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initial release

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Files changed (8) hide show
  1. README.md +48 -3
  2. config.json +121 -0
  3. pytorch_model.bin +3 -0
  4. special_tokens_map.json +1 -0
  5. supar.model +3 -0
  6. tokenizer.json +1 -0
  7. tokenizer_config.json +1 -0
  8. vocab.txt +2727 -0
README.md CHANGED
@@ -1,3 +1,48 @@
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- ---
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- license: apache-2.0
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- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ language:
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+ - "th"
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+ tags:
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+ - "thai"
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+ - "token-classification"
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+ - "pos"
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+ - "wikipedia"
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+ - "dependency-parsing"
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+ datasets:
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+ - "universal_dependencies"
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+ license: "apache-2.0"
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+ pipeline_tag: "token-classification"
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+ widget:
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+ - text: "หลายหัวดีกว่าหัวเดียว"
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+ ---
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+
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+ # roberta-base-thai-char-upos
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+
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+ ## Model Description
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+
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+ This is a RoBERTa model pre-trained on Thai Wikipedia texts for POS-tagging and dependency-parsing, derived from [roberta-base-thai-char](https://huggingface.co/KoichiYasuoka/roberta-base-thai-char). 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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+ import torch
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+ from transformers import AutoTokenizer,AutoModelForTokenClassification
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+ tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/roberta-base-thai-char-upos")
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+ model=AutoModelForTokenClassification.from_pretrained("KoichiYasuoka/roberta-base-thai-char-upos")
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+ s="หลายหัวดีกว่าหัวเดียว"
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+ t=tokenizer.tokenize(s)
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+ p=[model.config.id2label[q] for q in torch.argmax(model(tokenizer.encode(s,return_tensors="pt"))["logits"],dim=2)[0].tolist()[1:-1]]
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+ print(list(zip(t,p)))
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+ ```
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+
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+ or
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+
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+ ```
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+ import esupar
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+ nlp=esupar.load("KoichiYasuoka/roberta-base-thai-char-upos")
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+ print(nlp("หลายหัวดีกว่าหัวเดียว"))
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+ ```
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+
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+ ## See Also
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+
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+ [esupar](https://github.com/KoichiYasuoka/esupar): Tokenizer POS-tagger and Dependency-parser with BERT/RoBERTa models
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+
config.json ADDED
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+ {
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+ "architectures": [
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+ "RobertaForTokenClassification"
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+ ],
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+ "attention_probs_dropout_prob": 0.1,
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+ "bos_token_id": 0,
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+ "classifier_dropout": null,
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+ "eos_token_id": 2,
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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": "ADP",
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+ "1": "ADV",
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+ "2": "B-ADJ",
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+ "3": "B-ADP",
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+ "4": "B-ADV",
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+ "5": "B-AUX",
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+ "6": "B-CCONJ",
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+ "7": "B-DET",
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+ "8": "B-INTJ",
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+ "9": "B-NOUN",
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+ "10": "B-NUM",
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+ "11": "B-PART",
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+ "12": "B-PRON",
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+ "13": "B-PROPN",
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+ "14": "B-PUNCT",
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+ "15": "B-SCONJ",
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+ "16": "B-VERB",
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+ "17": "B-X",
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+ "18": "CCONJ",
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+ "19": "DET",
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+ "20": "I-ADJ",
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+ "21": "I-ADP",
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+ "22": "I-ADV",
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+ "23": "I-AUX",
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+ "24": "I-CCONJ",
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+ "25": "I-DET",
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+ "26": "I-INTJ",
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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-SCONJ",
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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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+ "ADP": 0,
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+ "ADV": 1,
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+ "B-ADJ": 2,
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+ "B-ADP": 3,
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+ "B-ADV": 4,
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+ "B-AUX": 5,
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+ "B-CCONJ": 6,
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+ "B-DET": 7,
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+ "B-INTJ": 8,
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+ "B-NOUN": 9,
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+ "B-NUM": 10,
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+ "B-PART": 11,
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+ "B-PRON": 12,
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+ "B-PROPN": 13,
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+ "B-PUNCT": 14,
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+ "B-SCONJ": 15,
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+ "B-VERB": 16,
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+ "B-X": 17,
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+ "CCONJ": 18,
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+ "DET": 19,
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+ "I-ADJ": 20,
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+ "I-ADP": 21,
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+ "I-ADV": 22,
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+ "I-AUX": 23,
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+ "I-CCONJ": 24,
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+ "I-DET": 25,
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+ "I-INTJ": 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-SCONJ": 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": "roberta",
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+ "num_attention_heads": 12,
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+ "num_hidden_layers": 12,
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+ "pad_token_id": 1,
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+ "position_embedding_type": "absolute",
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+ "tokenizer_class": "BertTokenizerFast",
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+ "torch_dtype": "float32",
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+ "transformers_version": "4.14.1",
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+ "type_vocab_size": 2,
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+ "use_cache": true,
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+ "vocab_size": 2727
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+ }
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