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KoichiYasuoka
/
lfm2-350m-japanese-ud-embeds

Token Classification
PyTorch
Japanese
lfm2
japanese
pos
dependency-parsing
custom_code
Model card Files Community
KoichiYasuoka commited on
Commit
3f9f094
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1 Parent(s): 0fefe30

initial release

Browse files
Files changed (9) hide show
  1. README.md +30 -0
  2. config.json +533 -0
  3. lfm2.py +5 -0
  4. maker.py +115 -0
  5. pytorch_model.bin +3 -0
  6. special_tokens_map.json +23 -0
  7. tokenizer.json +0 -0
  8. tokenizer_config.json +4079 -0
  9. ud.py +166 -0
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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+ - "pos"
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+ - "dependency-parsing"
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+ base_model: LiquidAI/LFM2-350M
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+ datasets:
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+ - "universal_dependencies"
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+ license: "lfm1.0"
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+ pipeline_tag: "token-classification"
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+ widget:
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+ - text: "全学年にわたって小学校の国語の教科書に挿し絵が用いられている"
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+ ---
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+
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+ # lfm2-350m-japanese-ud-embeds
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+
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+ ## Model Description
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+
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+ This is a LFM2 model pretrained for POS-tagging and dependency-parsing, derived from [LFM2-350M](https://huggingface.co/LiquidAI/LFM2-350M) and [UD_Japanese-GSDLUW](https://github.com/UniversalDependencies/UD_Japanese-GSDLUW).
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+
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+ ## How to Use
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+
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+ ```
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+ from transformers import pipeline
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+ nlp=pipeline("universal-dependencies","KoichiYasuoka/lfm2-350m-japanese-ud-embeds",trust_remote_code=True)
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+ print(nlp("全学年にわたって小学校の国語の教科書に挿し絵が用いられている"))
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+ ```
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+
config.json ADDED
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1
+ {
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+ "architectures": [
3
+ "Lfm2ForTokenClassification"
4
+ ],
5
+ "auto_map": {
6
+ "AutoModelForTokenClassification": "lfm2.Lfm2ForTokenClassification"
7
+ },
8
+ "block_auto_adjust_ff_dim": true,
9
+ "block_dim": 1024,
10
+ "block_ff_dim": 6656,
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+ "block_ffn_dim_multiplier": 1.0,
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+ "block_mlp_init_scale": 1.0,
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+ "block_multiple_of": 256,
14
+ "block_norm_eps": 1e-05,
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+ "block_out_init_scale": 1.0,
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+ "block_use_swiglu": true,
17
+ "block_use_xavier_init": true,
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+ "bos_token_id": 1,
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+ "conv_L_cache": 3,
20
+ "conv_bias": false,
21
+ "conv_dim": 1024,
22
+ "conv_dim_out": 1024,
23
+ "conv_use_xavier_init": true,
24
+ "custom_pipelines": {
25
+ "upos": {
26
+ "impl": "ud.BellmanFordTokenClassificationPipeline",
27
+ "pt": "AutoModelForTokenClassification"
28
+ },
29
+ "universal-dependencies": {
30
+ "impl": "ud.UniversalDependenciesPipeline",
31
+ "pt": "AutoModelForTokenClassification"
32
+ }
33
+ },
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+ "eos_token_id": 7,
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+ "hidden_size": 1024,
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+ "id2label": {
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+ "0": "ADJ",
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+ "1": "ADJ.",
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+ "2": "ADJ|_",
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+ "3": "ADJ|l-acl",
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+ "4": "ADJ|l-advcl",
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+ "5": "ADJ|l-amod",
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+ "6": "ADJ|l-ccomp",
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+ "7": "ADJ|l-csubj",
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+ "8": "ADJ|l-csubj:outer",
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+ "9": "ADJ|l-nmod",
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+ "10": "ADJ|l-nsubj",
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+ "11": "ADJ|l-obj",
