---
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: 0.0171
- Precision: 0.5782
- Recall: 0.6289
- F1: 0.6025
- Accuracy: 0.9940

## 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   | 25   | 0.2514          | 0.0       | 0.0    | 0.0    | 0.9913   |
| No log        | 2.0   | 50   | 0.0660          | 0.0       | 0.0    | 0.0    | 0.9913   |
| No log        | 3.0   | 75   | 0.0579          | 0.0       | 0.0    | 0.0    | 0.9913   |
| No log        | 4.0   | 100  | 0.0557          | 0.0       | 0.0    | 0.0    | 0.9913   |
| No log        | 5.0   | 125  | 0.0528          | 0.0       | 0.0    | 0.0    | 0.9913   |
| No log        | 6.0   | 150  | 0.0465          | 0.0       | 0.0    | 0.0    | 0.9913   |
| No log        | 7.0   | 175  | 0.0359          | 1.0       | 0.0052 | 0.0103 | 0.9914   |
| No log        | 8.0   | 200  | 0.0278          | 0.5802    | 0.3918 | 0.4677 | 0.9921   |
| No log        | 9.0   | 225  | 0.0241          | 0.5940    | 0.4072 | 0.4832 | 0.9922   |
| No log        | 10.0  | 250  | 0.0223          | 0.6       | 0.4175 | 0.4924 | 0.9925   |
| No log        | 11.0  | 275  | 0.0212          | 0.5417    | 0.4691 | 0.5028 | 0.9930   |
| No log        | 12.0  | 300  | 0.0204          | 0.52      | 0.4691 | 0.4932 | 0.9931   |
| No log        | 13.0  | 325  | 0.0199          | 0.5579    | 0.5464 | 0.5521 | 0.9936   |
| No log        | 14.0  | 350  | 0.0194          | 0.5761    | 0.5464 | 0.5608 | 0.9939   |
| No log        | 15.0  | 375  | 0.0190          | 0.5761    | 0.5464 | 0.5608 | 0.9938   |
| No log        | 16.0  | 400  | 0.0187          | 0.5670    | 0.5670 | 0.5670 | 0.9939   |
| No log        | 17.0  | 425  | 0.0184          | 0.5685    | 0.5773 | 0.5729 | 0.9938   |
| No log        | 18.0  | 450  | 0.0182          | 0.5707    | 0.6031 | 0.5865 | 0.9939   |
| No log        | 19.0  | 475  | 0.0180          | 0.5680    | 0.6031 | 0.5850 | 0.9940   |
| 0.0748        | 20.0  | 500  | 0.0177          | 0.5764    | 0.6031 | 0.5894 | 0.9941   |
| 0.0748        | 21.0  | 525  | 0.0176          | 0.5907    | 0.5876 | 0.5891 | 0.9941   |
| 0.0748        | 22.0  | 550  | 0.0176          | 0.5769    | 0.6186 | 0.5970 | 0.9941   |
| 0.0748        | 23.0  | 575  | 0.0174          | 0.5939    | 0.6031 | 0.5985 | 0.9942   |
| 0.0748        | 24.0  | 600  | 0.0173          | 0.5854    | 0.6186 | 0.6015 | 0.9942   |
| 0.0748        | 25.0  | 625  | 0.0172          | 0.5902    | 0.6237 | 0.6065 | 0.9942   |
| 0.0748        | 26.0  | 650  | 0.0172          | 0.5865    | 0.6289 | 0.6070 | 0.9942   |
| 0.0748        | 27.0  | 675  | 0.0171          | 0.5862    | 0.6134 | 0.5995 | 0.9941   |
| 0.0748        | 28.0  | 700  | 0.0171          | 0.5882    | 0.6186 | 0.6030 | 0.9942   |
| 0.0748        | 29.0  | 725  | 0.0171          | 0.5817    | 0.6237 | 0.6020 | 0.9941   |
| 0.0748        | 30.0  | 750  | 0.0171          | 0.5782    | 0.6289 | 0.6025 | 0.9940   |


### Framework versions

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