# Medical Text Analysis

OpenBioNER is a lightweight BERT model specifically designed for open-domain biomedical named entity recognition (NER). It can identify unseen entity types using only natural language descriptions of target entity types, without requiring retraining.

GLiNER is a Named Entity Recognition (NER) model capable of identifying any type of entity through a bidirectional Transformer encoder, particularly suitable for Personal Identifiable Information (PII) recognition.

A named entity recognition model fine-tuned on BioBERT for detecting medical symptoms from clinical records.

A pre-trained BERT model for Korean medical NLP, based on the KR-BERT architecture, pre-trained on a 116-million-word Korean medical corpus

This model is a fine-tuned Spanish negation scope detection model based on ajtamayoh/NER_EHR_Spanish_model_Mulitlingual_BERT

A Portuguese question-answering model trained on BERTimbau Large and fine-tuned on the Portuguese version of SQuAD v1.1, suitable for Portuguese QA tasks.

A domain-specific language model fine-tuned on 13K chemistry and 14K pharmaceutical Wikipedia article paragraphs for distinguishing between pharmaceutical and chemical domain texts.

This is a general model description, a brief introduction not exceeding 150 words.