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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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- medical |
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- science |
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datasets: |
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- ncbi_disease |
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model-index: |
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- name: bert-base-cased-finetuned-ner-NCBI_Disease |
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results: [] |
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language: |
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- en |
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metrics: |
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- seqeval |
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- f1 |
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- recall |
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- accuracy |
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- precision |
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pipeline_tag: token-classification |
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--- |
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# bert-base-cased-finetuned-ner-NCBI_Disease |
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This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the ncbi_disease dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0614 |
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- Disease: |
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- Precision: 0.8063891577928364 |
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- Recall: 0.8677083333333333 |
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- F1: 0.8359257400903161 |
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- Number: 960 |
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- Overall |
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- Precision: 0.8064 |
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- Recall: 0.8677 |
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- F1: 0.8359 |
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- Accuracy: 0.9825 |
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## Model description |
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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 |
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## Intended uses & limitations |
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This model is intended to demonstrate my ability to solve a complex problem using technology. |
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## Training and evaluation data |
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Data Source: https://huggingface.co/datasets/ncbi_disease |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Disease Precision | Disease Recall | Disease F1 | Disease Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |
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|:-----------:|:-----:|:----:|:---------------:|:--------:|:--------:|:--------:|:--------:|:-----------------:|:--------------:|:----------:|:-------:| |
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| 0.0525 | 1.0 | 340 | 0.0617 | 0.7813 | 0.7854 | 0.7834 | 960 | 0.7813 | 0.7854 | 0.7834 | 0.9796 | |
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| 0.022 | 2.0 | 680 | 0.0551 | 0.7897 | 0.8646 | 0.8255 | 960 | 0.7897 | 0.8646 | 0.8255 | 0.9819 | |
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| 0.0154 | 3.0 | 1020 | 0.0614 | 0.8064 | 0.8677 | 0.8359 | 960 | 0.8064 | 0.8677 | 0.8359 | 0.9825 | |
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* All values in the above chart are rounded to the nearest ten-thousandth. |
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### Framework versions |
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- Transformers 4.28.1 |
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- Pytorch 2.0.0 |
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- Datasets 2.11.0 |
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- Tokenizers 0.13.3 |
