---
base_model: alexyalunin/RuBioRoBERTa
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: RuBioRoBERTa_neg
  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. -->

# RuBioRoBERTa_neg

This model is a fine-tuned version of [alexyalunin/RuBioRoBERTa](https://huggingface.co/alexyalunin/RuBioRoBERTa) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5876
- Precision: 0.584
- Recall: 0.6053
- F1: 0.5945
- Accuracy: 0.9040

## 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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 50   | 0.6516          | 0.0       | 0.0    | 0.0    | 0.7729   |
| No log        | 2.0   | 100  | 0.6625          | 0.0       | 0.0    | 0.0    | 0.7706   |
| No log        | 3.0   | 150  | 0.5142          | 0.0081    | 0.0058 | 0.0068 | 0.7944   |
| No log        | 4.0   | 200  | 0.4359          | 0.0788    | 0.1464 | 0.1024 | 0.8281   |
| No log        | 5.0   | 250  | 0.3580          | 0.2362    | 0.3141 | 0.2696 | 0.8642   |
| No log        | 6.0   | 300  | 0.3419          | 0.2819    | 0.3237 | 0.3013 | 0.8762   |
| No log        | 7.0   | 350  | 0.3492          | 0.35      | 0.3642 | 0.3569 | 0.8841   |
| No log        | 8.0   | 400  | 0.2633          | 0.3549    | 0.4432 | 0.3942 | 0.8982   |
| No log        | 9.0   | 450  | 0.2819          | 0.3871    | 0.4624 | 0.4214 | 0.9001   |
| 0.4095        | 10.0  | 500  | 0.2522          | 0.5035    | 0.5491 | 0.5253 | 0.9119   |
| 0.4095        | 11.0  | 550  | 0.2831          | 0.4704    | 0.5511 | 0.5075 | 0.9077   |
| 0.4095        | 12.0  | 600  | 0.3013          | 0.5245    | 0.6185 | 0.5676 | 0.9105   |
| 0.4095        | 13.0  | 650  | 0.3070          | 0.4711    | 0.6127 | 0.5327 | 0.9048   |
| 0.4095        | 14.0  | 700  | 0.3398          | 0.4771    | 0.6416 | 0.5472 | 0.9039   |
| 0.4095        | 15.0  | 750  | 0.3275          | 0.4661    | 0.6224 | 0.5330 | 0.9114   |
| 0.4095        | 16.0  | 800  | 0.3730          | 0.5118    | 0.6281 | 0.5640 | 0.9141   |
| 0.4095        | 17.0  | 850  | 0.3847          | 0.5593    | 0.6358 | 0.5951 | 0.9160   |
| 0.4095        | 18.0  | 900  | 0.4070          | 0.5824    | 0.6262 | 0.6035 | 0.9182   |
| 0.4095        | 19.0  | 950  | 0.3583          | 0.5433    | 0.6281 | 0.5827 | 0.9161   |
| 0.0776        | 20.0  | 1000 | 0.3096          | 0.5152    | 0.5877 | 0.5491 | 0.9154   |
| 0.0776        | 21.0  | 1050 | 0.4015          | 0.5669    | 0.6204 | 0.5925 | 0.9224   |
| 0.0776        | 22.0  | 1100 | 0.5603          | 0.4251    | 0.6667 | 0.5191 | 0.8753   |
| 0.0776        | 23.0  | 1150 | 0.3353          | 0.6220    | 0.6089 | 0.6154 | 0.9230   |
| 0.0776        | 24.0  | 1200 | 0.3800          | 0.6133    | 0.6204 | 0.6169 | 0.9254   |
| 0.0776        | 25.0  | 1250 | 0.4451          | 0.5792    | 0.6127 | 0.5955 | 0.9153   |
| 0.0776        | 26.0  | 1300 | 0.4639          | 0.6060    | 0.6224 | 0.6141 | 0.9220   |
| 0.0776        | 27.0  | 1350 | 0.4141          | 0.5574    | 0.6647 | 0.6063 | 0.9194   |
| 0.0776        | 28.0  | 1400 | 0.4258          | 0.5675    | 0.6397 | 0.6014 | 0.9143   |
| 0.0776        | 29.0  | 1450 | 0.4131          | 0.5880    | 0.6435 | 0.6145 | 0.9193   |
| 0.0374        | 30.0  | 1500 | 0.4104          | 0.5823    | 0.6609 | 0.6191 | 0.9200   |
| 0.0374        | 31.0  | 1550 | 0.4047          | 0.6190    | 0.6667 | 0.6419 | 0.9213   |
| 0.0374        | 32.0  | 1600 | 0.4615          | 0.6233    | 0.6185 | 0.6209 | 0.9205   |
| 0.0374        | 33.0  | 1650 | 0.4597          | 0.6430    | 0.5934 | 0.6172 | 0.9169   |
| 0.0374        | 34.0  | 1700 | 0.3851          | 0.5043    | 0.6821 | 0.5799 | 0.9040   |
| 0.0374        | 35.0  | 1750 | 0.3989          | 0.6241    | 0.6590 | 0.6410 | 0.9206   |
| 0.0374        | 36.0  | 1800 | 0.4866          | 0.5710    | 0.6667 | 0.6151 | 0.9156   |
| 0.0374        | 37.0  | 1850 | 0.4198          | 0.6208    | 0.6339 | 0.6273 | 0.9241   |
| 0.0374        | 38.0  | 1900 | 0.4526          | 0.5615    | 0.6243 | 0.5912 | 0.9164   |
| 0.0374        | 39.0  | 1950 | 0.5038          | 0.6149    | 0.6031 | 0.6089 | 0.9187   |
| 0.0337        | 40.0  | 2000 | 0.3879          | 0.5684    | 0.6243 | 0.5950 | 0.9196   |
| 0.0337        | 41.0  | 2050 | 0.5178          | 0.5913    | 0.6301 | 0.6101 | 0.9170   |
| 0.0337        | 42.0  | 2100 | 0.4898          | 0.6558    | 0.5838 | 0.6177 | 0.9155   |


### Framework versions

- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1