---
base_model: alexyalunin/RuBioBERT
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: nerel-bio-RuBioBERT-al
  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. -->

# nerel-bio-RuBioBERT-al

This model is a fine-tuned version of [alexyalunin/RuBioBERT](https://huggingface.co./alexyalunin/RuBioBERT) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9899
- Precision: 0.7839
- Recall: 0.7903
- F1: 0.7871
- Accuracy: 0.8572

## 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: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.044         | 1.0   | 153  | 0.8960          | 0.7760    | 0.7814 | 0.7787 | 0.8499   |
| 0.0126        | 2.0   | 306  | 0.9091          | 0.7808    | 0.7810 | 0.7809 | 0.8505   |
| 0.0264        | 3.0   | 459  | 0.8231          | 0.7871    | 0.7896 | 0.7883 | 0.8572   |
| 0.007         | 4.0   | 612  | 0.9504          | 0.7825    | 0.7873 | 0.7849 | 0.8552   |
| 0.0027        | 5.0   | 765  | 0.9055          | 0.7838    | 0.7915 | 0.7876 | 0.8581   |
| 0.0066        | 6.0   | 918  | 0.9222          | 0.7829    | 0.7859 | 0.7844 | 0.8577   |
| 0.0005        | 7.0   | 1071 | 0.9561          | 0.7805    | 0.7903 | 0.7854 | 0.8564   |
| 0.0003        | 8.0   | 1224 | 0.9858          | 0.7816    | 0.7889 | 0.7852 | 0.8567   |
| 0.0004        | 9.0   | 1377 | 0.9904          | 0.7834    | 0.7899 | 0.7866 | 0.8572   |
| 0.0003        | 10.0  | 1530 | 0.9899          | 0.7839    | 0.7903 | 0.7871 | 0.8572   |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2