ncbi_disease_ner

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ncbi_disease_ner

This model is a fine-tuned version of bert-base-cased on an ncbi_disease dataset. It achieves the following results on the evaluation set:

• Loss: 0.0921
• Precision: 0.8082
• Recall: 0.8564
• F1: 0.8316
• Accuracy: 0.9835

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e-05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 5

Training results

Run the model

from transformers import pipeline

model_checkpoint = "manibt1993/ncbi_disease_ner"
token_classifier = pipeline(
    "token-classification", model=model_checkpoint, aggregation_strategy="simple"
)
token_classifier("patient has diabtes, anemia, hypertension with ckd which hurts the patient since 6 years. Patient today experience with right leg pain, fever and cough.")

Model output

[{'entity_group': 'Disease',
  'score': 0.69145554,
  'word': 'diabtes',
  'start': 12,
  'end': 19},
 {'entity_group': 'Disease',
  'score': 0.9955915,
  'word': 'anemia',
  'start': 21,
  'end': 27},
 {'entity_group': 'Disease',
  'score': 0.99971104,
  'word': 'hypertension',
  'start': 29,
  'end': 41},
 {'entity_group': 'Disease',
  'score': 0.9249976,
  'word': 'right leg pain',
  'start': 120,
  'end': 134},
 {'entity_group': 'Disease',
  'score': 0.9983512,
  'word': 'fever',
  'start': 136,
  'end': 141},
 {'entity_group': 'Disease',
  'score': 0.99849665,
  'word': 'cough',
  'start': 146,
  'end': 151}]

Framework versions

• Transformers 4.35.2
• Pytorch 2.1.0+cu121
• Datasets 2.16.1
• Tokenizers 0.15.1