roberta large N E R

Introduction

The XLM-RoBERTa model is a multilingual language model developed by Facebook, fine-tuned for Named Entity Recognition (NER) using the CoNLL-2003 dataset in English. It is designed for token classification tasks within natural language processing (NLP).

Architecture

XLM-RoBERTa is based on the RoBERTa model and is trained on 2.5TB of CommonCrawl data, supporting 94 languages. It leverages the architecture of RoBERTa, focusing on unsupervised cross-lingual representation learning. The model's multilingual capabilities allow it to perform well across various languages.

Training

The model was trained on the CoNLL-2003 dataset, which contains labeled data suitable for NER tasks. For comprehensive details on the training process, refer to the XLM-RoBERTa-large model card and the CoNLL-2003 data card.

Guide: Running Locally

1. Installation: Ensure Python and the transformers library are installed. You can install transformers via pip:

   pip install transformers
   

2. Setup: Use the following Python code to load the model and perform NER:

   from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline
   
   tokenizer = AutoTokenizer.from_pretrained("xlm-roberta-large-finetuned-conll03-english")
   model = AutoModelForTokenClassification.from_pretrained("xlm-roberta-large-finetuned-conll03-english")
   classifier = pipeline("ner", model=model, tokenizer=tokenizer)
   
   result = classifier("Hello I'm Omar and I live in Zürich.")
   print(result)
   

3. Hardware: For optimal performance, especially with large datasets, consider using cloud GPUs such as those offered by AWS, Google Cloud, or Azure.

License

Further information about the model's license is required for clarification. Please refer to the associated resources or contact the developers for specific licensing details.