Spaces:
Sleeping
Sleeping
File size: 9,861 Bytes
3229aff 7db58d4 3229aff 7db58d4 3229aff 7db58d4 3229aff 7db58d4 3229aff 7db58d4 3229aff 7db58d4 3229aff 7db58d4 3229aff 7db58d4 3229aff 7db58d4 3229aff 7db58d4 3229aff 7db58d4 3229aff 7db58d4 3229aff 93a695d 7db58d4 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 |
import json
import numpy as np
import random, math
import argparse
import os
import json, tqdm
import yaml
from models.models import GroqModel
import time
import random
def processdata(instance, noise_rate, passage_num, filename, correct_rate = 0):
query = instance['query']
ans = instance['answer']
neg_num = math.ceil(passage_num * noise_rate)
pos_num = passage_num - neg_num
if '_int' in filename:
for i in instance['positive']:
random.shuffle(i)
print(len(instance['positive']))
docs = [i[0] for i in instance['positive']]
if len(docs) < pos_num:
maxnum = max([len(i) for i in instance['positive']])
for i in range(1,maxnum):
for j in instance['positive']:
if len(j) > i:
docs.append(j[i])
if len(docs) == pos_num:
break
if len(docs) == pos_num:
break
neg_num = passage_num - len(docs)
if neg_num > 0:
negative = instance['negative'][:neg_num]
docs += negative
elif '_fact' in filename:
correct_num = math.ceil(passage_num * correct_rate)
pos_num = passage_num - neg_num - correct_num
indexs = list(range(len(instance['positive'])))
selected = random.sample(indexs,min(len(indexs),pos_num))
docs = [instance['positive_wrong'][i] for i in selected]
remain = [i for i in indexs if i not in selected]
if correct_num > 0 and len(remain) > 0:
docs += [instance['positive'][i] for i in random.sample(remain,min(len(remain),correct_num))]
if neg_num > 0:
docs += instance['negative'][:neg_num]
else:
if noise_rate == 1:
neg_num = passage_num
pos_num = 0
else:
if neg_num > len(instance['negative']):
neg_num = len(instance['negative'])
pos_num = passage_num - neg_num
elif pos_num > len(instance['positive']):
pos_num = len(instance['positive'])
neg_num = passage_num - pos_num
positive = instance['positive'][:pos_num]
negative = instance['negative'][:neg_num]
docs = positive + negative
random.shuffle(docs)
return query, ans, docs
def checkanswer(prediction, ground_truth):
prediction = prediction.lower()
if type(ground_truth) is not list:
ground_truth = [ground_truth]
labels = []
for instance in ground_truth:
flag = True
if type(instance) == list:
flag = False
instance = [i.lower() for i in instance]
for i in instance:
if i in prediction:
flag = True
break
else:
instance = instance.lower()
if instance not in prediction:
flag = False
labels.append(int(flag))
return labels
def getevalue(results):
results = np.array(results)
results = np.max(results,axis = 0)
if 0 in results:
return False
else:
return True
def predict(query, ground_truth, docs, model, system, instruction, temperature, dataset):
'''
label: 0 for positive, 1 for negative, -1 for not enough information
'''
if len(docs) == 0:
text = instruction.format(QUERY=query, DOCS='')
prediction = model.generate(text, temperature)
else:
docs = '\n'.join(docs)
text = instruction.format(QUERY=query, DOCS=docs)
prediction = model.generate(text, temperature, system)
if 'zh' in dataset:
prediction = prediction.replace(" ","")
if '信息不足' in prediction or 'insufficient information' in prediction:
labels = [-1]
else:
labels = checkanswer(prediction, ground_truth)
factlabel = 0
if '事实性错误' in prediction or 'factual errors' in prediction:
factlabel = 1
return labels,prediction, factlabel
def main3(args):
print(args)
return 'I am from evalue'
def main2(params):
print(' am main2')
parser = argparse.ArgumentParser()
parser.add_argument(
'--modelname', type=str, default='chatgpt',
help='model name'
)
parser.add_argument(
'--dataset', type=str, default='en',
help='evaluetion dataset',
choices=['en','zh','en_int','zh_int','en_fact','zh_fact']
)
parser.add_argument(
'--api_key', type=str, default='api_key',
help='api key of chatgpt'
)
parser.add_argument(
