KoichiYasuoka commited on
Commit
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initial release

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README.md ADDED
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+ ---
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+ language:
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+ - "ja"
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+ tags:
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+ - "japanese"
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+ - "token-classification"
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+ - "pos"
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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: "国境の長いトンネルを抜けると雪国であった。"
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+ ---
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+
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+ # Swallow-MS-7b-char-upos
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+
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+ ## Model Description
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+
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+ This is a Mistral model for POS-tagging, derived from [Swallow-MS-7b-v0.1](https://huggingface.co/tokyotech-llm/Swallow-MS-7b-v0.1). Every short-unit-word is tagged by [UPOS](https://universaldependencies.org/u/pos/) (Universal Part-Of-Speech) and [FEATS](https://universaldependencies.org/u/feat/).
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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 pipeline
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+ nlp=pipeline("upos","KoichiYasuoka/Swallow-MS-7b-char-upos",trust_remote_code=True,aggregation_strategy="simple")
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+ print(nlp("国境の長いトンネルを抜けると雪国であった。"))
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+ ```
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+
config.json ADDED
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+ {
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+ "architectures": [
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+ "MistralForTokenClassification"
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+ ],
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+ "attention_bias": false,
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+ "attention_dropout": 0.0,
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+ "auto_map": {
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+ "AutoModelForTokenClassification": "upos.MistralForTokenClassification"
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+ },
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+ "bos_token_id": 1,
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+ "custom_pipelines": {
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+ "upos": {
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+ "impl": "upos.BellmanFordTokenClassificationPipeline",
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+ "pt": "AutoModelForTokenClassification"
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+ }
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+ },
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+ "eos_token_id": 2,
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+ "hidden_act": "silu",
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+ "hidden_size": 4096,
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+ "id2label": {
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+ "0": "ADJ",
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+ "1": "B-ADJ",
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+ "2": "I-ADJ",
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+ "3": "ADJ|Polarity=Neg",
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+ "4": "B-ADJ|Polarity=Neg",
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+ "5": "I-ADJ|Polarity=Neg",
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+ "6": "ADP",
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+ "7": "B-ADP",
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+ "8": "I-ADP",
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+ "9": "ADV",
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+ "10": "B-ADV",
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+ "11": "I-ADV",
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+ "12": "AUX",
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+ "13": "B-AUX",
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+ "14": "I-AUX",
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+ "15": "AUX|Polarity=Neg",
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+ "16": "B-AUX|Polarity=Neg",
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+ "17": "I-AUX|Polarity=Neg",
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+ "18": "CCONJ",
