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Use BasicTokenizer to split over whitespaces.

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This commit is contained in:
Morgan Funtowicz 2019-12-20 09:38:08 +01:00
parent 9d0d1cd339
commit e516a34a15
1 changed files with 9 additions and 2 deletions
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11
transformers/pipelines.py
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@ -27,7 +27,7 @@ from typing import Union, Optional, Tuple, List, Dict
import numpy as np
from transformers import AutoConfig, AutoTokenizer, PreTrainedTokenizer, PretrainedConfig, \
SquadExample, squad_convert_examples_to_features, is_tf_available, is_torch_available, logger
SquadExample, squad_convert_examples_to_features, is_tf_available, is_torch_available, logger, BasicTokenizer
if is_tf_available():
import tensorflow as tf
@ -416,12 +416,19 @@ class NerPipeline(Pipeline):
Named Entity Recognition pipeline using ModelForTokenClassification head.
"""
def __init__(self, model, tokenizer: PreTrainedTokenizer = None,
args_parser: ArgumentHandler = None, device: int = -1,
binary_output: bool = False):
super().__init__(model, tokenizer, args_parser, device, binary_output)
self._basic_tokenizer = BasicTokenizer(do_lower_case=False)
def __call__(self, *texts, **kwargs):
inputs, answers = self._args_parser(*texts, **kwargs), []
for sentence in inputs:
# Ugly token to word idx mapping (for now)
token_to_word, words = [], sentence.split(' ')
token_to_word, words = [], self._basic_tokenizer.tokenize(sentence)
for i, w in enumerate(words):
tokens = self.tokenizer.tokenize(w)
token_to_word += [i] * len(tokens)