| ## 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" | |
| } | |
| ``` |