Distilbert Sentiment

🚀 零样本分类模型

本项目是一个基于transformers库的零样本分类模型，使用distilbert-base-uncased作为基础模型，在stanfordnlp/imdb数据集上进行训练，可用于情感分析等零样本分类任务。

🚀 快速开始

下面是使用该模型进行情感分析的示例代码：

# Sample text to predict
text = "I love this movie, it was fantastic!"

# Tokenize the input text
inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)

# Get model predictions
with torch.no_grad():
    outputs = model(**inputs)

# Get the logits (model's raw output)
logits = outputs.logits

# Convert logits to probabilities (if needed) and get the predicted class (0 or 1)
predictions = torch.argmax(logits, dim=-1).item()

# Map the prediction to sentiment labels
labels = {0: "NEGATIVE", 1: "POSITIVE"}  # Assuming binary classification
predicted_label = labels[predictions]

print(f"Predicted Sentiment: {predicted_label}")

💻 使用示例

基础用法

# Sample text to predict
text = "I love this movie, it was fantastic!"

# Tokenize the input text
inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)

# Get model predictions
with torch.no_grad():
    outputs = model(**inputs)

# Get the logits (model's raw output)
logits = outputs.logits

# Convert logits to probabilities (if needed) and get the predicted class (0 or 1)
predictions = torch.argmax(logits, dim=-1).item()

# Map the prediction to sentiment labels
labels = {0: "NEGATIVE", 1: "POSITIVE"}  # Assuming binary classification
predicted_label = labels[predictions]

print(f"Predicted Sentiment: {predicted_label}")

📄 许可证

本项目采用Apache 2.0许可证。

📦 模型信息