---
license: mit
base_model: cointegrated/rubert-tiny2
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: rubert-tiny2-odonata-extended-305-1-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-305-1-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: 0.0101
- Precision: 0.6420
- Recall: 0.5821
- F1: 0.6106
- Accuracy: 0.9967

## 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   | 0.1033          | 0.0       | 0.0    | 0.0    | 0.9952   |
| No log        | 2.0   | 64   | 0.0391          | 0.0       | 0.0    | 0.0    | 0.9952   |
| No log        | 3.0   | 96   | 0.0351          | 0.0       | 0.0    | 0.0    | 0.9952   |
| No log        | 4.0   | 128  | 0.0321          | 0.0       | 0.0    | 0.0    | 0.9952   |
| No log        | 5.0   | 160  | 0.0260          | 0.0       | 0.0    | 0.0    | 0.9952   |
| No log        | 6.0   | 192  | 0.0188          | 0.6809    | 0.1194 | 0.2032 | 0.9955   |
| No log        | 7.0   | 224  | 0.0158          | 0.6480    | 0.4328 | 0.5190 | 0.9961   |
| No log        | 8.0   | 256  | 0.0143          | 0.6567    | 0.4925 | 0.5629 | 0.9964   |
| No log        | 9.0   | 288  | 0.0133          | 0.6573    | 0.4366 | 0.5247 | 0.9963   |
| No log        | 10.0  | 320  | 0.0127          | 0.5898    | 0.5634 | 0.5763 | 0.9964   |
| No log        | 11.0  | 352  | 0.0122          | 0.6128    | 0.5373 | 0.5726 | 0.9965   |
| No log        | 12.0  | 384  | 0.0119          | 0.6122    | 0.6007 | 0.6064 | 0.9965   |
| No log        | 13.0  | 416  | 0.0114          | 0.6295    | 0.5261 | 0.5732 | 0.9965   |
| No log        | 14.0  | 448  | 0.0112          | 0.6349    | 0.5709 | 0.6012 | 0.9967   |
| No log        | 15.0  | 480  | 0.0111          | 0.6174    | 0.6082 | 0.6128 | 0.9966   |
| 0.0665        | 16.0  | 512  | 0.0108          | 0.6491    | 0.5522 | 0.5968 | 0.9967   |
| 0.0665        | 17.0  | 544  | 0.0108          | 0.6232    | 0.6418 | 0.6324 | 0.9967   |
| 0.0665        | 18.0  | 576  | 0.0106          | 0.6571    | 0.5149 | 0.5774 | 0.9967   |
| 0.0665        | 19.0  | 608  | 0.0105          | 0.6271    | 0.5522 | 0.5873 | 0.9965   |
| 0.0665        | 20.0  | 640  | 0.0105          | 0.6332    | 0.6119 | 0.6224 | 0.9967   |
| 0.0665        | 21.0  | 672  | 0.0104          | 0.6390    | 0.5746 | 0.6051 | 0.9966   |
| 0.0665        | 22.0  | 704  | 0.0104          | 0.6316    | 0.5821 | 0.6058 | 0.9966   |
| 0.0665        | 23.0  | 736  | 0.0103          | 0.6444    | 0.5410 | 0.5882 | 0.9966   |
| 0.0665        | 24.0  | 768  | 0.0103          | 0.6287    | 0.5560 | 0.5901 | 0.9966   |
| 0.0665        | 25.0  | 800  | 0.0102          | 0.6322    | 0.5709 | 0.6000 | 0.9966   |
| 0.0665        | 26.0  | 832  | 0.0102          | 0.6360    | 0.5672 | 0.5996 | 0.9966   |
| 0.0665        | 27.0  | 864  | 0.0102          | 0.6352    | 0.5784 | 0.6055 | 0.9966   |
| 0.0665        | 28.0  | 896  | 0.0102          | 0.6453    | 0.5634 | 0.6016 | 0.9967   |
| 0.0665        | 29.0  | 928  | 0.0101          | 0.6402    | 0.5709 | 0.6036 | 0.9967   |
| 0.0665        | 30.0  | 960  | 0.0101          | 0.6420    | 0.5821 | 0.6106 | 0.9967   |


### Framework versions

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