# coding=utf-8 # Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team. # Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ BERT classification fine-tuning: utilities to work with GLUE tasks """ from __future__ import absolute_import, division, print_function import csv import logging import os import sys from io import open from collections import defaultdict import numpy as np import pdb from scipy.stats import pearsonr, spearmanr from sklearn.metrics import matthews_corrcoef, f1_score logger = logging.getLogger(__name__) class InputExample(object): """A single training/test example for simple sequence classification.""" def __init__(self, guid, text_a, text_b=None, label=None): """Constructs a InputExample. Args: guid: Unique id for the example. text_a: string. The untokenized text of the first sequence. For single sequence tasks, only this sequence must be specified. text_b: (Optional) string. The untokenized text of the second sequence. Only must be specified for sequence pair tasks. label: (Optional) string. The label of the example. This should be specified for train and dev examples, but not for test examples. """ self.guid = guid self.text_a = text_a self.text_b = text_b self.label = label class InputFeatures(object): """A single set of features of data.""" def __init__(self, input_ids, input_mask, segment_ids, label_id): self.input_ids = input_ids self.input_mask = input_mask self.segment_ids = segment_ids self.label_id = label_id class DataProcessor(object): """Base class for data converters for sequence classification data sets.""" def get_train_examples(self, data_dir): """Gets a collection of `InputExample`s for the train set.""" raise NotImplementedError() def get_dev_examples(self, data_dir): """Gets a collection of `InputExample`s for the dev set.""" raise NotImplementedError() def get_labels(self): """Gets the list of labels for this data set.""" raise NotImplementedError() @classmethod def _read_tsv(cls, input_file, quotechar=None): """Reads a tab separated value file.""" with open(input_file, "r", encoding="utf-8-sig") as f: reader = csv.reader(f, delimiter="\t", quotechar=quotechar) lines = [] for line in reader: if sys.version_info[0] == 2: line = list(unicode(cell, 'utf-8') for cell in line) lines.append(line) return lines class MrpcProcessor(DataProcessor): """Processor for the MRPC data set (GLUE version).""" def get_train_examples(self, data_dir, percentage_per_label=1.0, sample_per_label=0): """See base class.""" logger.info("LOOKING AT {}".format(os.path.join(data_dir, "train.tsv"))) return self._create_examples( self._read_tsv(os.path.join(data_dir, "train.tsv")), "train") def get_dev_examples(self, data_dir): """See base class.""" return self._create_examples( self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev") def get_labels(self): """See base class.""" return ["0", "1"] def _create_examples(self, lines, set_type): """Creates examples for the training and dev sets.""" examples = [] for (i, line) in enumerate(lines): if i == 0: continue guid = "%s-%s" % (set_type, i) text_a = line[3] text_b = line[4] label = line[0] examples.append( InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label)) return examples class MnliProcessor(DataProcessor): """Processor for the MultiNLI data set (GLUE version).""" def get_train_examples(self, data_dir, percentage_per_label=1.0, sample_per_label=0): """See base class.""" return self._create_examples( self._read_tsv(os.path.join(data_dir, "train.tsv")), "train") def get_dev_examples(self, data_dir): """See base class.""" return self._create_examples( self._read_tsv(os.path.join(data_dir, "dev_matched.tsv")), "dev_matched") def get_labels(self): """See base class.""" return ["contradiction", "entailment", "neutral"] def _create_examples(self, lines, set_type): """Creates examples for the training and dev sets.""" examples = [] for (i, line) in enumerate(lines): if i == 0: continue guid = "%s-%s" % (set_type, line[0]) text_a = line[8] text_b = line[9] label = line[-1] examples.append( InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label)) return examples class MnliMismatchedProcessor(MnliProcessor): """Processor for the MultiNLI Mismatched data set (GLUE version).""" def get_dev_examples(self, data_dir): """See base class.""" return self._create_examples( self._read_tsv(os.path.join(data_dir, "dev_mismatched.tsv")), "dev_matched") class ColaProcessor(DataProcessor): """Processor for the CoLA data set (GLUE version).""" def get_train_examples(self, data_dir, percentage_per_label=1.0, sample_per_label=0): """See base class.""" return self._create_examples( self._read_tsv(os.path.join(data_dir, "train.tsv")), "train", percentage_per_label) def get_dev_examples(self, data_dir): """See base class.""" return self._create_examples( self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev") def get_labels(self): """See base class.""" return ["0", "1"] def _create_examples(self, lines, set_type, percentage_per_label=1.0, sample_per_label=0): """Creates examples for the training and dev sets.""" dict_label2examples = defaultdict(list) examples = [] for (i, line) in enumerate(lines): guid = "%s-%s" % (set_type, i) text_a = line[3] label = line[1] examples.append( InputExample(guid=guid, text_a=text_a, text_b=None, label=label)) dict_label2examples[label].append(i) if percentage_per_label<1.0: nlabel = GLUE_TASKS_NUM_LABELS['cola'] examples_sub = [] for i in range(nlabel): index = np.random.choice(dict_label2examples[str(i)], int(len(dict_label2examples[str(i)])*percentage_per_label), replace=False) for j in index: examples_sub.append(examples[j]) examples = examples_sub # pdb.set_trace() return examples class YelpProcessor(DataProcessor): """Processor for the Yelp short data set (GLUE version).""" def get_train_examples(self, data_dir, percentage_per_label=1.0, sample_per_label=0): """See base class.""" return self._create_examples( self._read_tsv(os.path.join(data_dir, "train.tsv")), "train", percentage_per_label, sample_per_label) def get_dev_examples(self, data_dir): """See base class.""" return self._create_examples( self._read_tsv(os.path.join(data_dir, "test.tsv")), "test", percentage_per_label=1.0, sample_per_label=5000) def get_labels(self): """See base class.""" return ["0", "1"] def _create_examples(self, lines, set_type, percentage_per_label=1.0, sample_per_label=0): """Creates examples for the training and dev sets.""" dict_label2examples = defaultdict(list) examples = [] for (i, line) in enumerate(lines): if i == 0: continue guid = "%s-%s" % (set_type, i) text_a = line[1] label = line[0] examples.append( InputExample(guid=guid, text_a=text_a, text_b=None, label=label)) dict_label2examples[label].append(i-1) if percentage_per_label<1.0 or sample_per_label>0: nlabel = GLUE_TASKS_NUM_LABELS['yelp'] examples_sub = [] for i in range(nlabel): if sample_per_label > 0: index = np.random.choice(dict_label2examples[str(i)], sample_per_label, replace=False) else: index = np.random.choice(dict_label2examples[str(i)], int(len(dict_label2examples[str(i)])*percentage_per_label), replace=False) for j in index: examples_sub.append(examples[j]) examples = examples_sub # pdb.set_trace() return examples class Sst2Processor(DataProcessor): """Processor for the SST-2 data set (GLUE version).""" def get_train_examples(self, data_dir, percentage_per_label=1.0, sample_per_label=0): """See base class.""" return self._create_examples( self._read_tsv(os.path.join(data_dir, "train.tsv")), "train", percentage_per_label) def get_dev_examples(self, data_dir): """See base class.""" return self._create_examples( self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev") def get_labels(self): """See base class.""" return ["0", "1"] def _create_examples(self, lines, set_type, percentage_per_label=1.0): """Creates examples for the training and dev sets.""" dict_label2examples = defaultdict(list) examples = [] for (i, line) in enumerate(lines): if i == 0: continue guid = "%s-%s" % (set_type, i) text_a = line[0] label = line[1] examples.append( InputExample(guid=guid, text_a=text_a, text_b=None, label=label)) dict_label2examples[label].append(i-1) if percentage_per_label<1.0: nlabel = GLUE_TASKS_NUM_LABELS['sst-2'] examples_sub = [] for i in range(nlabel): index = np.random.choice(dict_label2examples[str(i)], int(len(dict_label2examples[str(i)])*percentage_per_label), replace=False) for j in index: examples_sub.append(examples[j]) examples = examples_sub return examples class StsbProcessor(DataProcessor): """Processor for the STS-B data set (GLUE version).""" def get_train_examples(self, data_dir, percentage_per_label=1.0, sample_per_label=0): """See base class.""" return self._create_examples( self._read_tsv(os.path.join(data_dir, "train.tsv")), "train") def get_dev_examples(self, data_dir): """See base class.""" return self._create_examples( self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev") def get_labels(self): """See base class.""" return [None] def _create_examples(self, lines, set_type): """Creates examples for the training and dev sets.""" examples = [] for (i, line) in enumerate(lines): if i == 0: continue guid = "%s-%s" % (set_type, line[0]) text_a = line[7] text_b = line[8] label = line[-1] examples.append( InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label)) return examples class QqpProcessor(DataProcessor): """Processor for the QQP data set (GLUE version).""" def get_train_examples(self, data_dir, percentage_per_label=1.0, sample_per_label=0): """See base class.""" return self._create_examples( self._read_tsv(os.path.join(data_dir, "train.tsv")), "train") def get_dev_examples(self, data_dir): """See base class.""" return self._create_examples( self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev") def get_labels(self): """See base class.""" return ["0", "1"] def _create_examples(self, lines, set_type): """Creates examples for the training and dev sets.""" examples = [] for (i, line) in enumerate(lines): if i == 0: continue guid = "%s-%s" % (set_type, line[0]) try: text_a = line[3] text_b = line[4] label = line[5] except IndexError: continue examples.append( InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label)) return examples class QnliProcessor(DataProcessor): """Processor for the QNLI data set (GLUE version).""" def get_train_examples(self, data_dir, percentage_per_label=1.0, sample_per_label=0): """See base class.""" return self._create_examples( self._read_tsv(os.path.join(data_dir, "train.tsv")), "train") def get_dev_examples(self, data_dir): """See base class.""" return self._create_examples( self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev_matched") def get_labels(self): """See base class.""" return ["entailment", "not_entailment"] def _create_examples(self, lines, set_type): """Creates examples for the training and dev sets.""" examples = [] for (i, line) in enumerate(lines): if i == 0: continue guid = "%s-%s" % (set_type, line[0]) text_a = line[1] text_b = line[2] label = line[-1] examples.append( InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label)) return examples class RteProcessor(DataProcessor): """Processor for the RTE data set (GLUE version).""" def get_train_examples(self, data_dir, percentage_per_label=1.0, sample_per_label=0): """See base class.""" return self._create_examples( self._read_tsv(os.path.join(data_dir, "train.tsv")), "train") def get_dev_examples(self, data_dir): """See base class.""" return self._create_examples( self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev") def get_labels(self): """See base class.""" return ["entailment", "not_entailment"] def _create_examples(self, lines, set_type): """Creates examples for the training and dev sets.""" examples = [] for (i, line) in enumerate(lines): if i == 0: continue guid = "%s-%s" % (set_type, line[0]) text_a = line[1] text_b = line[2] label = line[-1] examples.append( InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label)) return examples class