grc alignment

Introduction

The GRC-ALIGNMENT model is an XLM-RoBERTa-based model developed for automatic multilingual text alignment, specifically at the word level. It is tailored for ancient Greek texts and is fine-tuned using a substantial corpus of both monolingual and parallel multilingual datasets.

Architecture

This model leverages the XLM-RoBERTa architecture, which is designed to handle multilingual text with a focus on robust language understanding. The model benefits from the Masked Language Model (MLM) training objective and is fine-tuned on datasets involving multiple languages such as ancient Greek, English, Latin, and Georgian.

Training

The GRC-ALIGNMENT model was trained on a dataset consisting of 12 million monolingual ancient Greek tokens. It was further fine-tuned on 45,000 parallel sentences sourced from multiple languages, predominantly ancient Greek-English, Greek-Latin, and Greek-Georgian. The training utilized data from sources like the Perseus Digital Library and the Digital Fragmenta Historicorum Graecorum project.

Guide: Running Locally

To run this model locally, follow these steps:

1. Set Up Environment: Ensure you have Python installed, along with necessary libraries like PyTorch and Hugging Face Transformers.
2. Download Model: Fetch the model from Hugging Face's model hub.
3. Load Model: Use the Transformers library to load the model and tokenizer.
4. Run Inference: Input your texts for alignment and retrieve the results.

Suggested Cloud GPUs

For optimal performance, consider using cloud-based GPUs from providers like AWS, Google Cloud, or Azure. These platforms offer powerful computing resources to handle large-scale NLP tasks efficiently.

License

The GRC-ALIGNMENT model is released under the Creative Commons Attribution 4.0 International License (cc-by-4.0). This allows for sharing, adaptation, and redistribution, provided appropriate credit is given.