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distilbert-base-uncased-ft-ncbi-disease

Token Classification Transformers PyTorch Safetensors
English distilbert named-entity-recognition multi_class_classification Inference Endpoints
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metadata
language: en
tags:
  - token-classification
  - named-entity-recognition
  - multi_class_classification
task:
  - token-classification
  - named-entity-recognition
  - multi_class_classification
license: cc
datasets:
  - ncbi_disease
metrics:
  - precision
  - recall
  - f1
  - accuracy
widget:
  - text: ' The risk of cancer, especially lymphoid neoplasias, is substantially elevated in A-T patients and has long been associated with chromosomal instability.'

Model information:

distilibert-base-uncased model finetuned using the ncbi_disease dataset from the datasets library.

Intended uses:

This model is intended to be used for named entity recoginition tasks. The model will identify disease entities in text. The model will predict lables based upon the NCBI-disease dataset, please see the dataset information for details.

Limitations:

Note that the dataset and model may not be fully represetative or suitable for all needs it is recommended that the paper for the dataset and the base model card should be reviewed before using the model -

How to use:

Load the model from the library using the following checkpoints:

from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("sarahmiller137/distilbert-base-uncased-ft-ncbi-disease")
model = AutoModel.from_pretrained("sarahmiller137/distilbert-base-uncased-ft-ncbi-disease")