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Model Card: bert-base-cased-biological-ner

Model Details

  • Model Name: bert-base-cased-biomedical-ner
  • Model Architecture: BERT (Bidirectional Encoder Representations from Transformers)
  • Pre-trained Model: bert-base-cased
  • Fine-tuned on: SourceData Dataset

Model Description

The bert-base-cased-biomedical-ner is a fine-tuned variant of the BERT (Bidirectional Encoder Representations from Transformers) model, designed specifically for the task of Named Entity Recognition (NER) in the biomedical domain. The model has been fine-tuned on the SourceData Dataset, which is a substantial and comprehensive biomedical corpus for machine learning and AI in the publishing context.

Named Entity Recognition is a crucial task in natural language processing, particularly in the biomedical field, where identifying and classifying entities like genes, proteins, diseases, and more is essential for various applications, including information retrieval, knowledge extraction, and data mining.

Intended Use

The bert-base-cased-biological-ner model is intended for NER tasks within the biomedical domain. It can be used for a range of applications, including but not limited to:

  • Identifying and extracting biomedical entities (e.g., genes, proteins, diseases) from unstructured text.
  • Enhancing information retrieval systems for scientific literature.
  • Supporting knowledge extraction and data mining from biomedical literature.
  • Facilitating the creation of structured biomedical databases.


Label Description
SMALL_MOLECULE Small molecules
GENEPROD Gene products (genes and proteins)
SUBCELLULAR Subcellular components
CELL_LINE Cell lines
CELL_TYPE Cell types
TISSUE Tissues and organs
DISEASE Diseases
EXP_ASSAY Experimental assays
Source of label information: EMBO/SourceData Dataset


from transformers import pipeline, AutoModelForTokenClassification, AutoTokenizer
import pandas as pd

tokenizer = AutoTokenizer.from_pretrained("Kushtrim/bert-base-cased-biomedical-ner")
model = AutoModelForTokenClassification.from_pretrained("Kushtrim/bert-base-cased-biomedical-ner")

ner = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy='first')

text = "Add your text here"

results = ner(text)


Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 2e-05
  • train_batch_size: 16
  • eval_batch_size: 16
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 3

Framework versions

  • Transformers 4.35.0
  • Pytorch 2.1.0+cu118
  • Datasets 2.14.6
  • Tokenizers 0.14.1
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Model size
108M params
Tensor type

Finetuned from

Dataset used to train Kushtrim/bert-base-cased-biomedical-ner

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