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