'--plm', type=str, default='THUDM/chatglm-6b',
help='name of plm'
)
parser.add_argument(
'--url', type=str, default='https://api.openai.com/v1/completions',
help='url of chatgpt'
)
parser.add_argument(
'--temp', type=float, default=0.7,
help='corpus id'
)
parser.add_argument(
'--noise_rate', type=float, default=0.0,
help='rate of noisy passages'
)
parser.add_argument(
'--correct_rate', type=float, default=0.0,
help='rate of correct passages'
)
parser.add_argument(
'--passage_num', type=int, default=5,
help='number of external passages'
)
parser.add_argument(
'--factchecking', type=bool, default=False,
help='whether to fact checking'
)
parser.add_argument(
'--max_instances', type=int, default=None,
help='Limit the number of examples to evaluate'
)
help='whether to fact checking'
args = parser.parse_args(params)
modelname = args.modelname
temperature = args.temp
noise_rate = args.noise_rate
passage_num = args.passage_num
instances = []
with open(f'data/{args.dataset}.json','r', encoding='utf-8') as f:
for i, line in enumerate(f):
if args.max_instances and i >= args.max_instances:
break
instances.append(json.loads(line))
if 'en' in args.dataset:
resultpath = 'result-en'
elif 'zh' in args.dataset:
resultpath = 'result-zh'
if not os.path.exists(resultpath):
os.mkdir(resultpath)
if args.factchecking:
prompt = yaml.load(open('config/instruction_fact.yaml', 'r', encoding='utf-8'), Loader=yaml.FullLoader)[args.dataset[:2]]
resultpath = resultpath + '/fact'
else:
prompt = yaml.load(open('config/instruction.yaml', 'r', encoding='utf-8'), Loader=yaml.FullLoader)[args.dataset[:2]]
system = prompt['system']
instruction = prompt['instruction']
model = GroqModel()
filename = f'{resultpath}/prediction_{args.dataset}_{modelname}_temp{temperature}_noise{noise_rate}_passage{passage_num}_correct{args.correct_rate}.json'
useddata = {}
if os.path.exists(filename):
with open(filename) as f:
data = json.loads(line)
useddata[data['id']] = data
results = []
with open(filename,'w') as f:
for instance in tqdm.tqdm(instances):
#if instance['id'] in useddata and instance['query'] == useddata[instance['id']]['query'] and instance['answer'] == useddata[instance['id']]['answer']:
#results.append(useddata[instance['id']])
#f.write(json.dumps(useddata[instance['id']], ensure_ascii=False)+'\n')
#continue
try:
random.seed(2333)
if passage_num == 0:
query = instance['query']
ans = instance['answer']
docs = []
else:
query, ans, docs = processdata(instance, noise_rate, passage_num, args.dataset, args.correct_rate)
label,prediction,factlabel = predict(query, ans, docs, model,system,instruction,temperature,args.dataset)
instance['label'] = label
newinstance = {
'id': instance['id'],
'query': query,
'ans': ans,
'label': [-1],
'label1': label,
'prediction': prediction,
'docs': docs,
'noise_rate': noise_rate,
'factlabel': factlabel
}
results.append(newinstance)
f.write(json.dumps(newinstance, ensure_ascii=False)+'\n')
time.sleep(random.uniform(2, 4))
except Exception as e:
print("Error123:", e)
continue
tt = 0
for i in results:
label = i['label']
if noise_rate == 1 and label[0] == -1:
tt += 1
elif 0 not in label and 1 in label:
tt += 1
scores = {
'all_rate': (tt)/len(results),
'noise_rate': noise_rate,
'tt':tt,
'nums': len(results),
}
if '_fact' in args.dataset:
fact_tt = 0
correct_tt = 0
for i in results:
if i['factlabel'] == 1:
fact_tt += 1
if 0 not in i['label']:
correct_tt += 1
fact_check_rate = fact_tt/len(results)
if fact_tt > 0:
correct_rate = correct_tt/fact_tt
else:
correct_rate = 0
scores['fact_check_rate'] = fact_check_rate
scores['correct_rate'] = correct_rate
scores['fact_tt'] = fact_tt
scores['correct_tt'] = correct_tt
print(scores)
json.dump(scores,open(f'{resultpath}/prediction_{args.dataset}_{modelname}_temp{temperature}_noise{noise_rate}_passage{passage_num}_correct{args.correct_rate}_result.json','w'),ensure_ascii=False,indent=4)
# if __name__ == '__main__':
# # main()
# print('test me here') |