Named Entity Recognition (NER)
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Named-entity recognition is a subtask of information extraction that seeks to locate and classify named entity mentioned in unstructured text into pre-defined categories such as person names, organizations, locations, medical codes, etc.
PyTorch Transformers Tutorials
A set of annotated Jupyter notebooks, that give user a template to fine-tune transformers model to downstream NLP tasks such as classification, NER etc.
Named Entity Recognition Tagging
In this post, we go through an example from Natural Language Processing, in which we learn how to load text data and perform NER tagging for each token.
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NER model for 40 languages trained with the new TFTrainer
This model is a fine-tuned XLM-Roberta-base over the 40 languages proposed in XTREME from Wikiann.
BioBERT: a pre-trained biomedical language representation model
Code for fine-tuning BioBERT for biomedical text mining tasks such as biomedical NER, relation extraction, QA, etc.
PYthon Automated Term Extraction
Term extraction algorithms such as C-Value, Basic, Combo Basic, Weirdness and Term Extractor using spaCy POS tagging.
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