KoichiYasuoka commited on
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1 Parent(s): 9911e21

initial release

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Files changed (6) hide show
  1. README.md +26 -0
  2. config.json +127 -0
  3. pytorch_model.bin +3 -0
  4. special_tokens_map.json +1 -0
  5. tokenizer_config.json +1 -0
  6. vocab.txt +0 -0
README.md ADDED
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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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+ - "wikipedia"
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+ license: "apache-2.0"
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+ pipeline_tag: "token-classification"
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+ ---
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+
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+ # chinese-bert-wwm-ext-upos
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+
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+ ## Model Description
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+
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+ This is a BERT model pre-trained on Chinese Wikipedia texts (both simplified and traditional) for POS-tagging, derived from [chinese-bert-wwm-ext](https://huggingface.co/hfl/chinese-bert-wwm-ext). Every word is tagged by [UPOS](https://universaldependencies.org/u/pos/) (Universal Part-Of-Speech).
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+
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+ ## How to Use
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+
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+ ```py
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+ from transformers import AutoTokenizer,AutoModelForTokenClassification
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+ tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/chinese-bert-wwm-ext-upos")
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+ model=AutoModelForTokenClassification.from_pretrained("KoichiYasuoka/chinese-bert-wwm-ext-upos")
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+ ```
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+
config.json ADDED
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+ {
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+ "architectures": [
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+ "BertForTokenClassification"
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+ ],
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+ "attention_probs_dropout_prob": 0.1,
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+ "classifier_dropout": null,
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+ "directionality": "bidi",
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+ "finetuning_task": "ner",
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+ "gradient_checkpointing": false,
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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-12,
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+ "max_position_embeddings": 512,
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+ "model_type": "bert",
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+ "num_attention_heads": 12,
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+ "num_hidden_layers": 12,
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+ "output_past": true,
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+ "pad_token_id": 0,
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+ "pooler_fc_size": 768,
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+ "pooler_num_attention_heads": 12,
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+ "pooler_num_fc_layers": 3,
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+ "pooler_size_per_head": 128,
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+ "pooler_type": "first_token_transform",
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+ "position_embedding_type": "absolute",
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+ "torch_dtype": "float32",
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+ "transformers_version": "4.10.0",
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+ "type_vocab_size": 2,
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+ "use_cache": true,
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+ "vocab_size": 21128
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+ }
pytorch_model.bin ADDED
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+ size 406933136
special_tokens_map.json ADDED
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+ {"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}
tokenizer_config.json ADDED
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+ {"do_lower_case": true, "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "tokenize_chinese_chars": true, "strip_accents": null, "tokenizer_class": "BertTokenizer"}
vocab.txt ADDED
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