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039cd00 | 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 | # import json
# import os
# from transformers import AutoTokenizer
# from tqdm import tqdm
# # ================= 設定區 =================
# MODEL_PATH = "Salesforce/Llama-xLAM-2-8b-fc-r"
# MAX_LENGTH = 16384
# INPUT_FILE = "sampled20new_fix2_fix2_toollist_remove_sys_Anyscale_no_tail.jsonl" # 輸入改成 .jsonl
# OUTPUT_FILE = "sampled20new_fix2_fix2_toollist_remove_sys_Anyscale_no_tail_dropped.jsonl" # 輸出改成 .jsonl
# # =========================================
# def convert_sharegpt_to_standard(conversations):
# """
# (同上) 將 ShareGPT 轉換為標準格式以套用 Template
# """
# new_messages = []
# for turn in conversations:
# role = turn.get('from', '')
# content = turn.get('value', '')
# if role in ['human', 'user']:
# role = 'user'
# elif role in ['gpt', 'chatgpt', 'assistant', 'model']:
# role = 'assistant'
# elif role in ['system']:
# role = 'system'
# elif role in ['tool', 'function']:
# role = 'tool'
# new_messages.append({"role": role, "content": content})
# return new_messages
# def get_accurate_token_len(tokenizer, entry):
# """
# (同上) 使用 apply_chat_template 獲取真實 Token 數
# """
# messages = []
# if "conversations" in entry:
# messages = convert_sharegpt_to_standard(entry["conversations"])
# elif "messages" in entry:
# messages = entry["messages"]
# elif "instruction" in entry:
# prompt = entry.get("instruction", "") + "\n" + entry.get("input", "")
# response = entry.get("output", "")
# messages = [{"role": "user", "content": prompt}, {"role": "assistant", "content": response}]
# if not messages:
# return len(tokenizer.encode(str(entry), add_special_tokens=False))
# try:
# tokenized_ids = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=False)
# return len(tokenized_ids)
# except Exception as e:
# text = "".join([m['content'] for m in messages])
# return len(tokenizer.encode(text))
# def count_lines(filename):
# """計算總行數以便顯示進度條"""
# with open(filename, 'rb') as f:
# return sum(1 for _ in f)
# def main():
# print(f"Loading tokenizer from {MODEL_PATH}...")
# try:
# tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH, trust_remote_code=True)
# except Exception as e:
# print(f"Error: {e}")
# return
# if not os.path.exists(INPUT_FILE):
# print(f"File not found: {INPUT_FILE}")
# return
# # 計算總行數 (非必要，但為了 tqdm 進度條好看)
# print("Counting total lines...")
# total_lines = count_lines(INPUT_FILE)
# print(f"Processing {INPUT_FILE} ({total_lines} lines)...")
# dropped_count = 0
# valid_count = 0
# # 使用 'w' 模式打開輸出檔，準備一行行寫入
# with open(INPUT_FILE, 'r', encoding='utf-8') as f_in, \
# open(OUTPUT_FILE, 'w', encoding='utf-8') as f_out:
# # 逐行讀取，不佔用大量記憶體
# for line in tqdm(f_in, total=total_lines, desc="Filtering"):
# line = line.strip()
# if not line: continue
# try:
# entry = json.loads(line)
# except json.JSONDecodeError:
# print("Skipping invalid JSON line")
# continue
# length = get_accurate_token_len(tokenizer, entry)
# if length <= MAX_LENGTH:
# # 寫入一行 JSON 字串
# f_out.write(json.dumps(entry, ensure_ascii=False) + '\n')
# valid_count += 1
# else:
# dropped_count += 1
# print(f"\n===== Result =====")
# print(f"Original Lines : {total_lines}")
# print(f"Dropped Lines : {dropped_count} ({(dropped_count/total_lines)*100:.2f}%)")
# print(f"Remaining Lines: {valid_count}")
# print(f"Saved to : {OUTPUT_FILE}")
# if __name__ == "__main__":
# main()
import json
import os
from transformers import AutoTokenizer
from tqdm import tqdm
