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---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- recall
- precision
model-index:
- name: biobert-biocause-trainer
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-biocause-trainer
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1681
- Accuracy: 0.9485
- F1: 0.9040
- Recall: 0.9511
- Precision: 0.8614
## 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: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Recall | Precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 0.6094 | 0.16 | 50 | 0.5106 | 0.7701 | 0.6246 | 0.7492 | 0.5355 |
| 0.5744 | 0.32 | 100 | 0.4291 | 0.8132 | 0.6898 | 0.8139 | 0.5986 |
| 0.5282 | 0.48 | 150 | 0.3735 | 0.7963 | 0.6995 | 0.9290 | 0.5610 |
| 0.4704 | 0.64 | 200 | 0.4850 | 0.8965 | 0.7724 | 0.6877 | 0.8808 |
| 0.4809 | 0.8 | 250 | 0.2955 | 0.9074 | 0.8192 | 0.8218 | 0.8166 |
| 0.3985 | 0.96 | 300 | 0.2699 | 0.8829 | 0.8014 | 0.9259 | 0.7064 |
| 0.347 | 1.13 | 350 | 0.2695 | 0.9275 | 0.8587 | 0.8628 | 0.8547 |
| 0.3729 | 1.29 | 400 | 0.2227 | 0.9320 | 0.8723 | 0.9101 | 0.8374 |
| 0.4059 | 1.45 | 450 | 0.2130 | 0.9420 | 0.8894 | 0.9132 | 0.8668 |
| 0.3023 | 1.61 | 500 | 0.1996 | 0.9477 | 0.8989 | 0.9117 | 0.8865 |
| 0.2676 | 1.77 | 550 | 0.1814 | 0.9521 | 0.9074 | 0.9196 | 0.8955 |
| 0.4202 | 1.93 | 600 | 0.1702 | 0.9452 | 0.8987 | 0.9511 | 0.8517 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.3.1
- Datasets 2.19.1
- Tokenizers 0.15.1
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