mirror of
https://github.com/hiyouga/LLaMA-Factory.git
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131 lines
4.1 KiB
Python
131 lines
4.1 KiB
Python
# coding=utf-8
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# Evaluates fine-tuned models automatically.
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# Usage: python evaluate.py --evalset ceval/ceval-exam:law --split dev --api_base http://localhost:8000/v1 --task_type choice
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# dataset format: question (string), A (string), B (string), C (string), D (string), answer Literal["A", "B", "C", "D"]
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import os
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import fire
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import json
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import openai
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from tqdm import tqdm
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from typing import Literal, Optional
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from datasets import load_dataset
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def format_example_choice(examples):
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model_inputs = {"query": [], "label": []}
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task_template = "请从ABCD四个选项中选出正确的选项，仅输出选项序号。\n{question}\nA. {A}\nB. {B}\nC. {C}\nD. {D}\n答案："
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for i in range(len(examples["id"])):
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query = task_template.format(
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question=examples["question"][i],
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A=examples["A"][i],
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B=examples["B"][i],
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C=examples["C"][i],
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D=examples["D"][i]
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)
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label = examples["answer"][i]
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model_inputs["query"].append(query)
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model_inputs["label"].append(label)
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return model_inputs
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def format_example_cloze(examples):
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model_inputs = {"query": [], "label": []}
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task_template = "请选择正确的答案填空，仅输出正确的选项。\n{question}\n选项：{A}\n{B}\n{C}\n{D}\n答案："
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for i in range(len(examples["id"])):
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query = task_template.format(
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question=examples["question"][i],
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A=examples["A"][i],
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B=examples["B"][i],
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C=examples["C"][i],
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D=examples["D"][i]
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)
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label = examples[examples["answer"][i]][i]
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model_inputs["query"].append(query)
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model_inputs["label"].append(label)
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return model_inputs
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def format_example_openqa(examples):
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model_inputs = {"query": [], "label": []}
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task_template = "回答以下问题：{question}\n答案："
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for i in range(len(examples["id"])):
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query = task_template.format(question=examples["question"][i])
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label = examples[examples["answer"][i]][i]
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model_inputs["query"].append(query)
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model_inputs["label"].append(label)
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return model_inputs
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TASK_DICT = {
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"choice": format_example_choice,
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"cloze": format_example_cloze,
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"openqa": format_example_openqa
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}
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EXT2TYPE = {
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"csv": "csv",
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"json": "json",
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"jsonl": "json"
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}
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def evaluate(
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evalset: str,
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api_base: str,
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split: Optional[str] = "val",
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task_type: Optional[Literal["choice", "cloze", "openqa"]] = "choice",
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n_samples: Optional[int] = 20
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):
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openai.api_base = api_base
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openai.api_key = "none"
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if os.path.isfile(evalset):
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dataset = load_dataset(EXT2TYPE[evalset.split(".")[-1]], data_files=evalset)["train"]
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elif ":" in evalset:
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evalset, subset = evalset.split(":")
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dataset = load_dataset(evalset, subset, split=split)
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else:
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dataset = load_dataset(evalset, split=split)
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n_samples = min(len(dataset), n_samples)
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dataset = dataset.map(TASK_DICT[task_type], batched=True)
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dataset = dataset.select(range(n_samples))
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n_correct = 0
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predictions = []
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for example in tqdm(dataset):
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query, label = example["query"], example["label"]
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predict = openai.ChatCompletion.create(
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model="default",
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messages=[{"role": "user", "content": query}],
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temperature=0.01,
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top_p=0.01,
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max_new_tokens=20
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).choices[0].message.content
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if task_type == "choice" and predict[0].lower() == label[0].lower():
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n_correct += 1
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if task_type == "cloze" and label in [predict[:len(label)], predict[-len(label):]]:
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n_correct += 1
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if task_type == "openqa" and label in predict:
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n_correct += 1
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predictions.append({
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"query": query,
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"label": label,
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"predict": predict
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})
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print("Result: {}/{}\nAccuracy: {:.2f}%".format(n_correct, n_samples, n_correct / n_samples * 100))
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with open("result.json", "w", encoding="utf-8") as f:
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json.dump(predictions, f, indent=2, ensure_ascii=False)
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if __name__ == "__main__":
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fire.Fire(evaluate)
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