# MODEL_PATH = "Salesforce/Llama-xLAM-2-8b-fc-r"
# MAX_LENGTH = 16384
# INPUT_FILE = "sampled20new_fix2_fix2_toollist_remove_sys_Anyscale_no_tail.jsonl" # 輸入改成 .jsonl
# OUTPUT_FILE = "sampled20new_fix2_fix2_toollist_remove_sys_Anyscale_no_tail_dropped.jsonl" # 輸出改成 .jsonl
# ================= 設定區 =================
# MODEL_PATH = "Qwen/Qwen2.5-7B-Instruct"
# MODEL_PATH = "meta-llama/Llama-3.1-8B-Instruct"
MODEL_PATH = "Salesforce/Llama-xLAM-2-8b-fc-r"
MAX_LENGTH = 5120
# multi_turn_miss_func_zh_tw_function_mix_sharegpt.jsonl
INPUT_FILE = "multi_turn_miss_func_zh_tw_function_mix_sharegpt.jsonl" # 輸入改成 .jsonl
OUTPUT_FILE = "multi_turn_miss_func_zh_tw_function_mix_sharegpt_dropped.jsonl" # 輸出改成 .jsonl
DROPPED_FILE = "multi_turn_miss_func_zh_tw_function_mix_sharegpt_too_long.jsonl" # 濾掉的 (Risky)
# =========================================
def convert_sharegpt_to_standard(conversations):
"""將 ShareGPT 轉換為標準格式以套用 Template"""
new_messages = []
for turn in conversations:
role = turn.get('from', '')
content = turn.get('value', '')
if role in ['human', 'user']: role = 'user'
elif role in ['gpt', 'chatgpt', 'assistant', 'model']: role = 'assistant'
elif role in ['system']: role = 'system'
elif role in ['tool', 'function']: role = 'tool'
new_messages.append({"role": role, "content": content})
return new_messages
def get_accurate_token_len(tokenizer, entry):
"""使用 apply_chat_template 獲取真實 Token 數"""
messages = []
if "conversations" in entry:
messages = convert_sharegpt_to_standard(entry["conversations"])
elif "messages" in entry:
messages = entry["messages"]
elif "instruction" in entry:
prompt = entry.get("instruction", "") + "\n" + entry.get("input", "")
response = entry.get("output", "")
messages = [{"role": "user", "content": prompt}, {"role": "assistant", "content": response}]
if not messages:
return len(tokenizer.encode(str(entry), add_special_tokens=False))
try:
tokenized_ids = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=False)
return len(tokenized_ids)
except Exception:
text = "".join([m['content'] for m in messages])
return len(tokenizer.encode(text))
def count_lines(filename):
with open(filename, 'rb') as f:
return sum(1 for _ in f)
def main():
print(f"Loading tokenizer from {MODEL_PATH}...")
try:
tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH, trust_remote_code=True)
except Exception as e:
print(f"Error: {e}")
return
if not os.path.exists(INPUT_FILE):
print(f"File not found: {INPUT_FILE}")
return
total_lines = count_lines(INPUT_FILE)
print(f"Processing {INPUT_FILE} ({total_lines} lines)...")
dropped_count = 0
valid_count = 0
# 同時打開三個檔案：讀取原始檔、寫入保留檔、寫入丟棄檔
with open(INPUT_FILE, 'r', encoding='utf-8') as f_in, \
open(OUTPUT_FILE, 'w', encoding='utf-8') as f_valid, \
open(DROPPED_FILE, 'w', encoding='utf-8') as f_dropped:
for line in tqdm(f_in, total=total_lines, desc="Filtering"):
line = line.strip()
if not line: continue
try:
entry = json.loads(line)
except json.JSONDecodeError:
continue
length = get_accurate_token_len(tokenizer, entry)
# 準備要寫入的 JSON 字串
json_str = json.dumps(entry, ensure_ascii=False) + '\n'
if length <= MAX_LENGTH:
f_valid.write(json_str)
valid_count += 1
else:
# 這裡將過長的數據寫入 dropped file
f_dropped.write(json_str)
dropped_count += 1
print(f"\n===== Result =====")
print(f"Original Lines : {total_lines}")
print(f"Valid Lines : {valid_count} -> Saved to {OUTPUT_FILE}")
print(f"Dropped Lines : {dropped_count} ({(dropped_count/total_lines)*100:.2f}%) -> Saved to {DROPPED_FILE}")
if __name__ == "__main__":
main() |