---
base_model: alexyalunin/RuBioRoBERTa
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: nerel-bio-RuBioRoBERTa-base
  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-RuBioRoBERTa-base

This model is a fine-tuned version of [alexyalunin/RuBioRoBERTa](https://huggingface.co/alexyalunin/RuBioRoBERTa) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5262
- Precision: 0.8251
- Recall: 0.8335
- F1: 0.8293
- Accuracy: 0.8827

## 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: 6
- eval_batch_size: 6
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 102  | 1.7932          | 0.4125    | 0.4094 | 0.4110 | 0.5484   |
| No log        | 2.0   | 204  | 0.5751          | 0.7711    | 0.7635 | 0.7673 | 0.8392   |
| No log        | 3.0   | 306  | 0.4426          | 0.8053    | 0.8163 | 0.8107 | 0.8727   |
| No log        | 4.0   | 408  | 0.4545          | 0.8070    | 0.8049 | 0.8060 | 0.8707   |
| 0.8666        | 5.0   | 510  | 0.4854          | 0.8100    | 0.8024 | 0.8062 | 0.8693   |
| 0.8666        | 6.0   | 612  | 0.4791          | 0.8194    | 0.8210 | 0.8202 | 0.8805   |
| 0.8666        | 7.0   | 714  | 0.4975          | 0.8202    | 0.8306 | 0.8254 | 0.8816   |
| 0.8666        | 8.0   | 816  | 0.4997          | 0.8217    | 0.8304 | 0.8260 | 0.8817   |
| 0.8666        | 9.0   | 918  | 0.5237          | 0.8237    | 0.8318 | 0.8277 | 0.8821   |
| 0.0548        | 10.0  | 1020 | 0.5262          | 0.8251    | 0.8335 | 0.8293 | 0.8827   |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.15.2