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ner_model_ep1

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metadata
license: apache-2.0
base_model: distilbert/distilbert-base-uncased
tags:
  - generated_from_trainer
model-index:
  - name: ner_model_ep1
    results: []

ner_model_ep1

This model is a fine-tuned version of distilbert/distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.3469
  • allergy Name F1: 0.7059
  • allergy Name Pres: 0.7326
  • allergy Name Rec: 0.6811
  • cancer F1: 0.6499
  • cancer Pres: 0.6837
  • cancer Rec: 0.6192
  • chronic Disease F1: 0.7431
  • chronic Disease Pres: 0.7462
  • chronic Disease Rec: 0.7400
  • treatment F1: 0.7572
  • treatmen Prest: 0.7680
  • treatment Rec: 0.7468
  • Over All Precision: 0.7475
  • Over All Recall: 0.7237
  • Over All F1: 0.7354
  • Over All Accuracy: 0.8824

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: 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: 5

Training results

Training Loss Epoch Step Validation Loss allergy Name F1 allergy Name Pres allergy Name Rec cancer F1 cancer Pres cancer Rec chronic Disease F1 chronic Disease Pres chronic Disease Rec treatment F1 treatmen Prest treatment Rec Over All Precision Over All Recall Over All F1 Over All Accuracy
0.5799 1.0 368 0.4111 0.2933 0.825 0.1784 0.5345 0.5010 0.5728 0.6044 0.6269 0.5834 0.6718 0.6294 0.7204 0.6084 0.6379 0.6228 0.8467
0.3846 2.0 736 0.3624 0.6618 0.6054 0.7297 0.6057 0.6025 0.6088 0.6553 0.6925 0.6219 0.7153 0.7450 0.6879 0.7 0.6537 0.6761 0.8642
0.3069 3.0 1104 0.3516 0.6801 0.7284 0.6378 0.6316 0.6489 0.6152 0.6994 0.7227 0.6775 0.7317 0.7368 0.7267 0.7187 0.6906 0.7044 0.8733
0.2571 4.0 1472 0.3492 0.6807 0.7687 0.6108 0.6472 0.6867 0.612 0.7239 0.7276 0.7201 0.7456 0.7548 0.7366 0.7358 0.7092 0.7222 0.8779
0.2276 5.0 1840 0.3469 0.7059 0.7326 0.6811 0.6499 0.6837 0.6192 0.7431 0.7462 0.7400 0.7572 0.7680 0.7468 0.7475 0.7237 0.7354 0.8824

Framework versions

  • Transformers 4.42.4
  • Pytorch 2.3.1+cu121
  • Datasets 2.20.0
  • Tokenizers 0.19.1