---
base_model: dmis-lab/biobert-base-cased-v1.1
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: cer_model-i
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# cer_model-i

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 None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4254
- Precision: 0.9224
- Recall: 0.8589
- F1: 0.8895
- Accuracy: 0.9318

## 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: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0212        | 1.0   | 4841  | 0.3474          | 0.9107    | 0.8663 | 0.8879 | 0.9305   |
| 0.0029        | 2.0   | 9682  | 0.3771          | 0.9206    | 0.8603 | 0.8894 | 0.9322   |
| 0.0008        | 3.0   | 14523 | 0.4254          | 0.9224    | 0.8589 | 0.8895 | 0.9318   |


### Framework versions

- Transformers 4.37.0
- Pytorch 2.1.2
- Datasets 2.1.0
- Tokenizers 0.15.1