## Model Overview
- The model used is `t5-small`, a lightweight transformer model from the T5 (Text-To-Text Transfer Transformer) family.
- It has been fine-tuned specifically for intent recognition on the labeled dataset provided in the `data` directory.
- The fine-tuning process allows the model to extract structured information such as **action, amount, currency, and recipient** from user commands.

## Model Access
- The trained model is hosted on Hugging Face and can be accessed here: [RayBe/t5-finetuned-final](https://huggingface.co/RayBe/t5-finetuned-final).
- To use the model, you can load it using the `transformers` library:

  ```python
  from transformers import T5Tokenizer, T5ForConditionalGeneration
  
  model_name = "RayBe/t5-finetuned-final"
  tokenizer = T5Tokenizer.from_pretrained(model_name)
  model = T5ForConditionalGeneration.from_pretrained(model_name)
  ```


## Usage
- The model is intended for real-time extraction of transaction details from natural language inputs.
- Example input:
  ```
  "send 5102.47 GBP to my brother."
  ```
  Expected output:
  ```json
  {
      "action": "send",
      "amount": 5102.47,
      "currency": "GBP",
      "recipient": "my brother"
  }
  ```