在MELD数据集上利用BERT得到句向量表示

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已于 2022-07-21 15:59:23 修改 · 1.1k 阅读

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#bert #深度学习 #pytorch

于 2022-06-01 12:45:47 首次发布

本文介绍了如何使用预训练的BERT模型对MELD情感对话数据集进行处理，提取句子级向量，并将其应用于情感标签预测。作者详细展示了加载模型、编码文本、获取特征向量的过程，并提供了关键代码片段。

目标数据格式：
[[{“text”: “Hi, I need an ID.”, “speaker”: “Ses05F_impro01_F”, “label”: “neu”, “feature”: [-0.16701631247997284, -0.5138905048370361,…]},
{“text”: “ahh Yeah, this is the wrong line. I’m sorry. You need to go back over to line two B. That’s where you should have started from.”, “speaker”: “Ses05F_impro01_M”, “label”: “neu”, “feature”: [-0.0958164632320404, -0.3956751525402069,…]}], <<<<<注意这里已有 1 batch已结束
[{“text”: “Hi, I need an ID.”, “speaker”: “Ses05F_impro01_F”, “label”: “neu”, “feature”: [-0.16701631247997284, -0.5138905048370361,…]},
{“text”: “ahh Yeah, this is the wrong line. I’m sorry. You need to go back over to line two B. That’s where you should have started from.”, “speaker”: “Ses05F_impro01_M”, “label”: “neu”, “feature”: [-0.0958164632320404, -0.3956751525402069,…]}]]
说明：data[i]第i个对话，data[i][j]第i个对话的第j句话，每句话由768维向量表示
MELD数据集下载：https://affective-meld.github.io/
bert-base-uncased模型下载：https://huggingface.co/bert-base-uncased/tree/main
pytorch下载pytorch_model.bin, config.json, vocab.txt

import os
os.environ["CUDA_VISIBLE_DEVICES"] = '0'
import pandas as pd
import torch
import gc
import pickle as pkl
from transformers import BertTokenizer, BertModel
from tqdm import tqdm

file_path = './MELD/dev_sent_emo.csv'
model_path

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