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---
license: apache-2.0
tags:
- generated_from_trainer
- biology
datasets:
- bionlp2004
model-index:
- name: bert-base-cased-finetuned-ner-bio_nlp_2004
results: []
language:
- en
metrics:
- seqeval
- f1
- recall
- precision
pipeline_tag: token-classification
---
# bert-base-cased-finetuned-ner-bio_nlp_2004
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased).
It achieves the following results on the evaluation set:
- Loss: 0.2066
- Dna:
- Precision: 0.6619127516778524
- Recall: 0.7471590909090909
- F1: 0.7019572953736656
- Number: 1056
- Rna:
- Precision: 0.589041095890411
- Recall: 0.7288135593220338
- F1: 0.6515151515151515
- Number': 118
- Cell Line:
- Precision: 0.4758522727272727
- Recall: 0.67
- F1: 0.5564784053156145
- Number: 500
- Cell Type:
- Precision: 0.7294117647058823
- Recall: 0.7100468505986466
- F1: 0.7195990503824848
- Number: 1921
- Protein:
- Precision: 0.6657656225155033
- Recall: 0.8263272153147819
- F1: 0.7374075378654457
- Number': 5067
- Overall
- Precision: 0.6628
- Recall: 0.7805
- F1: 0.7169
- Accuracy: 0.9367
## 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/tner-bionlp2004/NER%20Project%20Using%20tner-bionlp%202004%20Dataset%20(BERT-Base).ipynb
## Intended uses & limitations
This model is intended to demonstrate my ability to solve a complex problem using technology.
## Training and evaluation data
Dataset Source: https://huggingface.co/datasets/tner/bionlp2004
## 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 | Valid. Loss | Dna Precision | Dna Recall | Dna F1 | Dna Number | Rna Precision | Rna Recall | Rna F1 | Rna Number | Cell Line Precision | Cell Type Recall | Cell Type F1 | Cell Type Number | Cell Type | Protein Precision | Protein Recall | Protein F1 | Protein Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:---------:|:-----:|:----:|:---------:|:-------:|:------:|:------:|:------:|:-------------:|:----------:|:------:|:----------:|:----------:|:---------:|:-------:|:-------:|:---------:|:-------:|:--------:|:------:|:-----------:|:--------:|:--------:|:----------:|:---------:|
| 0.1701 | 1.0 | 1039 | 0.1927 | 0.6153 | 0.7254 | 0.6658 | 1056 | 0.6617 | 0.7458 | 0.7012 | 118 | 0.4670 | 0.608 | 0.5282 | 500 | 0.6997 | 0.7158 | 0.7077 | 1921 | 0.6603 | 0.7833 | 0.7166 | 5067 | 0.6499 | 0.7506 | 0.6966 | 0.9352 |
| 0.145 | 2.0 | 2078 | 0.1981 | 0.6364 | 0.7443 | 0.6862 | 1056 | 0.6408 | 0.7712 | 0.7000 | 118 | 0.4607 | 0.668 | 0.5453 | 500 | 0.7376 | 0.7022 | 0.7195 | 1921 | 0.6759 | 0.8149 | 0.7389 | 5067 | 0.6662 | 0.7722 | 0.7153 | 0.9364 |
| 0.1116 | 3.0 | 3117 | 0.2066 | 0.6619 | 0.7472 | 0.7020 | 1056 | 0.5890 | 0.7288 | 0.6515 | 118 | 0.4759 | 0.67 | 0.5565 | 500 | 0.7294 | 0.7100 | 0.7196 | 1921 | 0.6658 | 0.8263 | 0.7374 | 5067 | 0.6628 | 0.7805 | 0.7169 | 0.9367 |
* Metrics shown above are rounded to the neareset ten-thousandth
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0
- Datasets 2.11.0
- Tokenizers 0.13.3 |