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+ "12": "ADJ|l-obl",
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+ "13": "ADJ|r-acl",
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+ "14": "ADJ|r-amod",
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+ "15": "ADJ|r-dep",
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+ "16": "ADJ|root",
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+ "17": "ADP",
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+ "18": "ADP.",
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+ "19": "ADP|_",
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+ "20": "ADP|l-case",
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+ "21": "ADP|r-case",
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+ "22": "ADP|r-fixed",
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+ "23": "ADV",
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+ "24": "ADV.",
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+ "25": "ADV|_",
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+ "26": "ADV|l-advcl",
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+ "27": "ADV|l-advmod",
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+ "28": "ADV|l-obj",
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+ "29": "ADV|r-dep",
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+ "30": "ADV|root",
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+ "31": "AUX",
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+ "32": "AUX.",
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+ "33": "AUX|Polarity=Neg|_",
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+ "34": "AUX|Polarity=Neg|r-aux",
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+ "35": "AUX|Polarity=Neg|r-fixed",
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+ "36": "AUX|_",
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+ "37": "AUX|r-aux",
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+ "38": "AUX|r-cop",
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+ "39": "AUX|r-fixed",
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+ "40": "AUX|root",
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+ "41": "B-ADJ",
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+ "42": "B-ADJ.",
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+ "43": "B-ADP",
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+ "44": "B-ADP.",
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+ "45": "B-ADV",
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+ "46": "B-ADV.",
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+ "47": "B-AUX",
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+ "48": "B-AUX.",
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+ "49": "B-CCONJ",
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+ "50": "B-CCONJ.",
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+ "51": "B-DET",
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+ "52": "B-DET.",
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+ "53": "B-INTJ",
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+ "54": "B-INTJ.",
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+ "55": "B-NOUN",
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+ "56": "B-NOUN.",
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+ "57": "B-NUM",
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+ "58": "B-NUM.",
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+ "59": "B-PART",
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+ "60": "B-PART.",
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+ "61": "B-PRON",
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+ "62": "B-PRON.",
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+ "63": "B-PROPN",
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+ "64": "B-PROPN.",
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+ "65": "B-PUNCT",
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+ "66": "B-PUNCT.",
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+ "67": "B-SCONJ",
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+ "68": "B-SCONJ.",
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+ "69": "B-SYM",
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+ "70": "B-SYM.",
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+ "71": "B-VERB",
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+ "72": "B-VERB.",
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+ "73": "B-X",
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+ "74": "B-X.",
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+ "75": "CCONJ",
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+ "76": "CCONJ.",
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+ "77": "CCONJ|_",
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+ "78": "CCONJ|l-cc",
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+ "79": "CCONJ|r-cc",
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+ "80": "DET",
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+ "81": "DET.",
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+ "82": "DET|_",
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+ "83": "DET|l-det",
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+ "84": "I-ADJ",
