File size: 3,315 Bytes
4d1afa4 | 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 | import json
from tqdm import tqdm
import re
import time
import random
import os
from llm import LLM
# model_name = "claude-3-7-sonnet@20250219"
# model_name = "gemini-2.0-flash-lite"
# model_name = "gemini-2.5-pro-exp-03-25"
# model_name = "gpt-4o-240513"
model_name = "gpt-4.1"
# model_name = "Qwen/Qwen2.5-72B-Instruct-Turbo"
# model_name = "deepseek-ai/DeepSeek-V3"
# model_name = "deepseek-ai/DeepSeek-R1"
if "/" in model_name:
file_name = model_name.split("/")[-1]
else:
file_name = model_name
model = LLM(model_name)
def extract_answer(text):
"""
Extracts the model answer from the given text.
Expected format is <positive> or <negative>.
Args:
text (str): The input text containing the answer.
Returns:
str or None: Returns 'positive' or 'negative' if found, else None.
"""
match = re.search(r"<(positive|negative)>", text.strip(), re.IGNORECASE)
return match.group(1).lower() if match else None
# if model_name == "deepseek-ai/DeepSeek-R1":
# extract_idioms = extract_idioms_r1
# else:
# extract_idioms = extract_idioms_others
# Load data
with open("dataset/dataset-connotation-verified.json", mode='r', encoding='utf-8') as f:
data = json.load(f)
# Try to resume from existing results
results_path = f"results-connotation/{file_name}.json"
if os.path.exists(results_path):
with open(results_path, mode='r', encoding='utf-8') as f:
results = json.load(f)
else:
results = {}
# Loop and resume inference
for line in tqdm(data):
index = str(line["index"])
if index in results:
continue # Skip already processed
prompt = f"""Please determine the evaluative connotation of the following Chinese idiom. Classify the idiom as either positive (with a favorable meaning) or negative (with an unfavorable meaning). Do not choose neutral.
The idiom is as follows:
{line["idiom"]}
Please provide your final answer in the format:
<positive> or <negative>"""
max_retries = 5
retry_count = 0
success = False
while retry_count < max_retries and not success:
try:
response = model.call_llm(prompt, max_tokens=1024)
results[index] = {"response_text": response, "answer": extract_answer(response), "label": line["sentiment"]}
# Save after each successful inference
with open(results_path, mode='w', encoding='utf-8') as f:
json.dump(results, f, indent=4, ensure_ascii=False)
success = True
except Exception as e:
retry_count += 1
print(f"Error at index {index} (attempt {retry_count}/{max_retries}): {e}")
# If this is a rate limit or overloaded error, wait longer
if "overloaded" in str(e).lower() or "rate limit" in str(e).lower() or '429' in str(e).lower():
longer_delay = 10 * (2 ** retry_count) + random.uniform(0, 5)
print(f"Rate limit hit. Waiting {longer_delay:.2f} seconds...")
time.sleep(longer_delay)
# If we've used all our retries, save progress and exit
if retry_count >= max_retries:
print(f"Max retries reached for index {index}. Saving and continuing.") |