metadata

language:
  - en
tags:
  - ner
  - chemical
  - bionlp
  - bc4cdr
  - bioinfomatics
license: apache-2.0
datasets:
  - bionlp
  - bc4cdr
widget:
  - text: >-
      Serotonin receptor 2A (HTR2A) gene polymorphism predicts treatment
      response to venlafaxine XR in generalized anxiety disorder.

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