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+ "19": "B-CCONJ",
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+ "20": "I-CCONJ",
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+ "21": "DET",
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+ "22": "B-DET",
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+ "23": "I-DET",
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+ "24": "INTJ",
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+ "25": "B-INTJ",
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+ "26": "I-INTJ",
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+ "27": "NOUN",
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+ "28": "B-NOUN",
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+ "29": "I-NOUN",
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+ "30": "NOUN|Polarity=Neg",
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+ "31": "B-NOUN|Polarity=Neg",
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+ "32": "I-NOUN|Polarity=Neg",
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+ "33": "NUM",
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+ "34": "B-NUM",
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+ "35": "I-NUM",
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+ "36": "PART",
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+ "37": "B-PART",
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+ "38": "I-PART",
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+ "39": "PRON",
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+ "40": "B-PRON",
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+ "41": "I-PRON",
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+ "42": "PROPN",
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+ "43": "B-PROPN",
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+ "44": "I-PROPN",
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+ "45": "PUNCT",
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+ "46": "B-PUNCT",
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+ "47": "I-PUNCT",
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+ "48": "SCONJ",
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+ "49": "B-SCONJ",
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+ "50": "I-SCONJ",
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+ "51": "SYM",
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+ "52": "B-SYM",
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+ "53": "I-SYM",
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+ "54": "VERB",
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+ "55": "B-VERB",
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+ "56": "I-VERB",
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+ "57": "X",
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+ "58": "B-X",
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+ "59": "I-X"
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+ },
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+ "initializer_range": 0.02,
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+ "intermediate_size": 14336,
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+ "label2id": {
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+ "ADJ": 0,
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+ "ADJ|Polarity=Neg": 3,
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+ "ADP": 6,
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+ "ADV": 9,
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+ "AUX": 12,
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+ "AUX|Polarity=Neg": 15,
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+ "B-ADJ": 1,
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+ "B-ADJ|Polarity=Neg": 4,
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+ "B-ADP": 7,
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+ "B-ADV": 10,
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+ "B-AUX": 13,
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+ "B-AUX|Polarity=Neg": 16,
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+ "B-CCONJ": 19,
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+ "B-DET": 22,
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+ "B-INTJ": 25,
100
+ "B-NOUN": 28,
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+ "B-NOUN|Polarity=Neg": 31,
102
+ "B-NUM": 34,
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+ "B-PART": 37,
104
+ "B-PRON": 40,
105
+ "B-PROPN": 43,
106
+ "B-PUNCT": 46,
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+ "B-SCONJ": 49,
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+ "B-SYM": 52,
109
+ "B-VERB": 55,
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+ "B-X": 58,
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+ "CCONJ": 18,
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+ "DET": 21,
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+ "I-ADJ": 2,