WnliProcessor(DataProcessor): """Processor for the WNLI data set (GLUE version).""" def get_train_examples(self, data_dir, percentage_per_label=1.0, sample_per_label=0): """See base class.""" return self._create_examples( self._read_tsv(os.path.join(data_dir, "train.tsv")), "train") def get_dev_examples(self, data_dir): """See base class.""" return self._create_examples( self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev") def get_labels(self): """See base class.""" return ["0", "1"] def _create_examples(self, lines, set_type): """Creates examples for the training and dev sets.""" examples = [] for (i, line) in enumerate(lines): if i == 0: continue guid = "%s-%s" % (set_type, line[0]) text_a = line[1] text_b = line[2] label = line[-1] examples.append( InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label)) return examples def convert_examples_to_features(examples, label_list, max_seq_length, tokenizer, output_mode, cls_token_at_end=False, cls_token='[CLS]', cls_token_segment_id=1, sep_token='[SEP]', sep_token_extra=False, pad_on_left=False, pad_token=0, pad_token_segment_id=0, sequence_a_segment_id=0, sequence_b_segment_id=1, mask_padding_with_zero=True): """ Loads a data file into a list of `InputBatch`s `cls_token_at_end` define the location of the CLS token: - False (Default, BERT/XLM pattern): [CLS] + A + [SEP] + B + [SEP] - True (XLNet/GPT pattern): A + [SEP] + B + [SEP] + [CLS] `cls_token_segment_id` define the segment id associated to the CLS token (0 for BERT, 2 for XLNet) """ label_map = {label : i for i, label in enumerate(label_list)} features = [] for (ex_index, example) in enumerate(examples): if ex_index % 10000 == 0: logger.info("Writing example %d of %d" % (ex_index, len(examples))) tokens_a = tokenizer.tokenize(example.text_a) tokens_b = None if example.text_b: tokens_b = tokenizer.tokenize(example.text_b) # Modifies `tokens_a` and `tokens_b` in place so that the total # length is less than the specified length. # Account for [CLS], [SEP], [SEP] with "- 3". " -4" for RoBERTa. special_tokens_count = 4 if sep_token_extra else 3 _truncate_seq_pair(tokens_a, tokens_b, max_seq_length - special_tokens_count) else: # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa. special_tokens_count = 3 if sep_token_extra else 2 if len(tokens_a) > max_seq_length - special_tokens_count: tokens_a = tokens_a[:(max_seq_length - special_tokens_count)] # The convention in BERT is: # (a) For sequence pairs: # tokens: [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP] # type_ids: 0 0 0 0 0 0 0 0 1 1 1 1 1 1 # (b) For single sequences: # tokens: [CLS] the dog is hairy . [SEP] # type_ids: 0 0 0 0 0 0 0 # # Where "type_ids" are used to indicate whether this is the first # sequence or the second sequence. The embedding vectors for `type=0` and # `type=1` were learned during pre-training and are added to the wordpiece # embedding vector (and position vector). This is not *strictly* necessary # since the [SEP] token unambiguously separates the sequences, but it makes # it easier for the model to learn the concept of sequences. # # For classification tasks, the first vector (corresponding to [CLS]) is # used as as the "sentence vector". Note that this only makes sense because # the entire model is fine-tuned. tokens = tokens_a + [sep_token] if sep_token_extra: # roberta uses an extra separator b/w pairs of sentences tokens += [sep_token] segment_ids = [sequence_a_segment_id] * len(tokens) if tokens_b: tokens += tokens_b + [sep_token] segment_ids += [sequence_b_segment_id] * (len(tokens_b) + 1) if cls_token_at_end: tokens = tokens + [cls_token] segment_ids = segment_ids + [cls_token_segment_id] else: tokens = [cls_token] + tokens segment_ids = [cls_token_segment_id] + segment_ids input_ids = tokenizer.convert_tokens_to_ids(tokens) # The mask has 1 for real tokens and 0 for padding tokens. Only real # tokens are attended to. input_mask = [1 if mask_padding_with_zero else 0] * len(input_ids) # Zero-pad up to the sequence length. padding_length = max_seq_length - len(input_ids) if pad_on_left: input_ids = ([pad_token] * padding_length) + input_ids input_mask = ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask segment_ids = ([pad_token_segment_id] * padding_length) + segment_ids else: input_ids = input_ids + ([pad_token] * padding_length) input_mask = input_mask + ([0 if mask_padding_with_zero else 1] * padding_length) segment_ids = segment_ids + ([pad_token_segment_id] * padding_length) assert len(input_ids) == max_seq_length assert len(input_mask) == max_seq_length assert len(segment_ids) == max_seq_length if output_mode == "classification": label_id = label_map[example.label] elif output_mode == "regression": label_id = float(example.label) else: raise KeyError(output_mode) if ex_index < 5: logger.info("*** Example ***") logger.info("guid: %s" % (example.guid)) logger.info("tokens: %s" % " ".join( [str(x) for x in tokens])) logger.info("input_ids: %s" % " ".join([str(x) for x in input_ids])) logger.info("input_mask: %s" % " ".join([str(x) for x in input_mask])) logger.info("segment_ids: %s" % " ".join([str(x) for x in segment_ids])) logger.info("label: %s (id = %d)" % (example.label, label_id)) features.append( InputFeatures(input_ids=input_ids, input_mask=input_mask, segment_ids=segment_ids, label_id=label_id)) return features def _truncate_seq_pair(tokens_a, tokens_b, max_length): """Truncates a sequence pair in place to the maximum length.""" # This is a simple heuristic which will always truncate the longer sequence # one token at a time. This makes more sense than truncating an equal percent # of tokens from each, since if one sequence is very short then each token # that's truncated likely contains more information than a longer sequence. while True: total_length = len(tokens_a) + len(tokens_b) if total_length <= max_length: break if len(tokens_a) > len(tokens_b): tokens_a.pop() else: tokens_b.pop() def simple_accuracy(preds, labels): return (preds == labels).mean() def acc_and_f1(preds, labels): acc = simple_accuracy(preds, labels) f1 = f1_score(y_true=labels, y_pred=preds) return { "acc": acc, "f1": f1, "acc_and_f1": (acc + f1) / 2, } def pearson_and_spearman(preds, labels): pearson_corr = pearsonr(preds, labels)[0] spearman_corr = spearmanr(preds, labels)[0] return { "pearson": pearson_corr, "spearmanr": spearman_corr, "corr": (pearson_corr + spearman_corr) / 2, } def compute_metrics(task_name, preds, labels): assert len(preds) == len(labels) if task_name == "cola": return {"mcc": matthews_corrcoef(labels, preds)} elif task_name == "sst-2": return {"acc": simple_accuracy(preds, labels)} elif task_name == "mrpc": return acc_and_f1(preds, labels) elif task_name == "sts-b": return pearson_and_spearman(preds, labels) elif task_name == "qqp": return acc_and_f1(preds, labels) elif task_name == "mnli": return {"acc": simple_accuracy(preds, labels)} elif task_name == "mnli-mm": return {"acc": simple_accuracy(preds, labels)} elif task_name == "qnli": return {"acc": simple_accuracy(preds, labels)} elif task_name == "rte": return {"acc": simple_accuracy(preds, labels)} elif task_name == "wnli": return {"acc": simple_accuracy(preds, labels)} elif task_name == "yelp": return {"acc": simple_accuracy(preds, labels)} else: raise KeyError(task_name) processors = { "cola": ColaProcessor, "mnli": MnliProcessor, "mnli-mm": MnliMismatchedProcessor, "mrpc": MrpcProcessor, "sst-2": Sst2Processor, "sts-b": StsbProcessor, "qqp": QqpProcessor, "qnli": QnliProcessor, "rte": RteProcessor, "wnli": WnliProcessor, "yelp": YelpProcessor, } output_modes = { "cola": "classification", "mnli": "classification", "mnli-mm": "classification", "mrpc": "classification", "sst-2": "classification", "sts-b": "regression", "qqp": "classification", "qnli": "classification", "rte": "classification", "wnli": "classification", "yelp": "classification", } GLUE_TASKS_NUM_LABELS = { "cola": 2, "mnli": 3, "mrpc": 2, "sst-2": 2, "sts-b": 1, "qqp": 2, "qnli": 2, "rte": 2, "wnli": 2, "yelp": 2, }