🧠 Roberta‑Base‑Finetuning

Fine-tune Hugging Face’s distill-roberta-base model for custom text classification tasks using the Transformers & Datasets libraries.

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Features

• Seamless adapter to any text classification dataset.
• Utilizes Trainer API for elegant training/evaluation.
• Configurable hyperparameters: epochs, batch‑size, learning rate, weight decay.
• Customized id↔label mapping.

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Getting Started

Prerequisites

• Python 3.8+
• GPU recommended (CUDA-capable or AMD w/ ROCm)
• Dependencies:

  pip install torch transformers datasets huggingface_hub tensorboard fastapi
  sudo apt-get install git-lfs
  

Authenticate with Hugging Face (optional)

from huggingface_hub import notebook_login
notebook_login()

Dataset

Uses any Hugging Face dataset with train/test splits. By default:

dataset = load_dataset("your_dataset_id")

Example: "ag_news" dataset is used in tutorials.

Use Cases

• Topic Classification – e.g., AG News dataset
• Sentiment Analysis
• Domain‑specific Text Categorization
  Adaptable via dataset + updated id2label mapping.

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Accessing via API

POST /train_model

Train the model on a CSV dataset.

Request

Method: POST
Content-Type: multipart/form-data

Form Data

Key	Type	Description
file	file	CSV file containing training data
model_path	string	Path to save the trained model

Example

curl -X POST http://127.0.0.1:8000/train_model \
  -H "Authorization: Bearer <your_token>" \
  -F "file=@/path/to/new.csv" \
  -F "model_path=./model_path_here"

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GET /predict

Predict the role based on title and description.

Request

Method: GET
Content-Type: application/json

Body

{
  "title": "Title of the task goes in here",
  "description": "description of the task goes in here"
}

Example

curl -X GET http://127.0.0.1:8000/predict \
  -H "Authorization: Bearer <your_token>" \
  -H "Content-Type: application/json" \
  -d '{
        "title": "Setup a frontend interface",
        "description": "Create the dashboard UI for task monitoring"
      }'

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🛠 Notes

• Ensure the server is running at http://127.0.0.1:8000
• The model must be trained before using the /predict endpoint.
• The CSV file for training should contain labeled examples with fields compatible with your model format.

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Performance Tips

• For efficiency on AMD GPUs, consider mixed precision with torch.cuda.amp.
• Can easily incorporate LoRA via PEFT to reduce trainable params dramatically.
• Use RobertaTokenizerFast or AutoTokenizer for better speed.

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🛠️ Customization

• Adjust text length via max_length.
• Modify hyperparameters (batch size / LR / epochs).
• Swap in different datasets by changing load_dataset("...").
• Extend to multi-label classification and sequence tasks by adjusting model type and labels.

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