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+ "85": "I-ADJ.",
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+ "86": "I-ADP",
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+ "87": "I-ADP.",
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+ "88": "I-ADV",
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+ "89": "I-ADV.",
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+ "90": "I-AUX",
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+ "91": "I-AUX.",
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+ "92": "I-CCONJ",
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+ "93": "I-CCONJ.",
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+ "94": "I-DET",
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+ "96": "I-INTJ",
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+ "97": "I-INTJ.",
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+ "99": "I-NOUN.",
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+ "101": "I-NUM.",
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+ "102": "I-PART",
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+ "103": "I-PART.",
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+ "104": "I-PRON",
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+ "105": "I-PRON.",
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+ "106": "I-PROPN",
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+ "107": "I-PROPN.",
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+ "108": "I-PUNCT",
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+ "109": "I-PUNCT.",
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+ "110": "I-SCONJ",
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+ "111": "I-SCONJ.",
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+ "112": "I-SYM",
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+ "113": "I-SYM.",
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+ "114": "I-VERB",
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+ "115": "I-VERB.",
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+ "116": "I-X",
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+ "117": "I-X.",
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+ "118": "INTJ",
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+ "119": "INTJ.",
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+ "120": "INTJ|_",
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+ "121": "INTJ|l-discourse",
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+ "123": "INTJ|root",
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+ "124": "NOUN",
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+ "125": "NOUN.",
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+ "126": "NOUN|Polarity=Neg|_",
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+ "128": "NOUN|Polarity=Neg|root",
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+ "129": "NOUN|_",
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+ "130": "NOUN|l-acl",
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+ "131": "NOUN|l-advcl",
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+ "133": "NOUN|l-compound",
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+ "134": "NOUN|l-csubj",
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+ "135": "NOUN|l-csubj:outer",
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+ "136": "NOUN|l-nmod",
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+ "137": "NOUN|l-nsubj",
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+ "138": "NOUN|l-nsubj:outer",
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+ "140": "NOUN|l-obl",
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+ "145": "NUM",
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+ "149": "NUM|l-compound",
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+ "150": "NUM|l-nmod",
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+ "151": "NUM|l-nsubj",
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+ "152": "NUM|l-nsubj:outer",
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+ "153": "NUM|l-nummod",
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+ "154": "NUM|l-obj",
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+ "159": "PART.",
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+ "160": "PART|_",
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+ "163": "PRON",
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+ "164": "PRON.",
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+ "165": "PRON|_",
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+ "166": "PRON|l-acl",
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+ "167": "PRON|l-advcl",
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+ "168": "PRON|l-nmod",
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+ "169": "PRON|l-nsubj",
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+ "170": "PRON|l-nsubj:outer",
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+ "171": "PRON|l-obj",
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+ "172": "PRON|l-obl",
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+ "175": "PROPN.",
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+ "179": "PROPN|l-compound",
