distilbert-base-uncased-rx

Fine-Tuned DistilBERT for Sentiment Analysis

Model Description

This model is a fine-tuned version of distilbert-base-uncased on the amazon_polarity dataset. It is designed for binary sentiment classification, predicting whether a given text expresses a positive (1) or negative (0) sentiment. The model leverages the lightweight architecture of DistilBERT, making it efficient for deployment while maintaining strong performance.

• Developed by: [Jack.RX Tech]
• Model Type: Transformer-based text classification
• Base Model: distilbert-base-uncased
• Language: English
• License: Apache 2.0

Intended Uses

This model is intended for sentiment analysis tasks, particularly in analyzing product reviews or user feedback. It can be used in:

• E-commerce platforms to monitor customer opinions.
• Social media analysis for brand reputation management.
• Market research to gauge consumer sentiment.

Direct Use

The model can classify text directly without additional fine-tuning for similar binary sentiment tasks.

Downstream Use

It can be further fine-tuned for domain-specific sentiment analysis (e.g., medical reviews, movie critiques).

How to Use

Python Code Example

Below is an example of how to load and use the model with the transformers library:

from transformers import AutoModelForSequenceClassification, AutoTokenizer

# 加载模型和tokenizer
model_name = "huevan/distilbert-base-uncased-rx"  # 替换为你的仓库名
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

# 输入文本
text = "I love this product, it's amazing!"
inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=128)

# 预测
model.eval()
with torch.no_grad():
    outputs = model(**inputs)
    prediction = outputs.logits.argmax(-1).item()
    sentiment = "positive" if prediction == 1 else "negative"
print(f"Sentiment: {sentiment}")  # 输出: positive