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Rodrigo1771
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BioLinkBERT-base-drugtemist-en-word2vec-8-ner

Token Classification
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This model is a fine-tuned version of michiyasunaga/BioLinkBERT-base on the Rodrigo1771/drugtemist-en-8-ner dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0057
  • Precision: 0.9318
  • Recall: 0.9301
  • F1: 0.9310
  • Accuracy: 0.9987

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: 5e-05
  • train_batch_size: 32
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 64
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 10.0

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
No log 1.0 493 0.0050 0.9288 0.9245 0.9267 0.9987
0.018 2.0 986 0.0057 0.9104 0.9189 0.9147 0.9984
0.0044 3.0 1479 0.0079 0.9362 0.9161 0.9260 0.9985
0.0023 4.0 1972 0.0057 0.9318 0.9301 0.9310 0.9987
0.0014 5.0 2465 0.0070 0.9201 0.9226 0.9214 0.9986
0.0008 6.0 2958 0.0082 0.9118 0.9254 0.9186 0.9985
0.0006 7.0 3451 0.0074 0.9172 0.9394 0.9282 0.9986
0.0003 8.0 3944 0.0085 0.9219 0.9245 0.9232 0.9985
0.0003 9.0 4437 0.0086 0.9149 0.9320 0.9234 0.9985
0.0002 10.0 4930 0.0089 0.9172 0.9292 0.9231 0.9985

Framework versions

  • Transformers 4.44.2
  • Pytorch 2.4.0+cu121
  • Datasets 2.21.0
  • Tokenizers 0.19.1
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Base model

michiyasunaga/BioLinkBERT-base
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Dataset used to train Rodrigo1771/BioLinkBERT-base-drugtemist-en-word2vec-8-ner

Evaluation results

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