|
|
--- |
|
|
library_name: transformers |
|
|
base_model: dmis-lab/biobert-base-cased-v1.1 |
|
|
tags: |
|
|
- generated_from_trainer |
|
|
datasets: |
|
|
- ncbi_disease |
|
|
model-index: |
|
|
- name: biobert-ner-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. --> |
|
|
|
|
|
# biobert-ner-model |
|
|
|
|
|
This model is a fine-tuned version of [dmis-lab/biobert-base-cased-v1.1](https://huggingface.co/dmis-lab/biobert-base-cased-v1.1) on the ncbi_disease dataset. |
|
|
It achieves the following results on the evaluation set: |
|
|
- Loss: 0.0557 |
|
|
- Compositemention: {'precision': 0.2765957446808511, 'recall': 0.37142857142857144, 'f1': 0.3170731707317073, 'number': 35} |
|
|
- Diseaseclass: {'precision': 0.5, 'recall': 0.6031746031746031, 'f1': 0.5467625899280575, 'number': 126} |
|
|
- Modifier: {'precision': 0.7665198237885462, 'recall': 0.8169014084507042, 'f1': 0.7909090909090909, 'number': 213} |
|
|
- Specificdisease: {'precision': 0.75625, 'recall': 0.8810679611650486, 'f1': 0.81390134529148, 'number': 412} |
|
|
- Overall Precision: 0.6909 |
|
|
- Overall Recall: 0.7964 |
|
|
- Overall F1: 0.7400 |
|
|
- Overall Accuracy: 0.9854 |
|
|
|
|
|
## 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: 8 |
|
|
- eval_batch_size: 8 |
|
|
- seed: 42 |
|
|
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
|
|
- lr_scheduler_type: linear |
|
|
- num_epochs: 5 |
|
|
|
|
|
### Training results |
|
|
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Compositemention | Diseaseclass | Modifier | Specificdisease | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |
|
|
|:-------------:|:-----:|:----:|:---------------:|:-----------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:| |
|
|
| 0.0688 | 1.0 | 75 | 0.0687 | {'precision': 0.075, 'recall': 0.08571428571428572, 'f1': 0.08, 'number': 35} | {'precision': 0.3684210526315789, 'recall': 0.4444444444444444, 'f1': 0.4028776978417266, 'number': 126} | {'precision': 0.5581395348837209, 'recall': 0.676056338028169, 'f1': 0.6114649681528661, 'number': 213} | {'precision': 0.6083788706739527, 'recall': 0.8106796116504854, 'f1': 0.6951092611862643, 'number': 412} | 0.5375 | 0.6832 | 0.6017 | 0.9797 | |
|
|
| 0.0416 | 2.0 | 150 | 0.0621 | {'precision': 0.038461538461538464, 'recall': 0.02857142857142857, 'f1': 0.03278688524590164, 'number': 35} | {'precision': 0.3706896551724138, 'recall': 0.3412698412698413, 'f1': 0.3553719008264463, 'number': 126} | {'precision': 0.6653225806451613, 'recall': 0.7746478873239436, 'f1': 0.7158351409978307, 'number': 213} | {'precision': 0.6208695652173913, 'recall': 0.866504854368932, 'f1': 0.7234042553191489, 'number': 412} | 0.5865 | 0.7201 | 0.6465 | 0.9817 | |
|
|
| 0.0399 | 3.0 | 225 | 0.0537 | {'precision': 0.26666666666666666, 'recall': 0.34285714285714286, 'f1': 0.3, 'number': 35} | {'precision': 0.45390070921985815, 'recall': 0.5079365079365079, 'f1': 0.4794007490636704, 'number': 126} | {'precision': 0.7522123893805309, 'recall': 0.7981220657276995, 'f1': 0.7744874715261958, 'number': 213} | {'precision': 0.6927592954990215, 'recall': 0.8592233009708737, 'f1': 0.7670639219934994, 'number': 412} | 0.6501 | 0.7634 | 0.7022 | 0.9848 | |
|
|
| 0.0263 | 4.0 | 300 | 0.0548 | {'precision': 0.3125, 'recall': 0.42857142857142855, 'f1': 0.3614457831325301, 'number': 35} | {'precision': 0.47096774193548385, 'recall': 0.5793650793650794, 'f1': 0.5195729537366549, 'number': 126} | {'precision': 0.776255707762557, 'recall': 0.7981220657276995, 'f1': 0.7870370370370371, 'number': 213} | {'precision': 0.7433264887063655, 'recall': 0.8786407766990292, 'f1': 0.8053392658509455, 'number': 412} | 0.6821 | 0.7888 | 0.7316 | 0.9852 | |
|
|
| 0.0214 | 5.0 | 375 | 0.0557 | {'precision': 0.2765957446808511, 'recall': 0.37142857142857144, 'f1': 0.3170731707317073, 'number': 35} | {'precision': 0.5, 'recall': 0.6031746031746031, 'f1': 0.5467625899280575, 'number': 126} | {'precision': 0.7665198237885462, 'recall': 0.8169014084507042, 'f1': 0.7909090909090909, 'number': 213} | {'precision': 0.75625, 'recall': 0.8810679611650486, 'f1': 0.81390134529148, 'number': 412} | 0.6909 | 0.7964 | 0.7400 | 0.9854 | |
|
|
|
|
|
|
|
|
### Framework versions |
|
|
|
|
|
- Transformers 4.51.3 |
|
|
- Pytorch 2.6.0+cu124 |
|
|
- Datasets 3.6.0 |
|
|
- Tokenizers 0.21.1 |
|
|
|
