majiahui@haimaqingfan.com
2 weeks ago
1 changed files with 250 additions and 0 deletions
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import json |
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import os |
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import re |
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# os.environ["WANDB_DISABLED"] = "true" |
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# 设置CUDA设备 |
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os.environ['CUDA_VISIBLE_DEVICES'] = '2' |
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import logging |
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import os |
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import random |
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import sys |
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from dataclasses import dataclass, field |
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from typing import Optional |
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import datasets |
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import evaluate |
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import numpy as np |
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from datasets import load_dataset |
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from tqdm import tqdm |
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import transformers |
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from transformers import ( |
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AutoConfig, |
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AutoModelForSequenceClassification, |
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AutoTokenizer, |
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DataCollatorWithPadding, |
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EvalPrediction, |
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HfArgumentParser, |
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PretrainedConfig, |
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Trainer, |
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TrainingArguments, |
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default_data_collator, |
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set_seed, |
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BertTokenizer, |
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BertModel, |
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BigBirdTokenizer, |
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BigBirdForSequenceClassification |
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) |
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import torch |
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# 检查GPU是否可用 |
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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') |
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print(f"使用设备: {device}") |
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lable_2_id_fenji = { |
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"标题": 0, |
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"正文": 1, |
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"无用类别": 2 |
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} |
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# lable_2_id_fenji = { |
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# "标题+正文": 0, |
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# "无用类别": 1 |
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# } |
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tokenizer = AutoTokenizer.from_pretrained( |
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"data_zong_shout_3", |
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use_fast=True, |
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revision="main", |
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trust_remote_code=False, |
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) |
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model_name = "data_zong_shout_3" |
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config = AutoConfig.from_pretrained( |
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model_name, |
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num_labels=len(lable_2_id_fenji), |
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revision="main", |
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trust_remote_code=False |
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) |
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model_roberta = AutoModelForSequenceClassification.from_pretrained( |
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model_name, |
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config=config, |
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revision="main", |
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trust_remote_code=False, |
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ignore_mismatched_sizes=False, |
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).to(device) |
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model_name = "data_zong_no_start_shout_3" |
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config = AutoConfig.from_pretrained( |
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model_name, |
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num_labels=len(lable_2_id_fenji), |
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revision="main", |
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trust_remote_code=False |
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) |
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model_roberta_no_start = AutoModelForSequenceClassification.from_pretrained( |
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model_name, |
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config=config, |
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revision="main", |
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trust_remote_code=False, |
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ignore_mismatched_sizes=False, |
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).to(device) |
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model_name = "data_zong_no_end_shout_3" |
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config = AutoConfig.from_pretrained( |
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model_name, |
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num_labels=len(lable_2_id_fenji), |
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revision="main", |
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trust_remote_code=False |
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) |
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model_roberta_no_end = AutoModelForSequenceClassification.from_pretrained( |
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model_name, |
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config=config, |
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revision="main", |
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trust_remote_code=False, |
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ignore_mismatched_sizes=False, |
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).to(device) |
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# lable_2_id_fenji = { |
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# "标题": 0, |
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# "正文": 1, |
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# "无用类别": 2 |
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# } |
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id_2_lable = {} |
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for i in lable_2_id_fenji: |
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if lable_2_id_fenji[i] not in id_2_lable: |
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id_2_lable[lable_2_id_fenji[i]] = i |
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import pandas as pd |
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import torch |
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data_zong_1 = pd.read_csv("data/data_zong_dev_2.csv").values.tolist() |
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# data_zong_2 = pd.read_csv("data/data_zong_train.csv").values.tolist() |
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# data_zong = data_zong_1 + data_zong_2 |
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data_zong = data_zong_1 |
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output_text = "" |
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output_text_false = "" |
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zong_len = 0 |
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zhengque = 0 |
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tongji_false = {} |
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data_zong_true = [] |
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data_zong_false = [] |
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for i in tqdm(range(len(data_zong))): |
