tuned ClimateBERT language model with a classification head for classifying climate-related paragraphs into specific and non-specific paragraphs.\n\nUsing the climatebert/distilroberta-base-climate-f language model as starting point, the distilroberta-base-climate-specificity model is fine-tuned on our climatebert/climate_specificity dataset.\n\n*Note: This model is trained on paragraphs. It may not perform well on sentences.*", "## How to Get Started With the Model\n\nYou can use the model with a pipeline for text classification:" ]
[ 75, 17, 118, 23 ]
[ "passage: TAGS\n#transformers #pytorch #safetensors #roberta #text-classification #climate #en #dataset-climatebert/climate_specificity #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n# Model Card for distilroberta-base-climate-specificity## Model Description\n\nThis is the fine-tuned ClimateBERT language model with a classification head for classifying climate-related paragraphs into specific and non-specific paragraphs.\n\nUsing the climatebert/distilroberta-base-climate-f language model as starting point, the distilroberta-base-climate-specificity model is fine-tuned on our climatebert/climate_specificity dataset.\n\n*Note: This model is trained on paragraphs. It may not perform well on sentences.*## How to Get Started With the Model\n\nYou can use the model with a pipeline for text classification:" ]
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null
null
transformers
# Model Card for distilroberta-base-climate-tcfd ## Model Description This is the fine-tuned ClimateBERT language model with a classification head for classifying climate-related paragraphs into the four TCFD recommendation categories ([fsb-tcfd.org](https://www.fsb-tcfd.org)). Using the [climatebert/distilroberta-base-climate-f](https://huggingface.co/climatebert/distilroberta-base-climate-f) language model as starting point, the distilroberta-base-climate-tcfd model is fine-tuned on our [climatebert/tcfd_recommendations](https://huggingface.co/climatebert/tcfd_recommendations) dataset using only the four recommendation categories (i.e., we remove the non-climate-related class from the dataset). *Note: This model is trained on paragraphs. It may not perform well on sentences.* ## Citation Information ```bibtex @techreport{bingler2023cheaptalk, title={How Cheap Talk in Climate Disclosures Relates to Climate Initiatives, Corporate Emissions, and Reputation Risk}, author={Bingler, Julia and Kraus, Mathias and Leippold, Markus and Webersinke, Nicolas}, type={Working paper}, institution={Available at SSRN 3998435}, year={2023} } ``` ## How to Get Started With the Model You can use the model with a pipeline for text classification: ```python from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline from transformers.pipelines.pt_utils import KeyDataset import datasets from tqdm.auto import tqdm dataset_name = "climatebert/tcfd_recommendations" model_name = "climatebert/distilroberta-base-climate-tcfd" # If you want to use your own data, simply load them as 🤗 Datasets dataset, see https://huggingface.co/docs/datasets/loading dataset = datasets.load_dataset(dataset_name, split="test") model = AutoModelForSequenceClassification.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name, max_len=512) pipe = pipeline("text-classification", model=model, tokenizer=tokenizer, device=0) # See https://huggingface.co/docs/transformers/main_classes/pipelines#transformers.pipeline for out in tqdm(pipe(KeyDataset(dataset, "text"), padding=True, truncation=True)): print(out) ```
{"language": ["en"], "license": "apache-2.0", "tags": ["climate"], "datasets": ["climatebert/tcfd_recommendations"], "metrics": ["accuracy"]}
text-classification
climatebert/distilroberta-base-climate-tcfd
[ "transformers", "pytorch", "safetensors", "roberta", "text-classification", "climate", "en", "dataset:climatebert/tcfd_recommendations", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2022-03-02T23:29:05+00:00
[]
[ "en" ]
TAGS #transformers #pytorch #safetensors #roberta #text-classification #climate #en #dataset-climatebert/tcfd_recommendations #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
# Model Card for distilroberta-base-climate-tcfd ## Model Description This is the fine-tuned ClimateBERT language model with a classification head for classifying climate-related paragraphs into the four TCFD recommendation categories (URL). Using the climatebert/distilroberta-base-climate-f language model as starting point, the distilroberta-base-climate-tcfd model is fine-tuned on our climatebert/tcfd_recommendations dataset using only the four recommendation categories (i.e., we remove the non-climate-related class from the dataset). *Note: This model is trained on paragraphs. It may not perform well on sentences.* ## How to Get Started With the Model You can use the model with a pipeline for text classification:
[ "# Model Card for distilroberta-base-climate-tcfd", "## Model Description\n\nThis is the fine-tuned ClimateBERT language model with a classification head for classifying climate-related paragraphs into the four TCFD recommendation categories (URL).\n\nUsing the climatebert/distilroberta-base-climate-f language model as starting point, the distilroberta-base-climate-tcfd model is fine-tuned on our climatebert/tcfd_recommendations dataset using only the four recommendation categories (i.e., we remove the non-climate-related class from the dataset).\n\n*Note: This model is trained on paragraphs. It may not perform well on sentences.*", "## How to Get Started With the Model\n\nYou can use the model with a pipeline for text classification:" ]
[ "TAGS\n#transformers #pytorch #safetensors #roberta #text-classification #climate #en #dataset-climatebert/tcfd_recommendations #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# Model Card for distilroberta-base-climate-tcfd", "## Model Description\n\nThis is the fine-tuned ClimateBERT language model with a classification head for classifying climate-related paragraphs into the four TCFD recommendation categories (URL).\n\nUsing the climatebert/distilroberta-base-climate-f language model as starting point, the distilroberta-base-climate-tcfd model is fine-tuned on our climatebert/tcfd_recommendations dataset using only the four recommendation categories (i.e., we remove the non-climate-related class from the dataset).\n\n*Note: This model is trained on paragraphs. It may not perform well on sentences.*", "## How to Get Started With the Model\n\nYou can use the model with a pipeline for text classification:" ]
[ 73, 18, 153, 23 ]
[ "passage: TAGS\n#transformers #pytorch #safetensors #roberta #text-classification #climate #en #dataset-climatebert/tcfd_recommendations #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# Model Card for distilroberta-base-climate-tcfd## Model Description\n\nThis is the fine-tuned ClimateBERT language model with a classification head for classifying climate-related paragraphs into the four TCFD recommendation categories (URL).\n\nUsing the climatebert/distilroberta-base-climate-f language model as starting point, the distilroberta-base-climate-tcfd model is fine-tuned on our climatebert/tcfd_recommendations dataset using only the four recommendation categories (i.e., we remove the non-climate-related class from the dataset).\n\n*Note: This model is trained on paragraphs. It may not perform well on sentences.*## How to Get Started With the Model\n\nYou can use the model with a pipeline for text classification:" ]
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