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+ "I-ADJ|Polarity=Neg": 5,
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+ "I-ADP": 8,
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+ "I-ADV": 11,
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+ "I-AUX": 14,
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+ "I-AUX|Polarity=Neg": 17,
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+ "I-CCONJ": 20,
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+ "I-DET": 23,
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+ "I-INTJ": 26,
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+ "I-NOUN": 29,
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+ "I-NOUN|Polarity=Neg": 32,
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+ "I-NUM": 35,
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+ "I-PART": 38,
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+ "I-PRON": 41,
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+ "I-PROPN": 44,
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+ "I-PUNCT": 47,
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+ "I-SCONJ": 50,
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+ "I-SYM": 53,
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+ "I-VERB": 56,
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+ "I-X": 59,
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+ "INTJ": 24,
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+ "NOUN": 27,
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+ "NOUN|Polarity=Neg": 30,
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+ "NUM": 33,
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+ "PART": 36,
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+ "PRON": 39,
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+ "PROPN": 42,
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+ "PUNCT": 45,
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+ "SCONJ": 48,
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+ "SYM": 51,
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+ "VERB": 54,
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+ "X": 57
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+ },
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+ "max_position_embeddings": 4096,
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+ "model_type": "mistral",
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+ "num_attention_heads": 32,
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+ "num_hidden_layers": 32,
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+ "num_key_value_heads": 8,
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+ "pretraining_tp": 1,
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+ "rms_norm_eps": 1e-05,
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+ "rope_scaling": null,
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+ "rope_theta": 10000.0,
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+ "sliding_window": 4096,
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+ "tie_word_embeddings": false,
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+ "tokenizer_class": "LlamaTokenizerFast",
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+ "torch_dtype": "float32",
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+ "transformers_version": "4.41.2",
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+ "use_cache": true,
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+ "vocab_size": 43317
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+ }
maker.sh ADDED
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+ #! /bin/sh
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+ test -f ja_gsd_modern.conllu || curl -LO https://github.com/KoichiYasuoka/SuPar-UniDic/raw/main/suparunidic/suparmodels/ja_gsd_modern.conllu
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+ curl -L https://huggingface.co/KoichiYasuoka/Swallow-MS-7b-upos/resolve/main/tokenizer.json | env LANG=ja_JP.utf8 egrep -v '"[ぁ-ん] [ぁ-ん]",$' > newtokenizer.json
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+
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+ TMP=./maker$$.py
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+ cat << 'EOF' > $TMP
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+ #! /usr/bin/env deepspeed
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+ src="KoichiYasuoka/Swallow-MS-7b-upos"
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+ tgt="KoichiYasuoka/Swallow-MS-7b-char-upos"
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+ from transformers import LlamaTokenizerFast,MistralModel,MistralPreTrainedModel,AutoConfig,DataCollatorForTokenClassification,TrainingArguments,Trainer
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+ from transformers.modeling_outputs import TokenClassifierOutput
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+
