---
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: 0.0226
- Precision: 0.5313
- Recall: 0.551
- F1: 0.5410
- Accuracy: 0.9930

## 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: 15

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 32   | 0.1425          | 0.0       | 0.0    | 0.0    | 0.9910   |
| No log        | 2.0   | 64   | 0.0643          | 0.0       | 0.0    | 0.0    | 0.9910   |
| No log        | 3.0   | 96   | 0.0604          | 0.0       | 0.0    | 0.0    | 0.9910   |
| No log        | 4.0   | 128  | 0.0575          | 0.0       | 0.0    | 0.0    | 0.9910   |
| No log        | 5.0   | 160  | 0.0523          | 0.0       | 0.0    | 0.0    | 0.9910   |
| No log        | 6.0   | 192  | 0.0427          | 0.0       | 0.0    | 0.0    | 0.9910   |
| No log        | 7.0   | 224  | 0.0330          | 0.7753    | 0.069  | 0.1267 | 0.9913   |
| No log        | 8.0   | 256  | 0.0288          | 0.6309    | 0.376  | 0.4712 | 0.9921   |
| No log        | 9.0   | 288  | 0.0264          | 0.6145    | 0.373  | 0.4642 | 0.9924   |
| No log        | 10.0  | 320  | 0.0251          | 0.5489    | 0.432  | 0.4835 | 0.9926   |
| No log        | 11.0  | 352  | 0.0242          | 0.5354    | 0.484  | 0.5084 | 0.9927   |
| No log        | 12.0  | 384  | 0.0235          | 0.5393    | 0.515  | 0.5269 | 0.9928   |
| No log        | 13.0  | 416  | 0.0230          | 0.5310    | 0.54   | 0.5354 | 0.9929   |
| No log        | 14.0  | 448  | 0.0228          | 0.5287    | 0.553  | 0.5406 | 0.9930   |
| No log        | 15.0  | 480  | 0.0226          | 0.5313    | 0.551  | 0.5410 | 0.9930   |


### Framework versions

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