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e4b9a7b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 | # -*- coding: utf-8 -*-
"""
TransformersUD
Author: Prof. Koichi Yasuoka
This tagger is provided under the terms of the apache-2.0 License.
The source: https://huggingface.co/KoichiYasuoka/deberta-base-thai-ud-head
GitHub: https://github.com/KoichiYasuoka
"""
import os
from typing import List, Union
import numpy
import torch
import ufal.chu_liu_edmonds
from transformers import (
AutoConfig,
AutoModelForQuestionAnswering,
AutoModelForTokenClassification,
AutoTokenizer,
TokenClassificationPipeline,
)
from transformers.utils import cached_file
class Parse:
def __init__(
self, model: str = "KoichiYasuoka/deberta-base-thai-ud-head"
) -> None:
if model is None:
model = "KoichiYasuoka/deberta-base-thai-ud-head"
self.tokenizer = AutoTokenizer.from_pretrained(model)
self.model = AutoModelForQuestionAnswering.from_pretrained(model)
x = AutoModelForTokenClassification.from_pretrained
if os.path.isdir(model):
d, t = (
x(os.path.join(model, "deprel")),
x(os.path.join(model, "tagger")),
)
else:
c = AutoConfig.from_pretrained(
cached_file(model, "deprel/config.json")
)
d = x(cached_file(model, "deprel/pytorch_model.bin"), config=c)
s = AutoConfig.from_pretrained(
cached_file(model, "tagger/config.json")
)
t = x(cached_file(model, "tagger/pytorch_model.bin"), config=s)
self.deprel = TokenClassificationPipeline(
model=d, tokenizer=self.tokenizer, aggregation_strategy="simple"
)
self.tagger = TokenClassificationPipeline(
model=t, tokenizer=self.tokenizer
)
def __call__(
self, text: str, tag: str = "str"
) -> Union[List[List[str]], str]:
w = [
(t["start"], t["end"], t["entity_group"])
for t in self.deprel(text)
]
z, n = (
{t["start"]: t["entity"].split("|") for t in self.tagger(text)},
len(w),
)
r, m = (
[text[s:e] for s, e, p in w],
numpy.full((n + 1, n + 1), numpy.nan),
)
v, c = self.tokenizer(r, add_special_tokens=False)["input_ids"], []
for i, t in enumerate(v):
q = (
[self.tokenizer.cls_token_id]
+ t
+ [self.tokenizer.sep_token_id]
)
c.append(
[q]
+ v[0:i]
+ [[self.tokenizer.mask_token_id]]
+ v[i + 1 :]
+ [[q[-1]]]
)
b = [[len(sum(x[0 : j + 1], [])) for j in range(len(x))] for x in c]
with torch.no_grad():
d = self.model(
input_ids=torch.tensor([sum(x, []) for x in c]),
token_type_ids=torch.tensor(
[[0] * x[0] + [1] * (x[-1] - x[0]) for x in b]
),
)
s, e = d.start_logits.tolist(), d.end_logits.tolist()
for i in range(n):
for j in range(n):
m[i + 1, 0 if i == j else j + 1] = (
s[i][b[i][j]] + e[i][b[i][j + 1] - 1]
)
h = ufal.chu_liu_edmonds.chu_liu_edmonds(m)[0]
if [0 for i in h if i == 0] != [0]:
i = ([p for s, e, p in w] + ["root"]).index("root")
j = i + 1 if i < n else numpy.nanargmax(m[:, 0])
m[0:j, 0] = m[j + 1 :, 0] = numpy.nan
h = ufal.chu_liu_edmonds.chu_liu_edmonds(m)[0]
u = ""
if tag == "list":
_tag_data = []
for i, (s, e, p) in enumerate(w, 1):
p = "root" if h[i] == 0 else "dep" if p == "root" else p
_tag_data.append(
[
str(i),
r[i - 1],
"_",
z[s][0][2:],
"_",
"|".join(z[s][1:]),
str(h[i]),
p,
"_",
"_" if i < n and e < w[i][0] else "SpaceAfter=No",
]
)
return _tag_data
for i, (s, e, p) in enumerate(w, 1):
p = "root" if h[i] == 0 else "dep" if p == "root" else p
u += (
"\t".join(
[
str(i),
r[i - 1],
"_",
z[s][0][2:],
"_",
"|".join(z[s][1:]),
str(h[i]),
p,
"_",
"_" if i < n and e < w[i][0] else "SpaceAfter=No",
]
)
+ "\n"
)
return u + "\n"
|