from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
Load the model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("charlie1898/gpt2_finetuned_twitter_sentiment_analysis")
model = AutoModelForSequenceClassification.from_pretrained("charlie1898/gpt2_finetuned_twitter_sentiment_analysis")
Example input
text = "I love using Hugging Face models!"
inputs = tokenizer(text, return_tensors="pt")
outputs = model(**inputs)
predicted_class = torch.argmax(outputs.logits).item()
print(f"Predicted sentiment class: {predicted_class}")
Limitations
** Bias **: The dataset may contain biased or harmful text, potentially influencing predictions.
** Domain Limitations **: Optimized for English tweets; performance may degrade on other text types or languages.
Ethical Considerations
This model should be used responsibly. Be aware of biases in the training data and avoid deploying the model in sensitive or high-stakes applications without further validation.
