output
This model is a fine-tuned version of michiyasunaga/BioLinkBERT-base on the Rodrigo1771/drugtemist-en-ner dataset. It achieves the following results on the evaluation set:
- Loss: 0.0068
- Precision: 0.9139
- Recall: 0.9301
- F1: 0.9219
- Accuracy: 0.9986
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: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- 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 |
---|---|---|---|---|---|---|---|
0.0093 | 0.9997 | 1735 | 0.0096 | 0.9094 | 0.8705 | 0.8895 | 0.9982 |
0.0045 | 2.0 | 3471 | 0.0055 | 0.8789 | 0.9469 | 0.9116 | 0.9983 |
0.004 | 2.9997 | 5206 | 0.0071 | 0.8980 | 0.9189 | 0.9083 | 0.9985 |
0.0035 | 4.0 | 6942 | 0.0079 | 0.8373 | 0.9404 | 0.8859 | 0.9981 |
0.004 | 4.9997 | 8677 | 0.0071 | 0.9377 | 0.8984 | 0.9177 | 0.9985 |
0.0024 | 6.0 | 10413 | 0.0054 | 0.8922 | 0.9329 | 0.9121 | 0.9985 |
0.0014 | 6.9997 | 12148 | 0.0068 | 0.9139 | 0.9301 | 0.9219 | 0.9986 |
0.0012 | 8.0 | 13884 | 0.0073 | 0.9080 | 0.9292 | 0.9185 | 0.9986 |
0.0013 | 8.9997 | 15619 | 0.0067 | 0.9007 | 0.9301 | 0.9152 | 0.9986 |
0.0012 | 9.9971 | 17350 | 0.0069 | 0.9128 | 0.9273 | 0.9200 | 0.9986 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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Finetuned from
Dataset used to train Rodrigo1771/BioLinkBERT-base-drugtemist-en-ner
Evaluation results
- Precision on Rodrigo1771/drugtemist-en-nervalidation set self-reported0.914
- Recall on Rodrigo1771/drugtemist-en-nervalidation set self-reported0.930
- F1 on Rodrigo1771/drugtemist-en-nervalidation set self-reported0.922
- Accuracy on Rodrigo1771/drugtemist-en-nervalidation set self-reported0.999