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zong_len += 1 |
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paper = data_zong[i][0] |
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lable_true = data_zong[i][1] |
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paper_new_start_1, paper_new_start_2 = paper.split("<Start>") |
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paper_new_end_1, paper_new_end_2 = paper.split("<End>") |
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# 视野前后7句 |
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paper_new_start_1_jiequ = "\n".join(paper_new_start_1.strip("\n").split("\n")[-7:]) |
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paper_new_end_2_jiequ = "\n".join(paper_new_end_2.strip("\n").split("\n")[:7]) |
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paper_object = "<Start>" + paper_new_start_2.split("\n")[0] |
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paper_zhong = "\n".join([paper_new_start_1_jiequ, paper_object, paper_new_end_2_jiequ]) |
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# 视野前15句 |
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paper_new_start_1_jiequ = "\n".join(paper_new_start_1.strip("\n").split("\n")[-15:]) |
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paper_qian = "\n".join([paper_new_start_1_jiequ, paper_object]) |
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# 视野后15句 |
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paper_new_end_2_jiequ = "\n".join(paper_new_end_2.strip("\n").split("\n")[:15]) |
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paper_hou = "\n".join([paper_object, paper_new_end_2_jiequ]) |
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# 目标句子在中间预测结果 |
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sentence_list = [paper_zhong] |
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# sentence_list = [data[1][0]] |
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result = tokenizer(sentence_list, padding="max_length", max_length=512, truncation=True, return_tensors="pt") |
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result_on_device = {key: value.to(device) for key, value in result.items()} |
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logits = model_roberta(**result_on_device) |
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predicted_class_idx_zhong = torch.argmax(logits[0], dim=1).item() |
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# |
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sentence_list = [paper_qian] |
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# sentence_list = [data[1][0]] |
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result = tokenizer(sentence_list, padding="max_length", max_length=512, truncation=True, return_tensors="pt") |
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result_on_device = {key: value.to(device) for key, value in result.items()} |
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logits = model_roberta_no_end(**result_on_device) |
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predicted_class_idx_qian = torch.argmax(logits[0], dim=1).item() |
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sentence_list = [paper_hou] |
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# sentence_list = [data[1][0]] |
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result = tokenizer(sentence_list, padding="max_length", max_length=512, truncation=True, return_tensors="pt") |
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result_on_device = {key: value.to(device) for key, value in result.items()} |
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logits = model_roberta_no_start(**result_on_device) |
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predicted_class_idx_hou = torch.argmax(logits[0], dim=1).item() |
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output_text += paper |
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output_text += "\n" |
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output_text += f"前15句预测的类别索引: {id_2_lable[predicted_class_idx_qian]}\n" |
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output_text += f"后15句预测的类别索引: {id_2_lable[predicted_class_idx_hou]}\n" |
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output_text += f"中间句子预测的类别索引: {id_2_lable[predicted_class_idx_zhong]}\n" |
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output_text += f"真实的类别索引: {id_2_lable[lable_true]}\n" |
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output_text += "\n==============================================================================\n" |
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id_2_len = {} |
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for i in [predicted_class_idx_qian, predicted_class_idx_hou, predicted_class_idx_zhong]: |
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if i not in id_2_len: |
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id_2_len[i] = 1 |
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else: |
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id_2_len[i] += 1 |
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queding = False |
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predicted_class_idx = "" |
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for i in id_2_len: |
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if id_2_len[i] >= 2: |
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queding = True |
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predicted_class_idx = i |
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if queding == True and predicted_class_idx == lable_true: |
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zhengque += 1 |
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data_zong_true.append([paper, lable_true]) |
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else: |
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# jian = "-true-" + id_2_lable[lable_true] + "-false-" + id_2_lable[predicted_class_idx] |
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# if jian not in tongji_false: |
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# tongji_false[jian] = 1 |
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# else: |
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# tongji_false[jian] += 1 |
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output_text_false += paper |
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output_text_false += "\n" |
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output_text_false += f"前15句预测的类别索引: {id_2_lable[predicted_class_idx_qian]}\n" |
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output_text_false += f"后15句预测的类别索引: {id_2_lable[predicted_class_idx_hou]}\n" |
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output_text_false += f"中间句子预测的类别索引: {id_2_lable[predicted_class_idx_zhong]}\n" |
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output_text_false += f"真实的类别索引: {id_2_lable[lable_true]}\n" |
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output_text_false += "\n==============================================================================\n" |
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data_zong_false.append([paper]) |
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''' |
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{"text": "Terrible customer service.", "label": ["negative"]} |
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{"text": "Really great transaction.", "label": ["positive"]} |
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{"text": "Great price.", "label": ["positive"]} |
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''' |
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# with open("data/data_zong_false.jsonl", "w", encoding="utf-8") as f: |
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# for i in data_zong_false: |
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# f.write(json.dumps({ |
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# "text": i[0], |
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# "label": [] |
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# }, ensure_ascii=False)) |
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# f.write("\n") |
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# |
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# pd.DataFrame(data_zong_true, columns=["sentence", "label"]).to_csv("data/data_zong_true.csv", index=False, encoding="utf-8") |
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with open("data/data_zong_output_text_dev_roberta_zonghe.txt", "w", encoding="utf-8") as f: |
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f.write(output_text) |
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with open("data/data_zong_output_text_dev_roberta_zonghe_false.txt", "w", encoding="utf-8") as f: |
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f.write(output_text_false) |
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print(zhengque/zong_len) |
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print(tongji_false) |
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