| --- |
| license: apache-2.0 |
| base_model: bioformers/bioformer-16L |
| tags: |
| - generated_from_trainer |
| metrics: |
| - f1 |
| - precision |
| - recall |
| - accuracy |
| model-index: |
| - name: cl_ct_custom_model |
| results: [] |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # cl_ct_custom_model |
| |
| This model is a fine-tuned version of [bioformers/bioformer-16L](https://huggingface.co/bioformers/bioformer-16L) on the (https://huggingface.co/datasets/tner/bionlp2004) dataset. |
| It achieves the following results on the evaluation set: |
| |
| - Loss: 0.2590 |
| - F1: 0.7609 |
| - Precision: 0.7112 |
| - Recall: 0.8181 |
| - Accuracy: 0.9229 |
| |
| ## 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: 2e-05 |
| - train_batch_size: 16 |
| - eval_batch_size: 8 |
| - seed: 3407 |
| - gradient_accumulation_steps: 4 |
| - total_train_batch_size: 64 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - num_epochs: 10 |
| - mixed_precision_training: Native AMP |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | F1 | Precision | Recall | Accuracy | |
| |:-------------:|:------:|:----:|:---------------:|:------:|:---------:|:------:|:--------:| |
| | 0.4568 | 0.9971 | 259 | 0.2146 | 0.8139 | 0.7920 | 0.8370 | 0.9326 | |
| | 0.2115 | 1.9981 | 519 | 0.1907 | 0.8349 | 0.8125 | 0.8586 | 0.9379 | |
| | 0.1802 | 2.9990 | 779 | 0.1912 | 0.8407 | 0.8178 | 0.8650 | 0.9394 | |
| | 0.164 | 4.0 | 1039 | 0.1869 | 0.8449 | 0.8255 | 0.8652 | 0.9401 | |
| | 0.1518 | 4.9971 | 1298 | 0.1819 | 0.8525 | 0.8348 | 0.8710 | 0.9428 | |
| | 0.1424 | 5.9981 | 1558 | 0.1842 | 0.8506 | 0.8351 | 0.8666 | 0.9422 | |
| | 0.134 | 6.9990 | 1818 | 0.1869 | 0.8539 | 0.8373 | 0.8712 | 0.9428 | |
| | 0.128 | 8.0 | 2078 | 0.1889 | 0.8540 | 0.8374 | 0.8712 | 0.9429 | |
| | 0.1241 | 8.9971 | 2337 | 0.1892 | 0.8559 | 0.8401 | 0.8724 | 0.9432 | |
| | 0.1199 | 9.9711 | 2590 | 0.1899 | 0.8552 | 0.8392 | 0.8718 | 0.9431 | |
|
|
| ## Eval Classification report |
|
|
| | Class | Precision | Recall | F1-Score | Support | |
| |-------------|------------|--------|----------|---------| |
| | DNA | 0.78 | 0.84 | 0.81 | 2494 | |
| | RNA | 0.83 | 0.89 | 0.86 | 238 | |
| | Cell Line | 0.81 | 0.85 | 0.83 | 1050 | |
| | Cell Type | 0.74 | 0.79 | 0.77 | 775 | |
| | Protein | 0.88 | 0.90 | 0.89 | 6196 | |
| | **Micro Avg** | **0.84** | **0.87** | **0.86** | **10753** | |
| | **Macro Avg** | **0.81** | **0.86** | **0.83** | **10753** | |
| | **Weighted Avg** | **0.84** | **0.87** | **0.86** | **10753** | |
|
|
|
|
| ## Test Results |
|
|
| | Class | Precision | Recall | F1-Score | Support | |
| |-------------|-----------|--------|----------|---------| |
| | DNA | 0.74 | 0.79 | 0.76 | 2210 | |
| | RNA | 0.73 | 0.76 | 0.75 | 287 | |
| | Cell Line | 0.50 | 0.76 | 0.61 | 1057 | |
| | Cell Type | 0.75 | 0.68 | 0.71 | 2761 | |
| | Protein | 0.72 | 0.87 | 0.79 | 10082 | |
| | **Micro Avg** | **0.71** | **0.82** | **0.76** | **16397** | |
| | **Macro Avg** | **0.69** | **0.77** | **0.72** | **16397** | |
| | **Weighted Avg** | **0.72** | **0.82** | **0.76** | **16397** | |
|
|
|
|
| ### Framework versions |
|
|
| - Transformers 4.43.4 |
| - Pytorch 2.4.1+cu121 |
| - Datasets 2.20.0 |
| - Tokenizers 0.19.1 |
|
|
