sat 12l sm

Introduction

SAT-12L-SM is a state-of-the-art model designed for sentence segmentation. It uses 12 Transformer layers to achieve high accuracy across 85 languages. The model is part of the Segment Any Text project and is available through the Hugging Face platform.

Architecture

The model is based on the Transformer architecture, specifically utilizing 12 layers to perform token classification. It supports a wide range of languages, including those using Latin, Cyrillic, and other scripts, making it versatile for multilingual tasks.

Training

The training process involves fine-tuning the Transformer layers to optimize for sentence segmentation tasks. The model has been trained on diverse datasets to ensure it performs well across different languages and text types.

Guide: Running Locally

1. Clone the Repository: Clone the SAT-12L-SM repository from Hugging Face.

2. Set Up Environment: Ensure you have Python and the necessary libraries installed, including transformers and torch.

3. Download the Model: Use the Hugging Face Transformers library to download the model:

   from transformers import AutoTokenizer, AutoModelForTokenClassification
   
   tokenizer = AutoTokenizer.from_pretrained("segment-any-text/sat-12l-sm")
   model = AutoModelForTokenClassification.from_pretrained("segment-any-text/sat-12l-sm")
   

4. Run Inference: Use the model to segment sentences in your text data.

5. Cloud GPUs: For large-scale tasks, consider using cloud services like AWS, Google Cloud, or Azure to access GPU resources.

License

The SAT-12L-SM model is distributed under the MIT License, allowing for flexible use and modification in both personal and commercial projects.