---
license: mit
base_model: cointegrated/rubert-tiny2
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: rubert-tiny2-odonata-extended-ner
  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. -->

# rubert-tiny2-odonata-extended-ner

This model is a fine-tuned version of [cointegrated/rubert-tiny2](https://huggingface.co/cointegrated/rubert-tiny2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0063
- Precision: 0.7067
- Recall: 0.7681
- F1: 0.7361
- Accuracy: 0.9981

## 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
- num_epochs: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 32   | 0.1795          | 0.0       | 0.0    | 0.0    | 0.9961   |
| No log        | 2.0   | 64   | 0.0386          | 0.0       | 0.0    | 0.0    | 0.9961   |
| No log        | 3.0   | 96   | 0.0316          | 0.0       | 0.0    | 0.0    | 0.9961   |
| No log        | 4.0   | 128  | 0.0292          | 0.0       | 0.0    | 0.0    | 0.9961   |
| No log        | 5.0   | 160  | 0.0231          | 0.0       | 0.0    | 0.0    | 0.9961   |
| No log        | 6.0   | 192  | 0.0152          | 0.6923    | 0.1304 | 0.2195 | 0.9964   |
| No log        | 7.0   | 224  | 0.0123          | 0.6212    | 0.5942 | 0.6074 | 0.9971   |
| No log        | 8.0   | 256  | 0.0108          | 0.5946    | 0.6377 | 0.6154 | 0.9970   |
| No log        | 9.0   | 288  | 0.0099          | 0.6269    | 0.6087 | 0.6176 | 0.9972   |
| No log        | 10.0  | 320  | 0.0092          | 0.5921    | 0.6522 | 0.6207 | 0.9971   |
| No log        | 11.0  | 352  | 0.0087          | 0.6267    | 0.6812 | 0.6528 | 0.9974   |
| No log        | 12.0  | 384  | 0.0083          | 0.65      | 0.7536 | 0.6980 | 0.9977   |
| No log        | 13.0  | 416  | 0.0079          | 0.6456    | 0.7391 | 0.6892 | 0.9976   |
| No log        | 14.0  | 448  | 0.0076          | 0.6375    | 0.7391 | 0.6846 | 0.9977   |
| No log        | 15.0  | 480  | 0.0074          | 0.6667    | 0.7826 | 0.72   | 0.9979   |
| 0.0795        | 16.0  | 512  | 0.0072          | 0.6933    | 0.7536 | 0.7222 | 0.9980   |
| 0.0795        | 17.0  | 544  | 0.0071          | 0.6420    | 0.7536 | 0.6933 | 0.9978   |
| 0.0795        | 18.0  | 576  | 0.0069          | 0.6806    | 0.7101 | 0.6950 | 0.9979   |
| 0.0795        | 19.0  | 608  | 0.0068          | 0.68      | 0.7391 | 0.7083 | 0.9980   |
| 0.0795        | 20.0  | 640  | 0.0067          | 0.68      | 0.7391 | 0.7083 | 0.9980   |
| 0.0795        | 21.0  | 672  | 0.0066          | 0.6842    | 0.7536 | 0.7172 | 0.9980   |
| 0.0795        | 22.0  | 704  | 0.0065          | 0.6933    | 0.7536 | 0.7222 | 0.9980   |
| 0.0795        | 23.0  | 736  | 0.0065          | 0.6849    | 0.7246 | 0.7042 | 0.9980   |
| 0.0795        | 24.0  | 768  | 0.0064          | 0.7027    | 0.7536 | 0.7273 | 0.9981   |
| 0.0795        | 25.0  | 800  | 0.0063          | 0.7027    | 0.7536 | 0.7273 | 0.9981   |
| 0.0795        | 26.0  | 832  | 0.0063          | 0.7162    | 0.7681 | 0.7413 | 0.9981   |
| 0.0795        | 27.0  | 864  | 0.0063          | 0.7162    | 0.7681 | 0.7413 | 0.9981   |
| 0.0795        | 28.0  | 896  | 0.0063          | 0.7027    | 0.7536 | 0.7273 | 0.9981   |
| 0.0795        | 29.0  | 928  | 0.0063          | 0.7067    | 0.7681 | 0.7361 | 0.9981   |
| 0.0795        | 30.0  | 960  | 0.0063          | 0.7067    | 0.7681 | 0.7361 | 0.9981   |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.1+cpu
- Datasets 2.19.2
- Tokenizers 0.19.1