host model is a Named Entity Recognition (NER) model that identifies and annotates the host (living organism) 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. Turkestan cockroach nymphs (Finke, 2013) were fed to the treefrogs at a quantity of 10% of treefrog biomass twice a week.
2. Samples were collected from clinically healthy giant pandas (five females and four males) at the China Conservation and Research Center for Giant Pandas (Ya'an, China). 
3. Field-collected bee samples were dissected on dry ice and separated into head, thorax (excluding legs and wings), and abdomens.
   
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transformers pytorch bert token-classification autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us 

host model is a Named Entity Recognition (NER) model that identifies and annotates the host (living organism) of microbiome samples in texts.

The model is a fine-tuned BioBERT model and the training dataset is available in URL 

Testing examples:

1. Turkestan cockroach nymphs (Finke, 2013) were fed to the treefrogs at a quantity of 10% of treefrog biomass twice a week.
2. Samples were collected from clinically healthy giant pandas (five females and four males) at the China Conservation and Research Center for Giant Pandas (Ya'an, China). 
3. Field-collected bee samples were dissected on dry ice and separated into head, thorax (excluding legs and wings), and abdomens.
   
TAGS
#transformers #pytorch #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us 

passage: TAGS
#transformers #pytorch #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us 