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+ "181": "PROPN|l-nsubj",
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+ "182": "PROPN|l-nsubj:outer",
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+ "184": "PROPN|l-obl",
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+ "185": "PROPN|r-compound",
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+ "192": "PUNCT|r-punct",
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+ "196": "SCONJ|l-dep",
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+ "199": "SYM",
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+ "205": "SYM|l-obl",
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+ "206": "SYM|r-compound",
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+ "PROPN|_": 176,
447
+ "PROPN|l-acl": 177,
448
+ "PROPN|l-advcl": 178,
449
+ "PROPN|l-compound": 179,
450
+ "PROPN|l-nmod": 180,
451
+ "PROPN|l-nsubj": 181,
452
+ "PROPN|l-nsubj:outer": 182,
453
+ "PROPN|l-obj": 183,
454
+ "PROPN|l-obl": 184,
455
+ "PROPN|r-compound": 185,
456
+ "PROPN|r-nmod": 186,
457
+ "PROPN|root": 187,
458
+ "PUNCT": 188,
459
+ "PUNCT.": 189,
460
+ "PUNCT|_": 190,
461
+ "PUNCT|l-punct": 191,
462
+ "PUNCT|r-punct": 192,
463
+ "SCONJ": 193,
464
+ "SCONJ.": 194,
465
+ "SCONJ|_": 195,
466
+ "SCONJ|l-dep": 196,
467
+ "SCONJ|r-fixed": 197,
468
+ "SCONJ|r-mark": 198,
469
+ "SYM": 199,
470
+ "SYM.": 200,
471
+ "SYM|_": 201,
472
+ "SYM|l-compound": 202,
473
+ "SYM|l-dep": 203,
474
+ "SYM|l-nmod": 204,
475
+ "SYM|l-obl": 205,
476
+ "SYM|r-compound": 206,
477
+ "SYM|r-dep": 207,
478
+ "VERB": 208,
479
+ "VERB.": 209,
480
+ "VERB|_": 210,
481
+ "VERB|l-acl": 211,
482
+ "VERB|l-advcl": 212,
483
+ "VERB|l-ccomp": 213,
484
+ "VERB|l-compound": 214,
485
+ "VERB|l-csubj": 215,
486
+ "VERB|l-csubj:outer": 216,
487
+ "VERB|l-nmod": 217,
488
+ "VERB|l-obj": 218,
489
+ "VERB|l-obl": 219,
490
+ "VERB|r-acl": 220,
491
+ "VERB|r-advcl": 221,
492
+ "VERB|r-compound": 222,
493
+ "VERB|root": 223,
494
+ "X": 224,
495
+ "X.": 225,
496
+ "X|_": 226,
497
+ "X|l-nmod": 227,
498
+ "X|r-dep": 228
499
+ },
500
+ "layer_types": [
501
+ "conv",
502
+ "conv",
503
+ "full_attention",
504
+ "conv",
505
+ "conv",
506
+ "full_attention",
507
+ "conv",
508
+ "conv",
509
+ "full_attention",
510
+ "conv",
511
+ "full_attention",
512
+ "conv",
513
+ "full_attention",
514
+ "conv",
515
+ "full_attention",
516
+ "conv"
517
+ ],
518
+ "max_position_embeddings": 128000,
519
+ "model_type": "lfm2",
520
+ "norm_eps": 1e-05,
521
+ "num_attention_heads": 16,
522
+ "num_heads": 16,
523
+ "num_hidden_layers": 16,
524
+ "num_key_value_heads": 8,
525
+ "pad_token_id": 0,
526
+ "rope_theta": 1000000.0,
527
+ "tokenizer_class": "PreTrainedTokenizerFast",
528
+ "torch_dtype": "float32",
529
+ "transformers_version": "4.54.0",
530
+ "use_cache": true,
531
+ "use_pos_enc": true,
532
+ "vocab_size": 65536
533
+ }
lfm2.py ADDED
@@ -0,0 +1,5 @@
 
 
 
 
 
 
1
+ from transformers import Lfm2PreTrainedModel
2
+ from transformers.modeling_layers import GenericForTokenClassification
3
+
4
+ class Lfm2ForTokenClassification(GenericForTokenClassification,Lfm2PreTrainedModel):
5
+ pass
maker.py ADDED
@@ -0,0 +1,115 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ #! /usr/bin/python3
2
+ import os
3
+ src="LiquidAI/LFM2-350M"
4
+ tgt="KoichiYasuoka/lfm2-350m-japanese-ud-embeds"
5
+ url="https://github.com/UniversalDependencies/UD_Japanese-GSDLUW"
6
+ d=os.path.basename(url)
7
+ os.system(f"test -d {d} || git clone --depth=1 {url}")
8
+ os.system(f"for F in train dev test ; do cp {d}/*-$F.conllu $F.conllu ; done")
9
+ class UDEmbedsDataset(object):
10
+ def __init__(self,conllu,tokenizer,oldtokenizer=None,embeddings=None):
11
+ self.conllu=open(conllu,"r",encoding="utf-8")
12
+ self.tokenizer=tokenizer
13
+ self.oldtokenizer=oldtokenizer if oldtokenizer else tokenizer
14
+ self.embeddings=embeddings
15
+ self.seeks=[0]
16
+ label=set(["SYM","SYM.","SYM|_"])
17
+ dep=set()
18
+ s=self.conllu.readline()
19
+ while s!="":
20
+ if s=="\n":
21
+ self.seeks.append(self.conllu.tell())
22
+ else:
23
+ w=s.split("\t")
24
+ if len(w)==10:
25
+ if w[0].isdecimal():
26
+ p=w[3]
27
+ q="" if w[5]=="_" else "|"+w[5]
28
+ d=("|" if w[6]=="0" else "|l-" if int(w[0])<int(w[6]) else "|r-")+w[7]
29
+ for k in [p,p+".","B-"+p,"B-"+p+".","I-"+p,"I-"+p+".",p+q+"|_",p+q+d]:
30
+ label.add(k)
31
+ s=self.conllu.readline()
32
+ self.label2id={l:i for i,l in enumerate(sorted(label))}
33
+ def __call__(*args):
34
+ lid={l:i for i,l in enumerate(sorted(set(sum([list(t.label2id) for t in args],[]))))}
35
+ for t in args:
36
+ t.label2id=lid
37
+ return lid
38
+ def __del__(self):
39
+ self.conllu.close()
40
+ __len__=lambda self:len(self.seeks)-1
41
+ def __getitem__(self,i):
42
+ import torch
43
+ self.conllu.seek(self.seeks[i])
44
+ c,t,s=[],[""],False
45
+ while t[0]!="\n":
46
+ t=self.conllu.readline().split("\t")
47
+ if len(t)==10 and t[0].isdecimal():
48
+ if s:
49
+ t[1]=" "+t[1]
50
+ c.append(t)
51
+ s=t[9].find("SpaceAfter=No")<0
52
+ x=[True if t[6]=="0" or int(t[6])>j or sum([1 if int(c[i][6])==j+1 else 0 for i in range(j+1,len(c))])>0 else False for j,t in enumerate(c)]
53
+ v=self.tokenizer([t[1] for t in c],add_special_tokens=False)["input_ids"]
54
+ ids,upos=[self.tokenizer.bos_token_id],["SYM."]
