infoxlm large

Introduction

InfoXLM is a cross-lingual language model developed by Microsoft, designed to improve multilingual understanding through pre-training. Presented at NAACL 2021, InfoXLM utilizes an information-theoretic framework aiming at enhancing the performance of language models in cross-lingual tasks.

Architecture

InfoXLM is based on the xlm-roberta architecture, leveraging the capabilities of transformers and PyTorch. This model is compatible with Hugging Face's inference endpoints and can be used for fill-mask tasks, making it versatile for various NLP applications.

Training

The model is pre-trained using an information-theoretic approach, which focuses on maximizing the mutual information between languages. This framework helps in capturing cross-lingual knowledge and improves the model's adaptability to multiple languages. The training process is outlined in the paper "InfoXLM: An Information-Theoretic Framework for Cross-Lingual Language Model Pre-Training," available on arXiv.

Guide: Running Locally

To run InfoXLM locally, follow these steps:

1. Install the Hugging Face Transformers library:

   pip install transformers
   

2. Load the model using the following Python code:

   from transformers import AutoModelForMaskedLM, AutoTokenizer
   
   tokenizer = AutoTokenizer.from_pretrained("microsoft/infoxlm-large")
   model = AutoModelForMaskedLM.from_pretrained("microsoft/infoxlm-large")
   

3. To use a cloud GPU for faster processing, consider platforms like AWS, Google Cloud, or Azure, which provide GPU instances suitable for deploying large models like InfoXLM.

License

Specific licensing details for InfoXLM are not provided in the README.md. Users should consult Microsoft's repository or the model card on Hugging Face for licensing information. Additionally, refer to any accompanying files like LICENSE in the repository for more details.