A collection of example implementations showcasing various applications of HuggingFace's Transformers library for different NLP tasks.
This repository contains four different implementations that demonstrate the flexibility and power of transformer models for various natural language processing and tabular data tasks:
- Text Classification: Custom implementation for fine-tuning transformer models on text classification tasks.
- Seq2Seq (Summarization): T5-based implementation for text summarization tasks.
- T5 Encoder for Classification: Using only the encoder component of T5 for efficient sequence classification.
- Tabular Classification: Adapting transformer architectures for structured tabular data.
Each implementation includes detailed documentation, code examples, and configuration options to help you understand and adapt these models to your own use cases.
huggingface-transformers-examples/
├── text_classification/ # Text classification implementation
├── seq2seq/ # Sequence-to-sequence (summarization) implementation
├── t5encoder_classification/ # T5 encoder-based classification
├── tabular_classification/ # Transformer models for tabular data
├── LICENSE # License information
└── README.md # This file
A custom implementation for text classification using pre-trained transformer models like BART.
Key Features:
- Custom model architecture built on pre-trained transformers
- Configurable classification setup with support for any number of labels
- Easy integration with HuggingFace's Trainer API
Learn more about Text Classification
A T5-based implementation for text summarization, fine-tuned on the CNN DailyMail dataset.
Key Features:
- T5 model fine-tuning for abstractive summarization
- ROUGE metrics for evaluation
- Support for both training and inference
Learn more about Seq2Seq Summarization
A custom implementation that uses only the encoder component of the T5 model for sequence classification tasks.
Key Features:
- Lightweight classification using T5 encoder
- Custom classification head
- Comprehensive training pipeline with metrics tracking
Learn more about T5 Encoder Classification
An implementation that adapts transformer architectures (BERT, RoBERTa, DeBERTa) for structured tabular data classification.
Key Features:
- Positional encoding for tabular data
- Support for multiple transformer backbones
- Integration with Weights & Biases for experiment tracking
Learn more about Tabular Classification
Each implementation has its own requirements, but they all rely on HuggingFace Transformers and PyTorch. Refer to the individual README files in each directory for specific installation instructions.
Each implementation includes detailed usage instructions in its respective README file. The implementations follow a similar pattern of loading pre-trained models, configuring them for specific tasks, and fine-tuning them on custom datasets.
This project is licensed under the terms included in the LICENSE file.
Contributions are welcome! Please feel free to submit a Pull Request.