File size: 5,555 Bytes
d7977c0 9354068 d7977c0 9354068 4c352ab 9354068 d7977c0 9354068 d7977c0 9354068 d7977c0 29c5ee7 d7977c0 9354068 78413e7 9354068 d7977c0 9354068 d7977c0 9354068 78413e7 bb94fcb d7977c0 9354068 d7977c0 9354068 |
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 |
import re
import os
import torch
import json
from transformers import AutoTokenizer, AutoModelForTokenClassification, AutoConfig
from datasets import load_dataset
URL_RE = re.compile(r"https?:\/\/[\w\.\/\?\=\d&#%_:/-]+")
HANDLE_RE = re.compile(r"@\w+")
def preprocess_bernice(text):
text = HANDLE_RE.sub("@USER", text)
text = URL_RE.sub("HTTPURL", text)
return text
def preprocess_timelm(text):
text = HANDLE_RE.sub("@user", text)
text = URL_RE.sub("http", text)
return text
def preprocess(model_name, text):
if model_name == "jhu-clsp/bernice":
return preprocess_bernice(text)
if "twitter-roberta-base" in model_name:
return preprocess_timelm(text)
return text
class NER:
id_to_label = {
0: 'B-corporation',
1: 'B-creative_work',
2: 'B-event',
3: 'B-group',
4: 'B-location',
5: 'B-person',
6: 'B-product',
7: 'I-corporation',
8: 'I-creative_work',
9: 'I-event',
10: 'I-group',
11: 'I-location',
12: 'I-person',
13: 'I-product',
14: 'O'
}
def __init__(self, model_name: str):
self.model_name = model_name
self.config = AutoConfig.from_pretrained(self.model_name)
self.model = AutoModelForTokenClassification.from_pretrained(self.model_name, config=self.config)
self.tokenizer = AutoTokenizer.from_pretrained(self.model_name)
self.max_length = 128
# GPU setup (https://github.com/cardiffnlp/tweetnlp/issues/15)
if torch.cuda.is_available() and torch.cuda.device_count() > 0:
self.device = torch.device('cuda')
elif hasattr(torch.backends, "mps") and torch.backends.mps.is_available() and torch.backends.mps.is_built():
self.device = torch.device("mps")
else:
self.device = torch.device('cpu')
self.parallel = torch.cuda.device_count() > 1
if self.parallel:
self.model = torch.nn.DataParallel(self.model)
self.model.to(self.device)
self.model.eval()
self.dataset = load_dataset("tweettemposhift/tweet_temporal_shift", "ner_temporal")
self.tokenized_datasets = self.dataset.map(lambda x: self.tokenize_and_align_labels(x), batched=True)
def get_prediction(self, export_dir: str, batch_size: int):
os.makedirs(export_dir, exist_ok=True)
for test_split in ["test_1", "test_2", "test_3", "test_4"]:
predictions = self.predict(self.tokenized_datasets[test_split], batch_size)
with open(f"{export_dir}/{test_split}.jsonl", "w") as f:
f.write("\n".join([json.dumps(i) for i in predictions]))
def predict(self, example, batch_size: int):
input_keys = ['input_ids', 'attention_mask']
indices = list(range(0, len(example), batch_size)) + [len(example) + 1]
preds = []
labels = []
with torch.no_grad():
for i in range(len(indices) - 1):
encoded_input = example[indices[i]: indices[i + 1]]
labels += [
[self.id_to_label[y] if y in self.id_to_label else y for y in x]
for x in encoded_input['labels']
]
output = self.model(**{
k: torch.tensor(encoded_input[k]).to(self.device) for k in input_keys if k in encoded_input
})
prob = torch.softmax(output['logits'], dim=-1)
pred = torch.max(prob, dim=-1)[1].cpu().detach().int().tolist()
preds += [[self.id_to_label[_p] for _p in p] for p in pred]
return [{"prediction": p, "label": i} for p, i in zip(preds, labels)]
def tokenize_and_align_labels(self, examples):
tokens = [[preprocess(self.model_name, w) for w in t] for t in examples["text_tokenized"]]
tokenized_inputs = self.tokenizer(
tokens,
truncation=True,
is_split_into_words=True,
padding="max_length",
max_length=128
)
all_labels = examples["gold_label_sequence"]
new_labels = []
for token, label in zip(tokens, all_labels):
tmp_labels = [-100]
for to, la in zip(token, label):
to_tokenized = self.tokenizer.tokenize(to)
tmp_labels += [la] * len(to_tokenized)
if len(tmp_labels) > 128:
tmp_labels = tmp_labels[:128]
else:
tmp_labels = tmp_labels + [-100] * (128 - len(tmp_labels))
new_labels.append(tmp_labels)
tokenized_inputs["labels"] = new_labels
return tokenized_inputs
if __name__ == '__main__':
model_list = [
"roberta-base",
"bertweet-base",
"bernice",
"roberta-large",
"bertweet-large",
"twitter-roberta-base-2019-90m",
"twitter-roberta-base-dec2020",
"twitter-roberta-base-2021-124m",
"twitter-roberta-base-2022-154m",
"twitter-roberta-large-2022-154m"
]
for model_m in model_list:
alias = f"tweettemposhift/ner-ner_temporal-{model_m}"
NER(alias).get_prediction(export_dir=f"prediction_files/{os.path.basename(alias)}", batch_size=32)
for random_r in range(4):
for seed_s in range(3):
alias = f"tweettemposhift/ner-ner_random{random_r}_seed{seed_s}-{model_m}"
NER(alias).get_prediction(export_dir=f"prediction_files/{os.path.basename(alias)}", batch_size=32)
|