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+ class MistralForTokenClassification(MistralPreTrainedModel):
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+ def __init__(self,config):
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+ from torch import nn
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+ super().__init__(config)
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+ self.num_labels=config.num_labels
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+ self.model=MistralModel(config)
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+ if hasattr(config,"classifier_dropout") and config.classifier_dropout is not None:
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+ classifier_dropout=config.classifier_dropout
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+ elif hasattr(config,"hidden_dropout") and config.hidden_dropout is not None:
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+ classifier_dropout=config.hidden_dropout
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+ else:
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+ classifier_dropout=0.1
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+ self.dropout=nn.Dropout(classifier_dropout)
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+ self.classifier=nn.Linear(config.hidden_size,config.num_labels)
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+ self.post_init()
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+ def get_input_embeddings(self):
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+ return self.model.embed_tokens
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+ def set_input_embeddings(self,value):
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+ self.model.embed_tokens=value
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+ def forward(self,input_ids=None,past_key_values=None,attention_mask=None,position_ids=None,inputs_embeds=None,labels=None,use_cache=None,output_attentions=None,output_hidden_states=None,return_dict=None):
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+ return_dict=return_dict if return_dict is not None else self.config.use_return_dict
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+ transformer_outputs=self.model(input_ids,past_key_values=past_key_values,attention_mask=attention_mask,position_ids=position_ids,inputs_embeds=inputs_embeds,use_cache=use_cache,output_attentions=output_attentions,output_hidden_states=output_hidden_states,return_dict=return_dict)
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+ hidden_states=transformer_outputs[0]
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+ hidden_states=self.dropout(hidden_states)
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+ logits=self.classifier(hidden_states)
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+ loss=None
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+ if labels is not None:
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+ from torch import nn
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+ loss_fct=nn.CrossEntropyLoss()
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+ loss=loss_fct(logits.view(-1,self.num_labels),labels.view(-1))
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+ if not return_dict:
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+ output=(logits,)+transformer_outputs[1:]
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+ return ((loss,)+output) if loss is not None else output
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+ return TokenClassifierOutput(loss=loss,logits=logits,hidden_states=transformer_outputs.hidden_states,attentions=transformer_outputs.attentions)
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+
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+ class UPOSFileDataset(object):
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+ def __init__(self,conllu,tokenizer):
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+ self.conllu=open(conllu,"r",encoding="utf-8")
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+ self.tokenizer=tokenizer
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+ self.seeks=[0]
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+ self.multiword={}
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+ label=set(["SYM"])
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+ s=self.conllu.readline()
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+ while s!="":
57
+ if s=="\n":
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+ self.seeks.append(self.conllu.tell())
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+ else:
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+ w=s.split("\t")
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+ if len(w)==10:
62
+ if w[0].isdecimal():
63
+ label.add(w[3] if w[5]=="_" else w[3]+"|"+w[5])
64
+ elif w[0].find("-")>0:
65
+ t=w[0].split("-")
