This GitHub project is aimed at fine-tuning the flan t5 model for a text classification task using an E-commerce text dataset for 4 categories - "Electronics", "Household", "Books" and "Clothing & Accessories".
Text Classification model can be found on HuggingFace. The model is trained on the dataset and evaluated on the test set. The evaluation metrics are as follows:
The main advantage of this model is that together with prediction it outputs the confidence score for each class. This can be used to filter out the predictions with low confidence.
Text2Text Generation model can be found on HuggingFace. The model is trained on the dataset and evaluated on the test set. The evaluation metrics are as follows:
The dataset is a classification-based E-commerce text dataset, which almost covers 80% of any E-commerce website. The dataset consists of product and description data for 4 categories. The dataset can be found here.
The project employs the tokenizer of flan-t5 by Hugging Face, which helps in splitting the input text into a format that is understandable by the model.
An evaluation function has been implemented for post-processing the labels and predictions, which will also handle sequence length adjustments.
The project uses Seq2SeqTrainer and SequenceClassification for training the model. It also includes a helper function to preprocess the dataset.
To leverage the project you need to run the flan-t5-finetuning.py script which will trigger the training of the model.
The 'train' function fine-tunes the flan-t5 model, trains it with the dataset, outputs the metrics, creates a model card and pushes the model to Hugging Face model hub.
The preprocess function tokenizes the inputs, and also handles tokenization of the target labels. The compute_metrics function evaluates the model performance based on the F1 metric.
STEP 1: create and activate python virtual environment
python -m venv venv
source venv/bin/activateSTEP 2: install requirements with poetry
poetry install -vvWe use SemVer for versioning. For the versions available, see the tags on this repository.
This repository is licensed under MIT (c) 2023 GitHub, Inc.
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