| |
| """ |
| UDgoeswith |
| |
| 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-goeswith |
| |
| GitHub: https://github.com/KoichiYasuoka |
| """ |
| from typing import List, Union |
|
|
| import numpy as np |
| import torch |
| import ufal.chu_liu_edmonds |
| from transformers import AutoModelForTokenClassification, AutoTokenizer |
|
|
|
|
| class Parse: |
| def __init__( |
| self, model: str = "KoichiYasuoka/deberta-base-thai-ud-goeswith" |
| ) -> None: |
| if model is None: |
| model = "KoichiYasuoka/deberta-base-thai-ud-goeswith" |
| self.tokenizer = AutoTokenizer.from_pretrained(model) |
| self.model = AutoModelForTokenClassification.from_pretrained(model) |
|
|
| def __call__( |
| self, text: str, tag: str = "str" |
| ) -> Union[List[List[str]], str]: |
| w = self.tokenizer(text, return_offsets_mapping=True) |
| v = w["input_ids"] |
| x = [ |
| v[0:i] + [self.tokenizer.mask_token_id] + v[i + 1 :] + [j] |
| for i, j in enumerate(v[1:-1], 1) |
| ] |
| with torch.no_grad(): |
| e = self.model(input_ids=torch.tensor(x)).logits.numpy()[ |
| :, 1:-2, : |
| ] |
| r = [ |
| 1 if i == 0 else -1 if j.endswith("|root") else 0 |
| for i, j in sorted(self.model.config.id2label.items()) |
| ] |
| e += np.where(np.add.outer(np.identity(e.shape[0]), r) == 0, 0, np.nan) |
| g = self.model.config.label2id["X|_|goeswith"] |
| r = np.tri(e.shape[0]) |
| for i in range(e.shape[0]): |
| for j in range(i + 2, e.shape[1]): |
| r[i, j] = r[i, j - 1] if np.nanargmax(e[i, j - 1]) == g else 1 |
| e[:, :, g] += np.where(r == 0, 0, np.nan) |
| m = np.full((e.shape[0] + 1, e.shape[1] + 1), np.nan) |
| m[1:, 1:] = np.nanmax(e, axis=2).transpose() |
| p = np.zeros(m.shape) |
| p[1:, 1:] = np.nanargmax(e, axis=2).transpose() |
| for i in range(1, m.shape[0]): |
| m[i, 0], m[i, i], p[i, 0] = m[i, i], np.nan, p[i, i] |
| h = ufal.chu_liu_edmonds.chu_liu_edmonds(m)[0] |
| if [0 for i in h if i == 0] != [0]: |
| m[:, 0] += np.where( |
| m[:, 0] |
| == np.nanmax(m[[i for i, j in enumerate(h) if j == 0], 0]), |
| 0, |
| np.nan, |
| ) |
| m[[i for i, j in enumerate(h) if j == 0]] += [ |
| 0 if i == 0 or j == 0 else np.nan for i, j in enumerate(h) |
| ] |
| h = ufal.chu_liu_edmonds.chu_liu_edmonds(m)[0] |
| u = "" |
| v = [(s, e) for s, e in w["offset_mapping"] if s < e] |
| if tag == "list": |
| _tag_data = [] |
| for i, (s, e) in enumerate(v, 1): |
| q = self.model.config.id2label[p[i, h[i]]].split("|") |
| _tag_data.append( |
| [ |
| str(i), |
| text[s:e], |
| "_", |
| q[0], |
| "_", |
| "|".join(q[1:-1]), |
| str(h[i]), |
| q[-1], |
| "_", |
| "_" if i < len(v) and e < v[i][0] else "SpaceAfter=No", |
| ] |
| ) |
| return _tag_data |
| else: |
| for i, (s, e) in enumerate(v, 1): |
| q = self.model.config.id2label[p[i, h[i]]].split("|") |
| u += ( |
| "\t".join( |
| [ |
| str(i), |
| text[s:e], |
| "_", |
| q[0], |
| "_", |
| "|".join(q[1:-1]), |
| str(h[i]), |
| q[-1], |
| "_", |
| "_" |
| if i < len(v) and e < v[i][0] |
| else "SpaceAfter=No", |
| ] |
| ) |
| + "\n" |
| ) |
| return u + "\n" |
|
|
