39 lines
1.5 KiB
Python
39 lines
1.5 KiB
Python
import os
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# The repo is the working directory at run time, and there is no network.
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os.environ["HF_HUB_OFFLINE"] = "1"
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os.environ["TRANSFORMERS_OFFLINE"] = "1"
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MODEL_ID = "."
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import json
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import pandas as pd
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import torch
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from transformers import AutoTokenizer, AutoModelForCausalLM
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tok = AutoTokenizer.from_pretrained(MODEL_ID)
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model = AutoModelForCausalLM.from_pretrained(
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MODEL_ID, torch_dtype=torch.float16, device_map="auto"
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).eval()
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df = pd.read_csv("/tmp/data/test.csv", dtype=str).fillna("")
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rows = []
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for _, r in df.iterrows():
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messages = [
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{"role": "system", "content":
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"You solve International Linguistics Olympiad problems. Answer every numbered "
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"item. Put each answer on its own line, in order, with no numbering and no extra text."},
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{"role": "user", "content": f"{r['context'].strip()}\n\n{r['query'].strip()}"},
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]
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ids = tok.apply_chat_template(
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messages, add_generation_prompt=True, return_tensors="pt",
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).to(model.device)
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with torch.no_grad():
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out = model.generate(ids, max_new_tokens=512, do_sample=False)
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text = tok.decode(out[0][ids.shape[-1]:], skip_special_tokens=True).strip()
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answers = [ln.strip() for ln in text.splitlines() if ln.strip()]
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rows.append({"id": r["id"], "pred": json.dumps(answers, ensure_ascii=False)})
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print(f"{len(rows)}/{len(df)} done", flush=True)
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pd.DataFrame(rows).to_csv("submission.csv", index=False)
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print("wrote submission.csv", flush=True)
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