55
+ for i,(j,k) in enumerate(zip(v,c)):
56
+ if j==[]:
57
+ j=[self.tokenizer.unk_token_id]
58
+ p=k[3] if x[i] else k[3]+"."
59
+ ids+=j
60
+ upos+=[p] if len(j)==1 else ["B-"+p]+["I-"+p]*(len(j)-1)
61
+ x=[True if t[6]=="0" or int(t[6])>j or sum([1 if int(c[i][6])==j+1 else 0 for i in range(j+1,len(c))])>0 else False for j,t in enumerate(c)]
62
+ if len(x)<504:
63
+ x=[True]*len(x)
64
+ else:
65
+ w=sum([len(x)-i+1 if b else 0 for i,b in enumerate(x)])+len(ids)+1
66
+ for i in range(len(x)):
67
+ if x[i]==False and w+len(x)-i<128000:
68
+ x[i]=True
69
+ w+=len(x)-i+1
70
+ v=self.oldtokenizer([t[1] for t in c],add_special_tokens=False)["input_ids"]
71
+ p=[t[3] if t[5]=="_" else t[3]+"|"+t[5] for i,t in enumerate(c)]
72
+ d=[t[7] if t[6]=="0" else "l-"+t[7] if int(t[0])<int(t[6]) else "r-"+t[7] for t in c]
73
+ idx=[-1]
74
+ upos.append("SYM|_")
75
+ for i in range(len(x)):
76
+ if x[i]:
77
+ idx.append(i)
78
+ upos.append(p[i]+"|"+d[i] if c[i][6]=="0" else p[i]+"|_")
79
+ for j in range(i+1,len(x)):
80
+ idx.append(j)
81
+ upos.append(p[j]+"|"+d[j] if int(c[j][6])==i+1 else p[i]+"|"+d[i] if int(c[i][6])==j+1 else p[j]+"|_")
82
+ idx.append(-1)
83
+ upos.append("SYM|_")
84
+ with torch.no_grad():
85
+ m=[]
86
+ for j in v:
87
+ if j==[]:
88
+ j=[self.tokenizer.unk_token_id]
89
+ m.append(self.embeddings[j,:].sum(axis=0))
90
+ m.append(self.embeddings[self.tokenizer.eos_token_id])
91
+ emb=torch.stack(m)
92
+ return{"inputs_embeds":torch.vstack((self.embeddings[ids,:],emb[idx,:])),"labels":[self.label2id[p] for p in upos]}
93
+ from transformers import AutoTokenizer,AutoConfig,Lfm2PreTrainedModel,DefaultDataCollator,TrainingArguments,Trainer
94
+ from transformers.modeling_layers import GenericForTokenClassification
95
+ class Lfm2ForTokenClassification(GenericForTokenClassification,Lfm2PreTrainedModel):
96
+ pass
97
+ from tokenizers.pre_tokenizers import Sequence,Split,Whitespace,Punctuation
98
+ from tokenizers import Regex
99
+ from copy import deepcopy
100
+ otk=AutoTokenizer.from_pretrained(src,unk_token="<|endoftext|>")
101
+ otk.model_input_names=["input_ids","attention_mask"]
102
+ ntk=deepcopy(otk)
103
+ ntk.backend_tokenizer.pre_tokenizer=Sequence([Split(Regex("[ぁ-ん]"),"isolated"),Whitespace(),Punctuation(),otk.backend_tokenizer.pre_tokenizer])
104
+ trainDS=UDEmbedsDataset("train.conllu",ntk,otk)
105
+ devDS=UDEmbedsDataset("dev.conllu",ntk,otk)
106
+ testDS=UDEmbedsDataset("test.conllu",ntk,otk)
107
+ lid=trainDS(devDS,testDS)
108
+ cfg=AutoConfig.from_pretrained(src,num_labels=len(lid),label2id=lid,id2label={i:l for l,i in lid.items()},ignore_mismatched_sizes=True,trust_remote_code=True)
109
+ mdl=Lfm2ForTokenClassification.from_pretrained(src,config=cfg,ignore_mismatched_sizes=True,trust_remote_code=True)
110
+ trainDS.embeddings=mdl.get_input_embeddings().weight
111
+ arg=TrainingArguments(num_train_epochs=3,per_device_train_batch_size=1,dataloader_pin_memory=False,output_dir=tgt,overwrite_output_dir=True,save_total_limit=2,learning_rate=5e-05,warmup_ratio=0.1,save_safetensors=False)
112
+ trn=Trainer(args=arg,data_collator=DefaultDataCollator(),model=mdl,train_dataset=trainDS)
113
+ trn.train()
114
+ trn.save_model(tgt)
115
+ otk.save_pretrained(tgt)
