bert-base-cased-finetuned-ner-NCBI_Disease
This model is a fine-tuned version of bert-base-cased on the ncbi_disease dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0614
- Disease:
- Precision: 0.8063891577928364
- Recall: 0.8677083333333333
- F1: 0.8359257400903161
- Number: 960
- Overall
- Precision: 0.8064
- Recall: 0.8677
- F1: 0.8359
- Accuracy: 0.9825
Model description
For more information on how it was created, check out the following link: https://github.com/DunnBC22/NLP_Projects/blob/main/Token%20Classification/Monolingual/NCBI_Disease/NER%20Project%20Using%20NCBI_Disease%20Dataset.ipynb
Intended uses & limitations
This model is intended to demonstrate my ability to solve a complex problem using technology.
Training and evaluation data
Data Source: https://huggingface.co/datasets/ncbi_disease
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
Training results
| Training Loss | Epoch | Step | Validation Loss | Disease Precision | Disease Recall | Disease F1 | Disease Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.0525 | 1.0 | 340 | 0.0617 | 0.7813 | 0.7854 | 0.7834 | 960 | 0.7813 | 0.7854 | 0.7834 | 0.9796 |
| 0.022 | 2.0 | 680 | 0.0551 | 0.7897 | 0.8646 | 0.8255 | 960 | 0.7897 | 0.8646 | 0.8255 | 0.9819 |
| 0.0154 | 3.0 | 1020 | 0.0614 | 0.8064 | 0.8677 | 0.8359 | 960 | 0.8064 | 0.8677 | 0.8359 | 0.9825 |
- All values in the above chart are rounded to the nearest ten-thousandth.
Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0
- Datasets 2.11.0
- Tokenizers 0.13.3
