File size: 7,930 Bytes
3201a95 591028c 3201a95 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 |
# Adapt code from https://github.com/yuh-zha/AlignScore/tree/main
import sys
sys.path.append("..")
import nltk
nltk.download('punkt')
from nltk.tokenize import sent_tokenize
import torch
from transformers import AutoConfig, AutoTokenizer, AutoModelForSeq2SeqLM, AutoModelForSequenceClassification
import torch.nn as nn
from tqdm import tqdm
import torch.nn.functional as F
import os
def sent_tokenize_with_newlines(text):
blocks = text.split('\n')
tokenized_blocks = [sent_tokenize(block) for block in blocks]
tokenized_text = []
for block in tokenized_blocks:
tokenized_text.extend(block)
tokenized_text.append('\n')
return tokenized_text[:-1]
class Inferencer():
def __init__(self, path, chunk_size, max_input_length, batch_size) -> None:
self.path = path
self.device = "cuda:0" if torch.cuda.is_available() else "cpu"
self.model = AutoModelForSeq2SeqLM.from_pretrained(path).to(self.device)
self.tokenizer = AutoTokenizer.from_pretrained(path)
self.chunk_size=500 if chunk_size is None else chunk_size
self.max_input_length=2048 if max_input_length is None else max_input_length
self.max_output_length = 256
self.model.eval()
self.batch_size = batch_size
self.softmax = nn.Softmax(dim=-1)
def inference_example_batch(self, doc: list, claim: list):
"""
inference a example,
doc: list
claim: list
using self.inference to batch the process
"""
assert len(doc) == len(claim), "doc must has the same length with claimthesis!"
max_support_probs = []
used_chunks = []
support_prob_per_chunk = []
for one_doc, one_claim in tqdm(zip(doc, claim), desc="Evaluating", total=len(doc)):
output = self.inference_per_example(one_doc, one_claim)
max_support_probs.append(output['max_support_prob'])
used_chunks.append(output['used_chunks'])
support_prob_per_chunk.append(output['support_prob_per_chunk'])
return {
'max_support_probs': max_support_probs,
'used_chunks': used_chunks,
'support_prob_per_chunk': support_prob_per_chunk
}
def inference_per_example(self, doc:str, claim: str):
"""
inference a example,
doc: string
claim: string
using self.inference to batch the process
"""
def chunks(lst, n):
"""Yield successive chunks from lst with each having approximately n tokens.
For flan-t5, we split using the white space;
For roberta and deberta, we split using the tokenization.
"""
current_chunk = []
current_word_count = 0
for sentence in lst:
sentence_word_count = len(sentence.split())
if current_word_count + sentence_word_count > n:
yield ' '.join(current_chunk)
current_chunk = [sentence]
current_word_count = sentence_word_count
else:
current_chunk.append(sentence)
current_word_count += sentence_word_count
if current_chunk:
yield ' '.join(current_chunk)
doc_sents = sent_tokenize_with_newlines(doc)
doc_sents = doc_sents or ['']
doc_chunks = [chunk.replace(" \n ", '\n').strip() for chunk in chunks(doc_sents, self.chunk_size)]
'''
[chunk_1, chunk_2, chunk_3, chunk_4, ...]
[claim]
'''
claim_repeat = [claim] * len(doc_chunks)
output = self.inference(doc_chunks, claim_repeat)
return output
def inference(self, doc, claim):
"""
inference a list of doc and claim
Standard aggregation (max) over chunks of doc
Note: We do not have any post-processing steps for 'claim'
and directly check 'doc' against 'claim'. If there are multiple
sentences in 'claim'. Sentences are not splitted and are checked
as a single piece of text.
If there are multiple sentences in 'claim', we suggest users to
split 'claim' into sentences beforehand and prepares data like
(doc, claim_1), (doc, claim_2), ... for a multi-sentence 'claim'.
**We leave the user to decide how to aggregate the results from multiple sentences.**
Note: AggreFact-CNN is the only dataset that contains three-sentence
summaries and have annotations on the whole summaries, so we do not
split the sentences in each 'claim' during prediciotn for simplicity.
Therefore, for this dataset, our result is based on treating the whole
summary as a single piece of text (one 'claim').
In general, sentence-level prediciton performance is better than that on
the full-response-level.
"""
if isinstance(doc, str) and isinstance(claim, str):
doc = [doc]
claim = [claim]
batch_input, _, batch_org_chunks = self.batch_tokenize(doc, claim)
label_probs_list = []
used_chunks = []
for mini_batch_input, batch_org_chunk in zip(batch_input, batch_org_chunks):
mini_batch_input = {k: v.to(self.device) for k, v in mini_batch_input.items()}
with torch.no_grad():
decoder_input_ids = torch.zeros((mini_batch_input['input_ids'].size(0), 1), dtype=torch.long).to(self.device)
outputs = self.model(input_ids=mini_batch_input['input_ids'], attention_mask=mini_batch_input['attention_mask'], decoder_input_ids=decoder_input_ids)
logits = outputs.logits.squeeze(1)
# 3 for no support and 209 for support
label_logits = logits[:, torch.tensor([3, 209])].cpu()
label_probs = torch.nn.functional.softmax(label_logits, dim=-1)
label_probs_list.append(label_probs)
used_chunks.extend(batch_org_chunk)
label_probs = torch.cat(label_probs_list)
support_prob_per_chunk = label_probs[:, 1].cpu().numpy()
max_support_prob = label_probs[:, 1].max().item()
return {
'max_support_prob': max_support_prob,
'used_chunks': used_chunks,
'support_prob_per_chunk': support_prob_per_chunk
}
def batch_tokenize(self, doc, claim):
"""
input doc and claims are lists
"""
assert isinstance(doc, list) and isinstance(claim, list)
assert len(doc) == len(claim), "doc and claim should be in the same length."
original_text = [self.tokenizer.eos_token.join([one_doc, one_claim]) for one_doc, one_claim in zip(doc, claim)]
batch_input = []
batch_concat_text = []
batch_org_chunks = []
for mini_batch in self.chunks(original_text, self.batch_size):
model_inputs = self.tokenizer(
['predict: ' + text for text in mini_batch],
max_length=self.max_input_length,
truncation=True,
padding=True,
return_tensors="pt"
)
batch_input.append(model_inputs)
batch_concat_text.append(mini_batch)
batch_org_chunks.append([item[:item.find('</s>')] for item in mini_batch])
return batch_input, batch_concat_text, batch_org_chunks
def chunks(self, lst, n):
"""Yield successive n-sized chunks from lst."""
for i in range(0, len(lst), n):
yield lst[i:i + n]
def fact_check(self, doc, claim):
outputs = self.inference_example_batch(doc, claim)
return outputs['max_support_probs'], outputs['used_chunks'], outputs['support_prob_per_chunk'] |