Distilbert Sentiment

🚀 零樣本分類模型

本項目是一個基於transformers庫的零樣本分類模型，使用distilbert-base-uncased作為基礎模型，在stanfordnlp/imdb數據集上進行訓練，可用於情感分析等零樣本分類任務。

🚀 快速開始

下面是使用該模型進行情感分析的示例代碼：

# 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

基礎用法

# 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}")

📄 許可證

本項目採用Apache 2.0許可證。

📦 模型信息