| --- |
| library_name: transformers |
| tags: |
| - multilabel |
| - multilabel-token-classification |
| base_model: |
| - google-bert/bert-large-cased |
| --- |
| # 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) |
| ``` |
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