pytorch_model.bin ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:31f1a1aedbdafb2390308d89af6ba632c559ee3c72a5ea95954fdccc2fddff61
3
+ size 1418925026
special_tokens_map.json ADDED
@@ -0,0 +1,23 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "bos_token": {
3
+ "content": "<|startoftext|>",
4
+ "lstrip": false,
5
+ "normalized": false,
6
+ "rstrip": false,
7
+ "single_word": false
8
+ },
9
+ "eos_token": {
10
+ "content": "<|im_end|>",
11
+ "lstrip": false,
12
+ "normalized": false,
13
+ "rstrip": false,
14
+ "single_word": false
15
+ },
16
+ "pad_token": {
17
+ "content": "<|pad|>",
18
+ "lstrip": false,
19
+ "normalized": false,
20
+ "rstrip": false,
21
+ "single_word": false
22
+ }
23
+ }
tokenizer.json ADDED
The diff for this file is too large to render. See raw diff
 
tokenizer_config.json ADDED
@@ -0,0 +1,4079 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3999
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4000
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4016
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4017
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4018
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4019
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+ },
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+ "64396": {
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+ },
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4043
+ "special": true
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+ },
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+ "64398": {
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+ "content": "<|file_start|>",
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+ "lstrip": false,
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4051
+ "special": true
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+ },
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+ "64399": {
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+ "lstrip": false,
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+ "normalized": false,
4057
+ "rstrip": false,
4058
+ "single_word": false,
4059
+ "special": true
4060
+ }
4061
+ },
4062
+ "bos_token": "<|startoftext|>",
4063
+ "clean_up_tokenization_spaces": true,
4064
+ "eos_token": "<|im_end|>",
4065
+ "extra_special_tokens": {},
4066
+ "legacy": false,
4067
+ "model_max_length": 1000000000000000019884624838656,
4068
+ "model_input_names": [
4069
+ "input_ids",
4070
+ "attention_mask"
4071
+ ],
4072
+ "pad_token": "<|pad|>",
4073
+ "sp_model_kwargs": {},
4074
+ "spaces_between_special_tokens": false,
4075
+ "tokenizer_class": "PreTrainedTokenizerFast",
4076
+ "unk_token": "<|endoftext|>",
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+ "use_default_system_prompt": false,
4078
+ "use_fast": true
4079
+ }
ud.py ADDED
@@ -0,0 +1,166 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import numpy
2
+ from transformers import TokenClassificationPipeline
3
+
4
+ class BellmanFordTokenClassificationPipeline(TokenClassificationPipeline):
5
+ def __init__(self,**kwargs):
6
+ from copy import deepcopy
7
+ from tokenizers import Regex
8
+ from tokenizers.pre_tokenizers import Sequence,Split,Whitespace,Punctuation
9
