pytorch bert token-classification transformers autotrain_compatible
body-site model is a Named Entity Recognition (NER) model that identifies and annotates the body-site of microbiome samples in texts. 

The model is a fine-tuned BioBERT model and the training dataset is available in https://gitlab.com/maaly7/emerald_metagenomics_annotations 

Testing examples:

1. Scalp hair was collected from behind the right ear, near the right retroauricular crease, and pubic hair was collected from their right pubis, near the right inguinal crease.
2. Field-collected bee samples were dissected on dry ice and separated into head, thorax (excluding legs and wings), and abdomens.
3. TSO modulate the IEC and LPMC transcriptome To gain further insights into the mechanisms of TSO treatment, we performed genome wide expression analysis on intestinal epithelial cells (IEC) and lamina propria mononuclear cells (LPMC) isolated from caecum samples by RNA sequencing (RNAseq).
4. Two catheters were bilaterally placed in the CA1 region of the hippocampus with the coordinates of 4.5 mm anterior to bregma, 1.6 mm ventral to the dura, and two directions of ± 4.0 mm from the interaural line (Park et al. 2013; Yang et al. 2013). 