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---
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: []
datasets:
- tner/bionlp2004
language:
- en
pipeline_tag: token-classification
inference: true
library_name: transformers
---
<!-- 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 |