KoichiYasuoka
commited on
Commit
β’
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Parent(s):
9a5e901
initial release
Browse files- README.md +39 -0
- config.json +123 -0
- pytorch_model.bin +3 -0
- special_tokens_map.json +7 -0
- supar.model +3 -0
- tokenizer.json +0 -0
- tokenizer_config.json +20 -0
- vocab.txt +0 -0
README.md
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---
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language:
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- "zh"
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tags:
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- "chinese"
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- "token-classification"
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- "pos"
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- "dependency-parsing"
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datasets:
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- "universal_dependencies"
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license: "cc-by-sa-4.0"
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pipeline_tag: "token-classification"
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---
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# deberta-base-chinese-upos
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## Model Description
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This is a DeBERTa(V2) model pre-trained on Chinese Wikipedia texts (both simplified and traditional) for POS-tagging and dependency-parsing, derived from [deberta-base-chinese](https://huggingface.co/KoichiYasuoka/deberta-base-chinese). Every word is tagged by [UPOS](https://universaldependencies.org/u/pos/) (Universal Part-Of-Speech).
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## How to Use
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```py
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from transformers import AutoTokenizer,AutoModelForTokenClassification
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tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/deberta-base-chinese-upos")
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model=AutoModelForTokenClassification.from_pretrained("KoichiYasuoka/deberta-base-chinese-upos")
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```
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or
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```py
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import esupar
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nlp=esupar.load("KoichiYasuoka/deberta-base-chinese-upos")
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```
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## See Also
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[esupar](https://github.com/KoichiYasuoka/esupar): Tokenizer POS-tagger and Dependency-parser with BERT/RoBERTa/DeBERTa models
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config.json
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{
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"architectures": [
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"DebertaV2ForTokenClassification"
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],
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"attention_probs_dropout_prob": 0.1,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 768,
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"id2label": {
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"0": "ADJ",
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"1": "ADP",
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"2": "ADV",
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"3": "AUX",
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"4": "B-ADJ",
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"5": "B-ADP",
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"6": "B-ADV",
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"7": "B-AUX",
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"8": "B-CCONJ",
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"9": "B-DET",
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"10": "B-NOUN",
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"11": "B-NUM",
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"12": "B-PART",
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"13": "B-PRON",
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"14": "B-PROPN",
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"15": "B-PUNCT",
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"16": "B-SYM",
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"17": "B-VERB",
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"18": "B-X",
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"19": "CCONJ",
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"20": "DET",
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"21": "I-ADJ",
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"22": "I-ADP",
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"23": "I-ADV",
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"24": "I-AUX",
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"25": "I-CCONJ",
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"26": "I-DET",
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"27": "I-NOUN",
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"28": "I-NUM",
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"29": "I-PART",
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"30": "I-PRON",
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"31": "I-PROPN",
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"32": "I-PUNCT",
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"33": "I-SYM",
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"34": "I-VERB",
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"35": "I-X",
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"36": "NOUN",
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"37": "NUM",
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"38": "PART",
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"39": "PRON",
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"40": "PROPN",
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"41": "PUNCT",
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"42": "SYM",
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"43": "VERB",
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"44": "X"
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},
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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"label2id": {
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"ADJ": 0,
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"ADP": 1,
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"ADV": 2,
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"AUX": 3,
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"B-ADJ": 4,
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"B-ADP": 5,
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"B-ADV": 6,
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"B-AUX": 7,
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"B-CCONJ": 8,
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"B-DET": 9,
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"B-NOUN": 10,
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"B-NUM": 11,
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"B-PART": 12,
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"B-PRON": 13,
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"B-PROPN": 14,
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"B-PUNCT": 15,
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"B-SYM": 16,
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"B-VERB": 17,
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"B-X": 18,
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"CCONJ": 19,
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"DET": 20,
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"I-ADJ": 21,
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"I-ADP": 22,
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"I-ADV": 23,
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"I-AUX": 24,
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"I-CCONJ": 25,
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"I-DET": 26,
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"I-NOUN": 27,
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"I-NUM": 28,
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"I-PART": 29,
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"I-PRON": 30,
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"I-PROPN": 31,
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"I-PUNCT": 32,
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"I-SYM": 33,
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"I-VERB": 34,
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"I-X": 35,
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"NOUN": 36,
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"NUM": 37,
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"PART": 38,
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"PRON": 39,
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"PROPN": 40,
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"PUNCT": 41,
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"SYM": 42,
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"VERB": 43,
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"X": 44
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},
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"layer_norm_eps": 1e-07,
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"max_position_embeddings": 512,
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"max_relative_positions": -1,
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"model_type": "deberta-v2",
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"num_attention_heads": 12,
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"num_hidden_layers": 12,
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"pad_token_id": 0,
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"pooler_dropout": 0,
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"pooler_hidden_act": "gelu",
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"pooler_hidden_size": 768,
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"pos_att_type": null,
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"position_biased_input": true,
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"relative_attention": false,
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"tokenizer_class": "BertTokenizerFast",
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"torch_dtype": "float32",
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"transformers_version": "4.22.1",
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"type_vocab_size": 0,
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"vocab_size": 26582
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}
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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:a50326cd8550f74be43b2f68234c4b772c9c81c4c8287283274f0f88f7f21009
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size 423666163
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special_tokens_map.json
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{
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"cls_token": "[CLS]",
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"mask_token": "[MASK]",
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"pad_token": "[PAD]",
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"sep_token": "[SEP]",
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"unk_token": "[UNK]"
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}
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supar.model
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version https://git-lfs.github.com/spec/v1
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oid sha256:ca39f3787e2d0797e32b24001c6570d55d716421d22367350ecdf7a507003545
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size 476345963
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tokenizer.json
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tokenizer_config.json
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{
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"cls_token": "[CLS]",
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"do_basic_tokenize": true,
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"do_lower_case": false,
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"mask_token": "[MASK]",
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"model_max_length": 512,
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"never_split": [
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"[CLS]",
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"[PAD]",
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"[SEP]",
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"[UNK]",
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"[MASK]"
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],
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"pad_token": "[PAD]",
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"sep_token": "[SEP]",
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"strip_accents": false,
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"tokenize_chinese_chars": true,
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"tokenizer_class": "BertTokenizerFast",
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"unk_token": "[UNK]"
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}
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vocab.txt
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