t5-finetuned-final

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.

• To use the model, you can load it using the transformers library:

  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:

  {
      "action": "send",
      "amount": 5102.47,
      "currency": "GBP",
      "recipient": "my brother"
  }