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+ f,j,k=w[1],[],[]
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+ for i in range(int(t[0]),int(t[1])+1):
68
+ w=self.conllu.readline().split("\t")
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+ j.append(w[3] if w[5]=="_" else w[3]+"|"+w[5])
70
+ k.append(w[1])
71
+ p="+".join(j)
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+ label.add(p)
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+ if p in self.multiword:
74
+ self.multiword[p][f]=list(k)
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+ else:
76
+ self.multiword[p]={f:list(k)}
77
+ s=self.conllu.readline()
78
+ lid={}
79
+ for i,l in enumerate(sorted(label)):
80
+ lid[l],lid["B-"+l],lid["I-"+l]=i*3,i*3+1,i*3+2
81
+ self.label2id=lid
82
+ def __call__(*args):
83
+ lid={l:i for i,l in enumerate(sorted(set(sum([list(t.label2id) for t in args],[]))))}
84
+ for t in args:
85
+ t.label2id=lid
86
+ return lid
87
+ def __del__(self):
88
+ self.conllu.close()
89
+ __len__=lambda self:len(self.seeks)-1
90
+ def __getitem__(self,i):
91
+ self.conllu.seek(self.seeks[i])
92
+ form,upos=[],[]
93
+ while self.conllu.tell()<self.seeks[i+1]:
94
+ w=self.conllu.readline().split("\t")
95
+ if len(w)==10:
96
+ form.append(w[1])
97
+ if w[0].isdecimal():
98
+ upos.append(w[3] if w[5]=="_" else w[3]+"|"+w[5])
99
+ elif w[0].find("-")>0:
100
+ t=w[0].split("-")
101
+ u=[]
102
+ for j in range(int(t[0]),int(t[1])+1):
103
+ k=self.conllu.readline().split("\t")
104
+ u.append(k[3] if k[5]=="_" else k[3]+"|"+k[5])
105
+ upos.append("+".join(u))
106
+ v=self.tokenizer(form,add_special_tokens=False)
107
+ i,u=[],[]
108
+ for j,(x,y) in enumerate(zip(v["input_ids"],upos)):
109
+ if x!=[]:
110
+ i+=x
111
+ u+=[y] if len(x)==1 else ["B-"+y]+["I-"+y]*(len(x)-1)
112
+ if len(i)<self.tokenizer.model_max_length-3:
113
+ ids=[self.tokenizer.cls_token_id]+i+[self.tokenizer.sep_token_id]
114
+ upos=["SYM"]+u+["SYM"]
115
+ else:
116
+ ids=i[0:self.tokenizer.model_max_length-2]
117
+ upos=u[0:self.tokenizer.model_max_length-2]
118
+ return {"input_ids":ids,"labels":[self.label2id[t] for t in upos]}
119
+
120
+ tkz=LlamaTokenizerFast.from_pretrained(src,tokenizer_file="newtokenizer.json")
121
+ trainDS=UPOSFileDataset("ja_gsd_modern.conllu",tkz)
122
+ lid=trainDS.label2id
123
+ cfg=AutoConfig.from_pretrained(src,num_labels=len(lid),label2id=lid,id2label={i:l for l,i in lid.items()},ignore_mismatched_sizes=True)
124
+ dsp={"fp16":{"enabled":"auto"},"optimizer":{"type":"AdamW"},"scheduler":{"type":"WarmupLR","params":{}},"train_batch_size":"auto","train_micro_batch_size_per_gpu":"auto","zero_optimization":{"stage":3,"offload_optimizer":{"device":"cpu","pin_memory":True},"offload_param":{"device":"cpu","pin_memory":True},"overlap_comm":True,"contiguous_gradients":True,"reduce_bucket_size":"auto","stage3_prefetch_bucket_size":"auto","stage3_param_persistence_threshold":"auto","stage3_gather_16bit_weights_on_model_save":True}}
125
+ arg=TrainingArguments(num_train_epochs=3,per_device_train_batch_size=8,deepspeed=dsp,output_dir=tgt,overwrite_output_dir=True,save_total_limit=2,learning_rate=5e-05,warmup_ratio=0.1,save_safetensors=False)
126
+ trn=Trainer(args=arg,data_collator=DataCollatorForTokenClassification(tkz),model=MistralForTokenClassification.from_pretrained(src,config=cfg,ignore_mismatched_sizes=True),train_dataset=trainDS)
127
+ trn.train()
128
+ trn.save_model(tgt)
129
+ tkz.save_pretrained(tgt)
130
+ EOF
131
+ chmod 755 $TMP
132
+ $TMP
133
+ exit
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+ "model.norm.weight": "pytorch_model-00006-of-00006.bin"
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+ }
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+ }
special_tokens_map.json ADDED
@@ -0,0 +1,51 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "bos_token": {
3
+ "content": "<s>",
4
+ "lstrip": false,
5
+ "normalized": false,
6
+ "rstrip": false,
7
+ "single_word": false
8
+ },
9
+ "cls_token": {
10
+ "content": "<s>",
11
+ "lstrip": false,
12
+ "normalized": false,
13
+ "rstrip": false,
14
+ "single_word": false
15
+ },
16
+ "eos_token": {
17
+ "content": "</s>",
18
+ "lstrip": false,
19
+ "normalized": false,
20
+ "rstrip": false,
21
+ "single_word": false
22
+ },
23
+ "mask_token": {
24
+ "content": "<unk>",
25
+ "lstrip": false,
26
+ "normalized": false,
27
+ "rstrip": false,
28
+ "single_word": false
29
+ },
30
+ "pad_token": {
31
+ "content": "</s>",
32
+ "lstrip": false,
33
+ "normalized": false,
34
+ "rstrip": false,
35
+ "single_word": false
36
+ },
37
+ "sep_token": {
38
+ "content": "<s>",
39
+ "lstrip": false,
40
+ "normalized": false,
41
+ "rstrip": false,
42
+ "single_word": false
43
+ },
44
+ "unk_token": {
45
+ "content": "<unk>",
46
+ "lstrip": false,
47
+ "normalized": false,
48
+ "rstrip": false,
49
+ "single_word": false
50
+ }
51
+ }
tokenizer.json ADDED
The diff for this file is too large to render. See raw diff
 