+ super().__init__(**kwargs)
10
+ self.oldtokenizer=deepcopy(self.tokenizer)
11
+ self.tokenizer.backend_tokenizer.pre_tokenizer=Sequence([Split(Regex("[ぁ-ん]"),"isolated"),Whitespace(),Punctuation(),self.oldtokenizer.backend_tokenizer.pre_tokenizer])
12
+ x=self.model.config.label2id
13
+ y=[k for k in x if k.find("|")<0 and not k.startswith("I-")]
14
+ self.transition=numpy.full((len(x),len(x)),-numpy.inf)
15
+ self.sympos=numpy.full((len(x)),-numpy.inf)
16
+ for k,v in x.items():
17
+ if k.find("|")<0:
18
+ for j in ["I-"+k[2:]] if k.startswith("B-") else [k]+y if k.startswith("I-") else y:
19
+ self.transition[v,x[j]]=0
20
+ if k.startswith("SYM"):
21
+ self.sympos[v]=0
22
+ def check_model_type(self,supported_models):
23
+ pass
24
+ def postprocess(self,model_outputs,**kwargs):
25
+ if "logits" not in model_outputs:
26
+ return self.postprocess(model_outputs[0],**kwargs)
27
+ return self.bellman_ford_token_classification(model_outputs,**kwargs)
28
+ def bellman_ford_token_classification(self,model_outputs,**kwargs):
29
+ m=model_outputs["logits"][0].numpy()
30
+ v=model_outputs["offset_mapping"][0].tolist()
31
+ e=numpy.exp(m-numpy.max(m,axis=-1,keepdims=True))
32
+ z=e/e.sum(axis=-1,keepdims=True)
33
+ for i in range(m.shape[0]-1,0,-1):
34
+ if v[i-1][0]<v[i-1][1]:
35
+ m[i-1]+=numpy.max(m[i]+self.transition,axis=1)
36
+ else:
37
+ m[i-1]+=self.sympos
38
+ k=[numpy.argmax(m[0]+self.transition[0])]
39
+ for i in range(1,m.shape[0]):
40
+ k.append(numpy.argmax(m[i]+self.transition[k[-1]]))
41
+ w=[{"entity":self.model.config.id2label[j],"start":s,"end":e,"score":z[i,j]} for i,((s,e),j) in enumerate(zip(v,k)) if s<e]
42
+ if "aggregation_strategy" in kwargs and kwargs["aggregation_strategy"]!="none":
43
+ for i,t in reversed(list(enumerate(w))):
44
+ p=t.pop("entity")
45
+ if p.startswith("I-"):
46
+ w[i-1]["score"]=min(w[i-1]["score"],t["score"])
47
+ w[i-1]["end"]=w.pop(i)["end"]
48
+ elif i>0 and w[i-1]["end"]>t["start"]:
49
+ w[i-1]["score"]=min(w[i-1]["score"],t["score"])
50
+ w[i-1]["end"]=w.pop(i)["end"]
51
+ elif p.startswith("B-"):
52
+ t["entity_group"]=p[2:]
53
+ else:
54
+ t["entity_group"]=p
55
+ for t in w:
56
+ t["text"]=model_outputs["sentence"][t["start"]:t["end"]]
57
+ return w
58
+
59
+ class UniversalDependenciesPipeline(BellmanFordTokenClassificationPipeline):
60
+ def __init__(self,**kwargs):
61
+ kwargs["aggregation_strategy"]="simple"
62
+ super().__init__(**kwargs)
63
+ x=self.model.config.label2id
64
+ self.root=numpy.full((len(x)),-numpy.inf)
65
+ self.left_arc=numpy.full((len(x)),-numpy.inf)
66
+ self.right_arc=numpy.full((len(x)),-numpy.inf)
67
+ for k,v in x.items():
68
+ if k.endswith("|root"):
69
+ self.root[v]=0
70
+ elif k.find("|l-")>0:
71
+ self.left_arc[v]=0
72
+ elif k.find("|r-")>0:
73
+ self.right_arc[v]=0
74
+ def postprocess(self,model_outputs,**kwargs):
75
+ import torch
76
+ kwargs["aggregation_strategy"]="simple"
77
+ if "logits" not in model_outputs:
78
+ return self.postprocess(model_outputs[0],**kwargs)
79
+ w=self.bellman_ford_token_classification(model_outputs,**kwargs)
80
+ off=[(t["start"],t["end"]) for t in w]
81
+ for i,(s,e) in reversed(list(enumerate(off))):
82
+ if s<e:
83
+ d=w[i]["text"]
84
+ j=len(d)-len(d.lstrip())
