🚀 波兰语情感分类
这是一个用于波兰语情感分类的项目,借助预训练模型对文本进行情感分析,可判断文本情感为积极、消极或中性。
🚀 快速开始
以下是使用该模型进行情感分类的示例代码:
import numpy as np
from transformers import AutoTokenizer, AutoModelForSequenceClassification
id2label = {0: "negative", 1: "neutral", 2: "positive"}
tokenizer = AutoTokenizer.from_pretrained("Voicelab/herbert-base-cased-sentiment")
model = AutoModelForSequenceClassification.from_pretrained("Voicelab/herbert-base-cased-sentiment")
input = ["Ale fajnie, spadł dzisiaj śnieg! Ulepimy dziś bałwana?"]
encoding = tokenizer(
input,
add_special_tokens=True,
return_token_type_ids=True,
truncation=True,
padding='max_length',
return_attention_mask=True,
return_tensors='pt',
)
output = model(**encoding).logits.to("cpu").detach().numpy()
prediction = id2label[np.argmax(output)]
print(input, "--->", prediction)
预测输出:
['Ale fajnie, spadł dzisiaj śnieg! Ulepimy dziś bałwana?'] ---> positive
💻 使用示例
基础用法
import numpy as np
from transformers import AutoTokenizer, AutoModelForSequenceClassification
id2label = {0: "negative", 1: "neutral", 2: "positive"}
tokenizer = AutoTokenizer.from_pretrained("Voicelab/herbert-base-cased-sentiment")
model = AutoModelForSequenceClassification.from_pretrained("Voicelab/herbert-base-cased-sentiment")
input = ["Ale fajnie, spadł dzisiaj śnieg! Ulepimy dziś bałwana?"]
encoding = tokenizer(
input,
add_special_tokens=True,
return_token_type_ids=True,
truncation=True,
padding='max_length',
return_attention_mask=True,
return_tensors='pt',
)
output = model(**encoding).logits.to("cpu").detach().numpy()
prediction = id2label[np.argmax(output)]
print(input, "--->", prediction)
📚 详细文档
概述
📄 许可证
本项目采用CC BY 4.0许可证。