tokenizer_config.json ADDED
@@ -0,0 +1,42 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "add_bos_token": true,
3
+ "add_eos_token": false,
4
+ "added_tokens_decoder": {
5
+ "0": {
6
+ "content": "<unk>",
7
+ "lstrip": false,
8
+ "normalized": false,
9
+ "rstrip": false,
10
+ "single_word": false,
11
+ "special": true
12
+ },
13
+ "1": {
14
+ "content": "<s>",
15
+ "lstrip": false,
16
+ "normalized": false,
17
+ "rstrip": false,
18
+ "single_word": false,
19
+ "special": true
20
+ },
21
+ "2": {
22
+ "content": "</s>",
23
+ "lstrip": false,
24
+ "normalized": false,
25
+ "rstrip": false,
26
+ "single_word": false,
27
+ "special": true
28
+ }
29
+ },
30
+ "bos_token": "<s>",
31
+ "clean_up_tokenization_spaces": false,
32
+ "cls_token": "<s>",
33
+ "eos_token": "</s>",
34
+ "legacy": true,
35
+ "mask_token": "<unk>",
36
+ "model_max_length": 4096,
37
+ "pad_token": "</s>",
38
+ "sep_token": "<s>",
39
+ "tokenizer_class": "LlamaTokenizer",
40
+ "unk_token": "<unk>",
41
+ "use_default_system_prompt": false
42
+ }
upos.py ADDED
@@ -0,0 +1,76 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ from transformers import TokenClassificationPipeline,MistralModel,MistralPreTrainedModel
2
+ from transformers.modeling_outputs import TokenClassifierOutput
3
+
4
+ class BellmanFordTokenClassificationPipeline(TokenClassificationPipeline):
5
+ def __init__(self,**kwargs):
6
+ import numpy
7
+ super().__init__(**kwargs)
8
+ x=self.model.config.label2id
9
+ y=[k for k in x if not k.startswith("I-")]
10
+ self.transition=numpy.full((len(x),len(x)),numpy.nan)
11
+ for k,v in x.items():
12
+ for j in ["I-"+k[2:]] if k.startswith("B-") else [k]+y if k.startswith("I-") else y:
13
+ self.transition[v,x[j]]=0
14
+ def check_model_type(self,supported_models):
15
+ pass
16
+ def postprocess(self,model_outputs,**kwargs):
17
+ import numpy
18
+ if "logits" not in model_outputs:
19
+ return self.postprocess(model_outputs[0],**kwargs)
20
+ m=model_outputs["logits"][0].numpy()
21
+ e=numpy.exp(m-numpy.max(m,axis=-1,keepdims=True))
22
+ z=e/e.sum(axis=-1,keepdims=True)
23
+ for i in range(m.shape[0]-1,0,-1):
24
+ m[i-1]+=numpy.nanmax(m[i]+self.transition,axis=1)
25
+ k=[numpy.nanargmax(m[0])]
26
+ for i in range(1,m.shape[0]):
27
+ k.append(numpy.nanargmax(m[i]+self.transition[k[-1]]))
28
+ w=[{"entity":self.model.config.id2label[j],"start":s,"end":e,"score":z[i,j]} for i,((s,e),j) in enumerate(zip(model_outputs["offset_mapping"][0].tolist(),k)) if s<e]
29
+ if "aggregation_strategy" in kwargs and kwargs["aggregation_strategy"]!="none":
30
+ for i,t in reversed(list(enumerate(w))):
31
+ p=t.pop("entity")
32
+ if p.startswith("I-"):
33
+ w[i-1]["score"]=min(w[i-1]["score"],t["score"])
34
+ w[i-1]["end"]=w.pop(i)["end"]
35
+ elif p.startswith("B-"):
36
+ t["entity_group"]=p[2:]
37
+ else:
38
+ t["entity_group"]=p
39
+ for t in w:
40
+ t["text"]=model_outputs["sentence"][t["start"]:t["end"]]
41
+ return w
42
+
43
+ class MistralForTokenClassification(MistralPreTrainedModel):
44
+ def __init__(self,config):
45
+ from torch import nn
46
+ super().__init__(config)
47
+ self.num_labels=config.num_labels
48
+ self.model=MistralModel(config)
49
+ if hasattr(config,"classifier_dropout") and config.classifier_dropout is not None:
50
+ classifier_dropout=config.classifier_dropout
51
+ elif hasattr(config,"hidden_dropout") and config.hidden_dropout is not None:
52
+ classifier_dropout=config.hidden_dropout
53
+ else:
54
+ classifier_dropout=0.1
55
+ self.dropout=nn.Dropout(classifier_dropout)
56
+ self.classifier=nn.Linear(config.hidden_size,config.num_labels)
57
+ self.post_init()
58
+ def get_input_embeddings(self):
59
+ return self.model.embed_tokens
60
+ def set_input_embeddings(self,value):
61
+ self.model.embed_tokens=value
62
+ def forward(self,input_ids=None,past_key_values=None,attention_mask=None,position_ids=None,inputs_embeds=None,labels=None,use_cache=None,output_attentions=None,output_hidden_states=None,return_dict=None):
63
+ return_dict=return_dict if return_dict is not None else self.config.use_return_dict
64
+ transformer_outputs=self.model(input_ids,past_key_values=past_key_values,attention_mask=attention_mask,position_ids=position_ids,inputs_embeds=inputs_embeds,use_cache=use_cache,output_attentions=output_attentions,output_hidden_states=output_hidden_states,return_dict=return_dict)
65
+ hidden_states=transformer_outputs[0]
66
+ hidden_states=self.dropout(hidden_states)
67
+ logits=self.classifier(hidden_states)
68
+ loss=None
69
+ if labels is not None:
70
+ from torch import nn
71
+ loss_fct=nn.CrossEntropyLoss()
72
+ loss=loss_fct(logits.view(-1,self.num_labels),labels.view(-1))
73
+ if not return_dict:
74
+ output=(logits,)+transformer_outputs[2:]
75
+ return ((loss,)+output) if loss is not None else output
76
+ return TokenClassifierOutput(loss=loss,logits=logits,hidden_states=transformer_outputs.hidden_states,attentions=transformer_outputs.attentions)