from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("AwaisMughal1995/web3chatbot_fine_tuned_bart")
model = AutoModelForSequenceClassification.from_pretrained("AwaisMughal1995/web3chatbot_fine_tuned_bart")

# Example usage
text = "Your input text here"
inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)

with torch.no_grad():
    outputs = model(**inputs)
    predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
    predicted_class = torch.argmax(predictions, dim=-1)

print(f"Predicted class: {predicted_class.item()}")

Training Details

Training Data

Dataset: [Describe your training dataset]
Size: [Number of examples]

Training Procedure

Fine-tuning approach: [Describe your approach]
Training epochs: [Number of epochs]
Learning rate: [Learning rate used]
Batch size: [Batch size used]

Evaluation Results

Accuracy: [Your accuracy score]
F1 Score: [Your F1 score]

Intended Use

This model is intended for intent classification tasks. It can be used to classify user intents in chatbots, virtual assistants, or other NLP applications.

Limitations and Bias

[Describe any known limitations or biases of your model]

Citation

If you use this model, please cite:

@misc{web3chatbot_fine_tuned_bart,
  author = {AwaisMughal1995},
  title = {web3chatbot_fine_tuned_bart},
  year = {2024},
  publisher = {Hugging Face},
  url = {https://huggingface.co/AwaisMughal1995/web3chatbot_fine_tuned_bart}
}