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eval_one_by_one.py
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eval_one_by_one.py
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import os
import argparse
import logging
import ujson
import torch
from accelerate.logging import get_logger
from accelerate.utils import set_seed
from transformers import BertConfig, BertTokenizer
from sklearn.metrics import classification_report
from torch.nn.functional import softmax
from utils.models import BertForSequenceClassification, RobertaForSequenceClassification
MODEL_CLASS_MAPPING = {
'bert-base-chinese': BertForSequenceClassification,
'hfl/chinese-roberta-wwm-ext-large': RobertaForSequenceClassification
}
logger = get_logger(__name__)
label_mapping = {
0: 'Nonsense',
1: 'Humanoid Mimicry',
2: 'Linguistic Neglect',
3: 'Unamiable Judgment',
4: 'Toxic Language',
5: 'Unauthorized Preachment',
6: 'Nonfactual Statement',
7: 'Safe Response'
}
def parse_args():
parser = argparse.ArgumentParser(
'Finetune a transformers model on a text classification task.')
parser.add_argument(
'--max_length',
type=int,
default=512,
help=
('The maximum total input sequence length after tokenization. Sequences longer than this will be truncated,'
' sequences shorter will be padded if `--pad_to_max_length` is passed.'
))
parser.add_argument(
'--pad_to_max_length',
action='store_true',
help=
'If passed, pad all samples to `max_length`. Otherwise, dynamic padding is used.'
)
parser.add_argument(
'--model_name_or_path',
type=str,
help=
'Path to pretrained model or model identifier from huggingface.co/models.',
default='bert-base-chinese')
parser.add_argument('--seed',
type=int,
default=42,
help='A seed for reproducible training.')
parser.add_argument('--cuda', type=str, default='0', help='cuda device')
args = parser.parse_args()
return args
def main():
args = parse_args()
os.environ['CUDA_VISIBLE_DEVICES'] = args.cuda
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
datefmt='%m/%d/%Y %H:%M:%S',
level=logging.INFO,
)
# If passed along, set the training seed now.
if args.seed is not None:
set_seed(args.seed)
num_labels = 8
config = BertConfig.from_pretrained(args.model_name_or_path,
num_labels=num_labels,
finetuning_task='text classification')
tokenizer = BertTokenizer.from_pretrained(
args.model_name_or_path,
use_fast=False,
never_split=['[seeker]', '[supporter]'])
tokenizer.vocab['[seeker]'] = tokenizer.vocab.pop('[unused1]')
tokenizer.vocab['[supporter]'] = tokenizer.vocab.pop('[unused2]')
MODEL_CLASS = MODEL_CLASS_MAPPING[args.model_name_or_path]
model = MODEL_CLASS(config, args.model_name_or_path)
model_name_or_path: str = args.model_name_or_path
model_name_or_path = model_name_or_path.replace('/', '-')
PATH = f'out/{model_name_or_path}/{args.seed}/pytorch_model.bin'
model.load_state_dict(torch.load(PATH))
model.cuda()
padding = 'max_length' if args.pad_to_max_length else False
model.eval()
with open('./data/test.json', 'r', encoding='utf-8') as f:
test_data = ujson.load(f)
target_dir = f'./finetuned_model_predict/{model_name_or_path}/{args.seed}'
os.makedirs(target_dir, exist_ok=True)
existings = os.listdir(target_dir)
for idx, item in enumerate(test_data):
if f'{idx}.json' in existings:
print(f'{idx}.json DONE')
else:
context = item['context']
response = item['response']
result = tokenizer.encode_plus(text=context,
text_pair=response,
padding=padding,
max_length=args.max_length,
truncation=True,
add_special_tokens=True,
return_token_type_ids=True,
return_tensors='pt')
result = result.to('cuda')
with torch.no_grad():
outputs = model(**result)
predictions = outputs.logits.argmax(dim=-1)
pred_label = predictions.item()
item['predict_label'] = pred_label
with open(f'{target_dir}/{idx}.json', 'w',
encoding='utf-8') as f:
ujson.dump(item, f, ensure_ascii=False, indent=2)
if __name__ == '__main__':
main()
print('done')