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update readme update readme update readme Update benchmarks.py Update download_adapters.sh Update esft.py
253 lines
10 KiB
Python
253 lines
10 KiB
Python
import torch
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from transformers import pipeline, TextGenerationPipeline
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import json
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import time
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import re
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class BaseEvaluator:
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def __init__(self, dataset, config):
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self.dataset = dataset
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self.max_new_tokens = config['max_new_tokens']
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self.batch_size = config['eval_batch_size']
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def infer(self, model, tokenizer):
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generator = pipeline('text-generation', model=model, tokenizer=tokenizer)
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input_text = [i['prompt'] for i in self.dataset]
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responses = generator(input_text, max_new_tokens=self.max_new_tokens, do_sample=False, return_full_text=False, temperature=None, top_p=None, batch_size=self.batch_size)
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output = [{"prompt": input_text[i], "raw_prediction": responses[i][0]['generated_text'], "raw_answers": self.dataset[i]['raw_answers']} for i in range(len(responses))]
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return output
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def eval_metric(self, results):
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scores = []
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for sample in results:
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raw_prediction, raw_answers = sample["raw_prediction"], sample["raw_answers"]
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prediction, answers = self.post_process(raw_prediction, raw_answers)
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score = self._metrics(prediction, answers[0])
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scores.append(score)
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return scores
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def post_process(self, raw_prediction, ground_truths):
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pred = raw_prediction.strip()
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if pred == "":
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pred = "None"
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pred.strip(".。")
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ground_truth = ground_truths[0]
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return pred, [ground_truth]
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def _metrics(self, prediction, ground_truth):
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raise NotImplementedError
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def evaluate(self, model, tokenizer):
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print("Running inference on evaluation dataset...")
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results = self.infer(model, tokenizer)
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print("Evaluating results...")
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metrics = self.eval_metric(results)
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print("Evaluation complete. The result is as follows:")
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print(f"Average score: {sum(metrics) / len(metrics)}")
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return results, metrics
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class GPT4Evaluator(BaseEvaluator):
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def __init__(self, dataset, config):
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super().__init__(dataset, config)
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import openai
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self.client = openai.AzureOpenAI(
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api_key=config['openai_api_key'],
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api_version="2024-02-15-preview"
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)
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def query_gpt4(self, text):
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# try for 5 times
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MAX_TRIAL=5
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for i in range(MAX_TRIAL):
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try:
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chat_completion = self.client.chat.completions.create(
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model="gpt-4-1106-preview",
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messages=[
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{"role": "system", "content": "You are a helpful assistant. Follow the user's instructions carefully. Respond using markdown."},
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{"role": "user", "content": text}
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],
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max_tokens=80
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)
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response_text = chat_completion.choices[0].message.content
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break
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except Exception as e:
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print("ERROR:", e)
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print(f"error in connecting to OpenAI server for {i+1}-th time. try again")
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response_text = ""
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time.sleep(10)
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return response_text
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def parse_gpt4(self, response_text):
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score = re.findall(self.pattern, response_text)
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if score:
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score = float(score[0]) / 10
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else:
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score = 0.0
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print("GPT4没有给出合理的分数:", response_text)
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return score
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@property
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def template(self):
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raise NotImplementedError
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@property
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def pattern(self):
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raise NotImplementedError
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def _metrics(self, prediction, ground_truth):
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text = self.template.format(prediction=prediction, ground_truth=ground_truth)
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response_text = self.query_gpt4(text)
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score = self.parse_gpt4(response_text)
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return score
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class IntentEvaluator(BaseEvaluator):
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def post_process(self, raw_prediction, ground_truths):
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pred = raw_prediction.strip()
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if pred == "":
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pred = "None"
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pred = pred.strip('.。')
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if "```json" in pred:
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try:
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pred = pred[pred.index("```json") + 7:]
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pred = pred[:pred.index("```")]
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except:
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print("unable to parse answer", pred)
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pred = "{}"
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if "\n" in pred:
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pred = [i for i in pred.split("\n") if i][0]
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pred = pred.strip('.。')
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ground_truth = ground_truths[0]
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return pred, [ground_truth]
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def _metrics(self, prediction, ground_truth):
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ground_truth = json.loads(ground_truth)
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try:
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prediction = json.loads(prediction)
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except:
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print(f"unable to parse prediction {prediction} of example with gt {ground_truth}")
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return {'intent_acc': 0, 'slots_f1': 0, 'em': 0}
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intent_em = prediction.get('intent', '') == ground_truth.get('intent', '')
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gt_slots = {(k, str(tuple(sorted([str(i) for i in v]))) if isinstance(v, list) else v) for k, v in ground_truth.get('slots', {}).items()}
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try:
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pred_slots = {(k, str(tuple(sorted([str(i).replace(" ", "") for i in v]))) if isinstance(v, list) else v.replace(" ", "")) for k, v in prediction.get('slots', {}).items()}
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except:
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print(f"OK to parse prediction slots {prediction} of example with gt {ground_truth}, but failed in processing the contents.")
