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Category/token-classification
Token classification is a fundamental task in natural language processing (NLP) that involves categorizing each token (i.e., word or subword) in a text sequence. This process, also known as sequence tagging or sequence labeling, is essential for a wide range of NLP applications.
Once a model is trained, it can be used to predict the labels for new, unseen sequences of tokens. These predictions can be used for a variety of NLP applications, such as information extraction, machine translation, and text classification.
In summary, token classification AI models are a critical component of NLP, enabling computers to better understand natural language text and extract valuable insights.
token-classification
A named entity recognition model for 10 high-resource languages, trained on a fine-tuned mBERT base model. The model recognizes three types of entities: location, organization, and person. The training data consists of entity-annotated news articles from various datasets for each language, and the model distinguishes between the beginning and continuation of an entity.
token-classification
A Named Entity Recognition model fine-tuned from CamemBERT on the Wikiner-FR dataset. Our model achieves high performance on various entities, including Persons, Organizations, Locations, and Miscellaneous entities.
token-classification
We present a fine-tuned RoBERTa model for English named entity recognition, achieving high performance on both formal and informal datasets. Our approach uses a simplified version of the CONLL2003 dataset and removes unnecessary prefixes for improved efficiency. The resulting model shows superiority over other models, especially on entities that do not begin with uppercase letters, and can be used for various applications such as email signature detection.
token-classification
The bert-base-NER model is a fine-tuned BERT model that achieves state-of-the-art performance on the CoNLL-2003 Named Entity Recognition task. It was trained on the English version of the standard CoNLL-2003 dataset and recognizes four types of entities: location, organization, person, and miscellaneous. The model occasionally tags subword tokens as entities and post-processing of results may be necessary to handle these cases.
token-classification
A fine-tuned BERT model that achieves state-of-the-art performance on the CoNLL-2003 Named Entity Recognition task. The model was trained on the English version of the standard CoNLL-2003 dataset and distinguishes between four types of entities: location, organization, person, and miscellaneous.
token-classification
The German BERT model was fine-tuned on the Legal-Entity-Recognition dataset for the named entity recognition (NER) task, achieving an F1 score of 85.67% on the evaluation set. The model uses a pre-trained BERT base model and is trained with a provided script from Hugging Face. The labels covered include various types of legal entities, such as companies, organizations, and individuals.
token-classification
This paper presents a fine-tuned Spanish BERT model (BETO) for the Named Entity Recognition (NER) task. The model was trained on the CONLL Corpora ES dataset and achieved an F1 score of 90.17%. The authors also compared their model with other state-of-the-art models, including a multilingual BERT and a TinyBERT model, and demonstrated its effectiveness in identifying entities in Spanish text.
token-classification
A model for removing references to priors from radiology reports based on a fine-tuned BioBERT model.
