bert-large-raw-climate-sentiment-wqf7007

BERT Climate Sentiment Analysis Model

Model Description

This model fine-tunes BERT (bert-large-uncased) to perform sentiment analysis on climate change-related tweets. It classifies tweets into four sentiment categories: anti-climate (negative), neutral, pro-climate (positive), and news.

Model Details

• Model Type: Fine-tuned BERT (bert-large-uncased)
• Version: 1.0.0
• Framework: PyTorch & Transformers
• Language: English
• License: AGPL-3.0

Training Data

This model was trained on the Twitter Climate Change Sentiment Dataset, which contains tweets related to climate change labeled with sentiment categories:

• news: Factual news about climate change (2)
• pro: Supporting action on climate change (1)
• neutral: Neutral stance on climate change (0)
• anti: Skeptical about climate change claims (-1)

The dataset was used with raw text without special preprocessing to evaluate performance on natural language tweets.

Training Procedure

• Training Framework: PyTorch with Transformers
• Training Approach: Fine-tuning the entire BERT model
• Optimizer: AdamW with learning rate 2e-5
• Batch Size: 64
• Early Stopping: Yes, with patience of 3 epochs
• Hardware: GPU acceleration (when available)

Model Performance

• AUC-ROC

• Training and Validation Loss

Limitations and Biases

• The model is trained on Twitter data, which may not generalize well to other text sources.
• Twitter data may contain inherent biases in how climate change is discussed.
• The model might struggle with complex or nuanced sentiment expressions.
• Sarcasm and figurative language may be misclassified.
• The model is only trained for English language content.

Usage

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("google/bert-large-uncased")
model = AutoModelForSequenceClassification.from_pretrained("keanteng/bert-large-raw-climate-sentiment-wqf7007")

# Prepare text
text = "Climate change is real and we need to act now!"
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=128)

# Make prediction
with torch.no_grad():
    outputs = model(**inputs)
    predictions = torch.argmax(outputs.logits, dim=1)

# Map prediction to sentiment
sentiment_map = {-1: "anti", 0: "neutral", 1: "pro", 2: "news"}
predicted_sentiment = sentiment_map[predictions.item()]
print("Predicted sentiment: " + predicted_sentiment)

Ethical Considerations

This model should be used responsibly for analyzing climate sentiment and should not be deployed in ways that might:

• Amplify misinformation about climate change
• Target or discriminate against specific groups
• Make critical decisions without human oversight