Xlm Roberta Large Finetuned Conll03 English
What makes XLM-RoBERTa-large so remarkable? It's a large multi-lingual language model, trained on 2.5TB of filtered CommonCrawl data, and fine-tuned with the conll2003 dataset in English. This model is capable of token classification, a natural language understanding task, and can be used for tasks like Named Entity Recognition (NER) and Part-of-Speech (PoS) tagging. With its multilingual capabilities, it can handle 100 different languages, making it a versatile tool for various applications. However, it's essential to be aware of the potential biases and limitations, such as propagating historical and current stereotypes, and to use it responsibly.
Table of Contents
Model Overview
The XLM-RoBERTa-large-finetuned-conll03-english model is a powerful language model developed by Facebook. It’s a multi-lingual model, trained on 2.5TB of filtered CommonCrawl data in 100 different languages, and fine-tuned on the CoNLL-2003 dataset in English.
Capabilities
The model can be used for token classification, which is a natural language understanding task that assigns labels to specific words or tokens in a text. It’s also useful for downstream tasks like Named Entity Recognition (NER) and Part-of-Speech (PoS) tagging.
What can it do?
The model can identify named entities in a sentence, like people, places, or organizations. For instance, if you input the sentence “Alya told Jasmine that Andrew could pay with cash..”, the model will identify the names “Alya”, “Jasmine”, and “Andrew” as people.
Strengths
- High accuracy: The model has been fine-tuned on the CoNLL-2003 dataset in English, which has improved its performance on token classification tasks.
- Large training dataset: The model was trained on
2.5TBof filtered CommonCrawl data, which provides it with a vast amount of knowledge about language.
Unique Features
- Multi-lingual capabilities: The model’s ability to understand and process multiple languages makes it a unique and valuable tool for applications that require language understanding across different languages.
- Fine-tuned for English: The model’s fine-tuning on the CoNLL-2003 dataset in English makes it particularly well-suited for applications that require high accuracy on English language tasks.
Example Use Cases
- Named Entity Recognition (NER): The model can be used to identify and classify named entities in text, such as people, places, and organizations.
- Part-of-Speech (PoS) Tagging: The model can be used to identify the part of speech (such as noun, verb, adjective, etc.) of each word in a sentence.
Performance
The model’s performance is impressive, with a score of 0.99995816 on the CoNLL-2003 dataset. This is a significant achievement, indicating that the model can accurately identify named entities in text.
Speed
The model’s speed is also notable, making it suitable for large-scale natural language processing tasks.
Efficiency
The model’s efficiency is also impressive, requiring 500 32GB Nvidia V100 GPUs to train.
Comparison to Other Models
Compared to other models, such as RoBERTa and XLM, the XLM-RoBERTa-large-finetuned-conll03-english model has been fine-tuned for English token classification tasks, making it a more specialized and accurate model for this specific task.
Limitations
The model has some significant limitations that you should be aware of.
Biases and Risks
The model may generate language that is disturbing or offensive to some people. It can also propagate historical and current stereotypes.
Technical Limitations
The model is trained on a large dataset, but it’s not perfect. It can make mistakes, especially when dealing with complex or nuanced language.
Environmental Impact
Training large language models like this one requires significant computational resources and energy. This can have a substantial environmental impact.
Format
The model uses a multi-lingual transformer architecture and accepts input in the form of tokenized text sequences.
Architecture
The model is based on Facebook’s RoBERTa model, which is a type of transformer architecture.
Data Formats
The model supports input in the form of text sequences, which need to be pre-processed into tokens.
Input Requirements
To use the model, you’ll need to provide input in the form of text sequences. For example, you might want to classify the entities in a sentence like “Hello I’m Omar and I live in Zürich.”
Output Format
The model outputs a list of entities, each with a label and a confidence score.
Getting Started
To use the model, you can follow the code example provided in the model card. You’ll need to install the transformers library and use the AutoTokenizer and AutoModelForTokenClassification classes to load the model and tokenizer. Then, you can use the pipeline function to create a named entity recognition pipeline.
from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import 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)
classifier("Hello I'm Omar and I live in Zürich.")
