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05b89c8 f88adfa 05b89c8 | 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 | #import subprocess, sys
#subprocess.run([sys.executable, "-m", "pip", "install", "-q",
# "transformers>=4.43", "accelerate>=0.30", "torch>=2.2", "pandas"], check=True)
import os
# The repo is the working directory at run time, and the sandbox has no network.
os.environ["HF_HUB_OFFLINE"] = "1"
os.environ["TRANSFORMERS_OFFLINE"] = "1"
MODEL_ID = "." # "." "./model" # your model's weights are shipped in this repo
import re
import json
import pandas as pd
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
tok = AutoTokenizer.from_pretrained(MODEL_ID)
if tok.pad_token_id is None:
tok.pad_token = tok.eos_token
model = AutoModelForCausalLM.from_pretrained(
MODEL_ID,
torch_dtype=torch.float16,
device_map="auto",
).eval()
df = pd.read_csv("/tmp/data/test.csv", dtype=str).fillna("")
rows = []
# ===== CHANGE HERE — your prompt (the main lever: how you ask the model) =====
SYSTEM = (
"You solve International Linguistics Olympiad problems by reasoning from the "
"data you are given. You may meet a task type you have never seen: read the "
"instruction and the examples, and answer in the same form they use. "
"Common task types and what to give -- "
"translation: the translated form only, in the language the task asks for; "
"fill_blanks: only the missing form for each blank; "
"match_letters: only the option letter (for example A, B, C); "
"text_to_num: the number in digits; "
"num_to_text: the number written out in words, in the language asked; "
"any other type: give exactly what the instruction asks, nothing else. "
"Reason step by step first. Then write a line that says exactly FINAL ANSWERS: "
"and, below it, one answer per line in the order the items are asked -- the "
"bare answer only, no numbering, no quotes, no extra text."
)
# ===== CHANGE HERE — how you read the answers back (must match the format your prompt asks for) =====
def parse_answers(text):
"""Keep only the lines after the last 'FINAL ANSWERS:' marker, one answer per line."""
marker = list(re.finditer(r"(?im)^\s*final answers?\s*:?\s*$", text))
if marker:
text = text[marker[-1].end():]
answers = []
for line in text.splitlines():
line = re.sub(r"^\s*\d+[.)]\s*", "", line).strip() # drop "1. " / "2) " if the model adds it
if line:
answers.append(line)
return answers
for _, r in df.iterrows():
messages = [
{
"role": "system",
"content": SYSTEM
},
{
"role": "user",
"content": f"{r['context'].strip()}\n\n{r['query'].strip()}",
},
]
inputs = tok.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt",
return_dict=True,
).to(model.device)
input_length = inputs["input_ids"].shape[-1]
with torch.no_grad():
out = model.generate(
**inputs,
max_new_tokens=512,
do_sample=False,
pad_token_id=tok.pad_token_id,
)
text = tok.decode(
out[0][input_length:],
skip_special_tokens=True,
).strip()
answers = parse_answers(text)
#answers = [
# line.strip()
# for line in text.splitlines()
# if line.strip()
#]
rows.append({
"id": r["id"],
"pred": json.dumps(answers, ensure_ascii=False),
})
pd.DataFrame(rows).to_csv("submission.csv", index=False) |