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jvaquet/
multilabel-classification-bert

Name: multilabel-classification-bert
Availability: InStock

multilabel-token-classification

custom_code

google-bert/bert-large-cased

finetune

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Overview

This is an extension of the bert-large-cased model to enable multi-label token classification.
The training objective is BCELoss.
Labels are one-hot encoded.
Model output logits can be normalized using sigmoid activation.
This model uses the same weights as bert-large-cased and thus needs to be fine-tuned for downstream tasks.

Usage

To initialize the model for fine tuning, simply provide id2label and label2id, similarly to standard token classification fine tuning:

python

from transformers import AutoModelForTokenClassification

model = AutoModelForTokenClassification.from_pretrained('jvaquet/multilabel-classification-bert', 
  id2label = id2label, 
  label2id = label2id,
  trust_remote_code=True)