85
+ if j>0:
86
+ d=d.lstrip()
87
+ off[i]=(off[i][0]+j,off[i][1])
88
+ j=len(d)-len(d.rstrip())
89
+ if j>0:
90
+ d=d.rstrip()
91
+ off[i]=(off[i][0],off[i][1]-j)
92
+ if d.strip()=="":
93
+ off.pop(i)
94
+ w.pop(i)
95
+ v=self.oldtokenizer([t["text"] for t in w],add_special_tokens=False)
96
+ x=[not t["entity_group"].endswith(".") for t in w]
97
+ z=model_outputs["input_ids"][0]
98
+ if len(x)<504:
99
+ x=[True]*len(x)
100
+ else:
101
+ k=sum([len(x)-i+1 if b else 0 for i,b in enumerate(x)])+len(z)+1
102
+ for i in numpy.argsort(numpy.array([t["score"] for t in w])):
103
+ if x[i]==False and k+len(x)-i<128000:
104
+ x[i]=True
105
+ k+=len(x)-i+1
106
+ ids=[-1]
107
+ for i in range(len(x)):
108
+ if x[i]:
109
+ ids.append(i)
110
+ for j in range(i+1,len(x)):
111
+ ids.append(j)
112
+ ids.append(-1)
113
+ with torch.no_grad():
114
+ e=self.model.get_input_embeddings().weight
115
+ m=[]
116
+ for j in v["input_ids"]:
117
+ if j==[]:
118
+ j=[self.tokenizer.unk_token_id]
119
+ m.append(e[j,:].sum(axis=0))
120
+ m.append(e[self.tokenizer.eos_token_id,:])
121
+ m=torch.stack(m).to(self.device)
122
+ e=self.model(inputs_embeds=torch.unsqueeze(torch.vstack((e[z,:],m[ids,:])),0))
123
+ m=e.logits[0].cpu().numpy()
124
+ e=numpy.full((len(x),len(x),m.shape[-1]),m.min())
125
+ k=len(z)+1
126
+ for i in range(len(x)):
127
+ if x[i]:
128
+ e[i,i]=m[k]+self.root
129
+ k+=1
130
+ for j in range(1,len(x)-i):
131
+ e[i+j,i]=m[k]+self.left_arc
132
+ e[i,i+j]=m[k]+self.right_arc
133
+ k+=1
134
+ k+=1
135
+ m,p=numpy.max(e,axis=2),numpy.argmax(e,axis=2)
136
+ h=self.chu_liu_edmonds(m)
137
+ z=[i for i,j in enumerate(h) if i==j]
138
+ if len(z)>1:
139
+ k,h=z[numpy.argmax(m[z,z])],numpy.min(m)-numpy.max(m)
140
+ m[:,z]+=[[0 if j in z and (i!=j or i==k) else h for i in z] for j in range(m.shape[0])]
141
+ h=self.chu_liu_edmonds(m)
142
+ q=[self.model.config.id2label[p[j,i]].split("|") for i,j in enumerate(h)]
143
+ t=model_outputs["sentence"].replace("\n"," ")
144
+ u="# text = "+t+"\n"
145
+ for i,(s,e) in enumerate(off):
146
+ u+="\t".join([str(i+1),t[s:e],"_",q[i][0],"_","_" if len(q[i])<3 else "|".join(q[i][1:-1]),str(0 if h[i]==i else h[i]+1),"root" if q[i][-1]=="root" else q[i][-1][2:],"_","_" if i+1<len(off) and e<off[i+1][0] else "SpaceAfter=No"])+"\n"
147
+ return u+"\n"
148
+ def chu_liu_edmonds(self,matrix):
149
+ h=numpy.argmax(matrix,axis=0)
150
+ x=[-1 if i==j else j for i,j in enumerate(h)]
151
+ for b in [lambda x,i,j:-1 if i not in x else x[i],lambda x,i,j:-1 if j<0 else x[j]]:
152
+ y=[]
153
+ while x!=y:
154
+ y=list(x)
155
+ for i,j in enumerate(x):
156
+ x[i]=b(x,i,j)
157
+ if max(x)<0:
158
+ return h
159
+ y,x=[i for i,j in enumerate(x) if j==max(x)],[i for i,j in enumerate(x) if j<max(x)]
160
+ z=matrix-numpy.max(matrix,axis=0)
161
+ m=numpy.block([[z[x,:][:,x],numpy.max(z[x,:][:,y],axis=1).reshape(len(x),1)],[numpy.max(z[y,:][:,x],axis=0),numpy.max(z[y,y])]])
162
+ k=[j if i==len(x) else x[j] if j<len(x) else y[numpy.argmax(z[y,x[i]])] for i,j in enumerate(self.chu_liu_edmonds(m))]
163
+ h=[j if i in y else k[x.index(i)] for i,j in enumerate(h)]
164
+ i=y[numpy.argmax(z[x[k[-1]],y] if k[-1]<len(x) else z[y,y])]
165
+ h[i]=x[k[-1]] if k[-1]<len(x) else i
166
+ return h