|
--- |
|
license: apache-2.0 |
|
language: |
|
- zh |
|
--- |
|
|
|
验证集准确度: 0.9382193411826961 |
|
验证集分类报告: |
|
precision recall f1-score support |
|
|
|
negative 0.93 0.95 0.94 3785 |
|
positive 0.95 0.96 0.95 6919 |
|
neutral 0.93 0.89 0.91 4414 |
|
|
|
accuracy 0.94 15118 |
|
macro avg 0.94 0.93 0.93 15118 |
|
weighted avg 0.94 0.94 0.94 15118 |
|
|
|
大概使用了10w+的数据做了一个基金方面的中文情感分析模型，暂时测试下来还可以，负面方面的文本是有专人处理过的，中性的可能不准确。 |
|
# 返回值解释： |
|
0: 'negative', 1: 'positive', 2: 'neutral' |
|
|
|
|
|
|
|
# 测试代码如下： |
|
import sys |
|
import re |
|
import torch |
|
from transformers import BertTokenizer, BertForSequenceClassification |
|
from torch.nn.functional import softmax |
|
|
|
#设定使用CPU或CUDA |
|
device = 'cuda' if torch.cuda.is_available() else 'cpu' |
|
|
|
#载入预先保存的模型和分词器 |
|
model = BertForSequenceClassification.from_pretrained('sanshizhang/Chinese-Sentiment-Analysis-Fund-Direction') |
|
tokenizer = BertTokenizer.from_pretrained('sanshizhang/Chinese-Sentiment-Analysis-Fund-Direction') |
|
|
|
#确保模型在正确的设备上 |
|
model = model.to(device) |
|
model.eval() # 把模型设置为评估模式 |
|
|
|
#函数定义：进行预测并返回预测概率 |
|
def predict_sentiment(text): |
|
# 编码文本数据 |
|
encoding = tokenizer.encode_plus( |
|
text, |
|
max_length=512, |
|
add_special_tokens=True, |
|
return_token_type_ids=False, |
|
padding='max_length', # 修改此处 |
|
return_attention_mask=True, |
|
return_tensors='pt', |
|
truncation=True |
|
) |
|
|
|
|
|
# 取出输入对应的编码 |
|
input_ids = encoding['input_ids'].to(device) |
|
attention_mask = encoding['attention_mask'].to(device) |
|
|
|
# 不计算梯度 |
|
with torch.no_grad(): |
|
# 产生情感预测的logits |
|
outputs = model(input_ids=input_ids, attention_mask=attention_mask) |
|
|
|
# 使用softmax将logits转换为概率 |
|
probs = softmax(outputs.logits, dim=1) |
|
|
|
# 返回概率和预测的类别 |
|
return probs, torch.argmax(probs, dim=1).cpu().numpy()[0] |
|
|
|
#从命令行参数获取文本，合并并清理特殊字符 |
|
arguments = sys.argv[1:] # 忽略脚本名称 |
|
text = ' '.join(arguments) # 合并为单一字符串 |
|
text = re.sub(r"[^\u4e00-\u9fff\d.a-zA-Z%+\-。！？，、；：（）【】《》“”‘’]", '', text) # 去除特殊字符 |
|
|
|
#print(f"传过来的文本是: {text}") |
|
#进行预测 |
|
probabilities, prediction = predict_sentiment(text) |
|
|
|
sentiment_labels = {0: 'negative', 1: 'positive', 2: 'neutral'} |
|
|
|
#打印出预测的情感及其概率 |
|
predicted_sentiment = sentiment_labels[prediction] |
|
print(f"Predicted sentiment: {predicted_sentiment},Probability:{probabilities[0][prediction].item()}") |
|
#print(f"Probability: {probabilities[0][prediction].item()}") |