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return {'intent_acc': 0, 'slots_f1': 0, 'em': 0}
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correct_slots = pred_slots.intersection(gt_slots)
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slots_em = (len(correct_slots) == len(pred_slots)) and (len(correct_slots) == len(gt_slots))
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return int(intent_em and slots_em)
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SummaryTemplate = """
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请你进行以下电话总结内容的评分。请依据以下标准综合考量，以确定预测答案与标准答案之间的一致性程度。满分为10分，根据预测答案的准确性、完整性和相关性来逐项扣分。请先给每一项打分并给出总分，再给出打分理由。总分为10分减去每一项扣除分数之和，最低可扣到0分。请以“内容准确性扣x分，详细程度/完整性扣x分，...，总分是：x分"为开头。
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1. **内容准确性**：
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- 预测答案是否准确反映了客户问题或投诉的核心要点。
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- 是否有任何关键信息被错误陈述或误解。
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2. **详细程度/完整性**：
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- 预测答案中包含的细节是否充分，能否覆盖标准答案中所有重要点。
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- 对于任何遗漏的关键信息，应相应减分。
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3. **内容冗余度**：
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- 预测答案是否简洁明了，和标准答案风格一致，不存在冗余信息。
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- 如果预测答案过长或与标准答案风格不一致，需相应减分。
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4. **行动指令正确性**：
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- 预测答案对后续处理的建议或请求是否与标准答案相符。
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- 如果处理建议发生改变或丢失，需相应减分。
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预测答案：{prediction}
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参考答案：{ground_truth}
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"""
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class SummaryEvaluator(GPT4Evaluator):
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@property
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def pattern(self):
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return r"总分是：(\d+\.\d+|\d+)分"
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@property
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def template(self):
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return SummaryTemplate
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LawTemplate = """
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请你进行以下法案判决预测内容的评分。请依据以下标准综合考量，以确定预测答案与标准答案之间的一致性程度。满分为10分，根据预测答案的准确性、完整性和相关性来逐项扣分。请先给每一项打分并给出总分，再给出打分理由。总分为10分减去每一项扣除分数之和，最低可扣到0分。请以“相关性扣x分，完整性扣x分，...，总分是：x分"为开头。
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1. **相关性**：预测答案与标准答案的相关程度是最重要的评分标准。如果预测的判决情况与标准答案完全一致，即所有事实和结果都被精确复制或以不同但等效的方式表述，则应给予高分。若只有部分一致或存在偏差，则根据一致的程度适当扣分。如果没有预测判决内容，扣10分。
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2. **完整性**：评估预测答案是否涵盖了所有标准答案中提到的关键点，包括但不限于当事人、具体金额、责任判定、费用承担等。如果遗漏重要信息，则应相应扣分。
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3. **准确性**：检查预测答案中提及的细节、数字、日期和法律依据是否与标准答案保持一致。任何错误信息均需扣分，并且严重错误应该导致更多的扣分。
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4. **客观性与专业性**：预测答案应客观反映法案内容并使用恰当的法律术语。主观臆断或非专业表达需酌情扣分。
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预测答案：{prediction}
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参考答案：{ground_truth}
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"""
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class LawEvaluator(GPT4Evaluator):
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@property
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def pattern(self):
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return r"总分是：(\d+\.\d+|\d+)分"
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@property
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def template(self):
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return LawTemplate
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TranslationTemplate = """
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You are an expert master in machine translation. Please score the predicted answer against the standard answer out of 10 points based on the following criteria:
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Content accuracy: Does the predicted answer accurately reflect the key points of the reference answer?
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Level of detail/completeness: Does the predicted answer cover all important points from the standard answer?
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Content redundancy: Is the predicted answer concise and consistent with the style of the standard answer?
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Respond following the format:"Content accuracy x points, level of detail/completeness x points, ..., total score: x points". The total score is the average of all the scores. Do not give reasons for your scores.
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Predicted answer: {prediction}
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Reference answer: {ground_truth}
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"""
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class TranslationEvaluator(GPT4Evaluator):
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@property
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def pattern(self):
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return r"score: *?(\d+\.\d+|\d+) *?point"
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@property
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def template(self):
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return TranslationTemplate
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def post_process(self, raw_prediction, ground_truths):
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pred = raw_prediction.strip().split("\n\n")[0]
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if pred == "":
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pred = "None"
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pred.strip(".。")
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ground_truth = ground_truths[0]
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return pred, [ground_truth]
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