# NER to find Gene & Gene products

The model was trained on bionlp and bc4cdr dataset, pretrained on this pubmed-pretrained roberta model All the labels, the possible token classes.

{"label2id":
{
"O": 0,
"Chemical": 1,
}
}


Notice, we removed the 'B-','I-' etc from data label.🗡

## This is the template we suggest for using the model

Of course I'm well aware of the aggregation_strategy arguments offered by hf, but by the way of training, I discard any entropy loss for appending subwords, like only the label for the 1st subword token is not -100, after many search effort, I can't find a way to achieve that with default pipeline, hence I fancy an inference class myself.

!pip install forgebox
from forgebox.hf.train import NERInference
ner = NERInference.from_pretrained("raynardj/ner-chemical-bionlp-bc5cdr-pubmed")
a_df = ner.predict(["text1", "text2"])


check our NER model on