xlm-roberta-large-finetuned-conll03-english

Maintainer: FacebookAI

Total Score

101

Last updated 5/28/2024

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PropertyValue
Model LinkView on HuggingFace
API SpecView on HuggingFace
Github LinkNo Github link provided
Paper LinkNo paper link provided

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Model overview

The xlm-roberta-large-finetuned-conll03-english model is a large multi-lingual language model developed by FacebookAI. It is based on the XLM-RoBERTa architecture, which is a multi-lingual version of the RoBERTa model. The model was pre-trained on 2.5TB of filtered CommonCrawl data containing 100 languages, and then fine-tuned on the English ConLL2003 dataset for the task of token classification.

Similar models include the XLM-RoBERTa (large-sized) model, the XLM-RoBERTa (base-sized) model, the roberta-large-mnli model, and the xlm-roberta-large-xnli model. These models share architectural similarities as part of the RoBERTa and XLM-RoBERTa family, but are fine-tuned on different tasks and datasets.

Model inputs and outputs

Inputs

  • Text: The model takes in text as input, which can be in any of the 100 languages the model was pre-trained on.

Outputs

  • Token labels: The model outputs a label for each token in the input text, indicating the type of entity or concept that token represents (e.g. person, location, organization).

Capabilities

The xlm-roberta-large-finetuned-conll03-english model is capable of performing token classification tasks on English text, such as named entity recognition (NER) and part-of-speech (POS) tagging. It has been fine-tuned specifically on the CoNLL2003 dataset, which contains annotations for named entities like people, organizations, locations, and miscellaneous entities.

What can I use it for?

The xlm-roberta-large-finetuned-conll03-english model can be used for a variety of NLP tasks that involve identifying and classifying entities in English text. Some potential use cases include:

  • Information Extraction: Extracting structured information, such as company names, people, and locations, from unstructured text.
  • Content Moderation: Identifying potentially offensive or sensitive content in user-generated text.
  • Data Enrichment: Augmenting existing datasets with entity-level annotations to enable more advanced analysis and machine learning.

Things to try

One interesting aspect of the xlm-roberta-large-finetuned-conll03-english model is its multilingual pre-training. While the fine-tuning was done on an English-specific dataset, the underlying XLM-RoBERTa architecture suggests the model may have some cross-lingual transfer capabilities.

You could try using the model to perform token classification on text in other languages, even though it was not fine-tuned on those specific languages. The performance may not be as strong as a model fine-tuned on the target language, but it could still provide useful results, especially for languages that are linguistically similar to English.

Additionally, you could experiment with using the model's features (the contextualized token embeddings) as input to other downstream machine learning models, such as for text classification or sequence labeling tasks. The rich contextual information captured by the XLM-RoBERTa model may help boost the performance of these downstream models.



This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!

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