forked from PaddlePaddle/PaddleNLP
-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathutils.py
166 lines (147 loc) Β· 6.67 KB
/
utils.py
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
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
# Copyright (c) 2022 PaddlePaddle Authors. 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.
import evaluate
import nltk
import numpy as np
from paddlenlp.metrics import BLEU
def convert_example(
example,
tokenizer,
decoder_start_token_id,
max_source_length,
max_target_length,
ignore_pad_token_for_loss=True,
is_train=True,
):
"""
Convert an example into necessary features.
"""
# Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
# in one example possible giving several features when a context is long, each of those features having a
# context that overlaps a bit the context of the previous feature.
# NOTE: Almost the same functionality as HuggingFace's prepare_train_features function. The main difference is
# that HugggingFace uses ArrowTable as basic data structure, while we use list of dictionary instead.
context = example["context"]
question = example["question"]
try:
answer = example["answers"][0]
except:
print(example["context"])
print(example["question"])
print(example["answers"])
print(example["answer_starts"])
print(example["is_impossible"])
input_seq = f"answer: {answer} context: {context} </s>"
output_seq = f"question: {question} </s>"
outputs = tokenizer(
output_seq,
max_seq_len=max_target_length,
pad_to_max_seq_len=True,
truncation_strategy="longest_first",
)
output_ids = [decoder_start_token_id] + outputs["input_ids"][:-1]
if ignore_pad_token_for_loss:
# Replace all tokenizer.pad_token_id in the outputs by -100 when we want to ignore padding in the loss.
outputs["input_ids"] = [(l if l != tokenizer.pad_token_id else -100) for l in outputs["input_ids"]]
if is_train:
inputs = tokenizer(
input_seq,
max_seq_len=max_source_length,
pad_to_max_seq_len=True,
truncation_strategy="longest_first",
return_attention_mask=True,
return_length=False,
)
return inputs["input_ids"], inputs["attention_mask"], output_ids, outputs["input_ids"]
else:
inputs = tokenizer(
input_seq,
max_seq_len=max_source_length,
pad_to_max_seq_len=True,
truncation_strategy="longest_first",
return_attention_mask=True,
return_length=True,
)
return inputs["input_ids"], inputs["attention_mask"], inputs["length"], output_ids, outputs["input_ids"]
def compute_metrics(preds, labels, tokenizer, ignore_pad_token_for_loss=True):
def compute_bleu(predictions, references, rouge_types=None, use_stemmer=True):
bleu1 = BLEU(n_size=1)
bleu2 = BLEU(n_size=2)
bleu3 = BLEU(n_size=3)
bleu4 = BLEU(n_size=4)
assert len(predictions) == len(references)
for i in range(len(predictions)):
bleu1.add_inst(predictions[i], [references[i]])
bleu2.add_inst(predictions[i], [references[i]])
bleu3.add_inst(predictions[i], [references[i]])
bleu4.add_inst(predictions[i], [references[i]])
result = {
"BLEU-1": bleu1.score() * 100,
"BLEU-2": bleu2.score() * 100,
"BLEU-3": bleu3.score() * 100,
"BLEU-4": bleu4.score() * 100,
}
return result
def compute_bleu_hf(predictions, references, rouge_types=None, use_stemmer=True):
predictions = [" ".join(prediction) for prediction in predictions]
references = [[" ".join(reference)] for reference in references]
bleu = evaluate.load("bleu")
assert len(predictions) == len(references)
bleu1_results = bleu.compute(predictions=predictions, references=references, max_order=1)
bleu2_results = bleu.compute(predictions=predictions, references=references, max_order=2)
bleu3_results = bleu.compute(predictions=predictions, references=references, max_order=3)
bleu4_results = bleu.compute(predictions=predictions, references=references, max_order=4)
result = {
"BLEU-1": bleu1_results["bleu"] * 100,
"BLEU-2": bleu2_results["bleu"] * 100,
"BLEU-3": bleu3_results["bleu"] * 100,
"BLEU-4": bleu4_results["bleu"] * 100,
}
return result
def post_process_text(preds, labels):
preds = [pred.strip() for pred in preds]
labels = [label.strip() for label in labels]
preds = [pred.strip("question:") for pred in preds]
labels = [label.strip("question:") for label in labels]
labels = [label.strip() for label in labels]
# expects newline after each sentence
preds = ["\n".join(nltk.sent_tokenize(pred)) for pred in preds]
labels = ["\n".join(nltk.sent_tokenize(label)) for label in labels]
preds = [pred.split() for pred in preds]
labels = [label.split() for label in labels]
return preds, labels
def post_process_seq(seq, bos_idx, eos_idx, output_bos=False, output_eos=False):
"""
Post-process the decoded sequence.
"""
eos_pos = len(seq) - 1
for i, idx in enumerate(seq):
if idx == eos_idx:
eos_pos = i
break
seq = [idx for idx in seq[: eos_pos + 1] if (output_bos or idx != bos_idx) and (output_eos or idx != eos_idx)]
return seq
if ignore_pad_token_for_loss:
labels = np.asarray(labels)
labels = np.where(labels != -100, labels, tokenizer.pad_token_id)
decoded_preds, decoded_labels = [], []
for pred, label in zip(preds, labels):
pred_id = post_process_seq(pred, tokenizer.bos_token_id, tokenizer.eos_token_id)
label_id = post_process_seq(label, tokenizer.bos_token_id, tokenizer.eos_token_id)
decoded_preds.append(tokenizer.decode(pred_id))
decoded_labels.append(tokenizer.decode(label_id))
decoded_preds, decoded_labels = post_process_text(decoded_preds, decoded_labels)
# bleu_result = compute_bleu(decoded_preds, decoded_labels)
bleu_result = compute_bleu_hf(decoded_preds, decoded_labels)
return bleu_result, decoded_preds, decoded_labels