---
license: mit
base_model: cointegrated/rubert-tiny2
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: rubert-tiny2-odonata-f3-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-f3-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: 0.0188
- Precision: 0.6653
- Recall: 0.6157
- F1: 0.6395
- Accuracy: 0.9944

## 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: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 32   | 0.1309          | 0.0       | 0.0    | 0.0    | 0.9903   |
| No log        | 2.0   | 64   | 0.0672          | 0.0       | 0.0    | 0.0    | 0.9903   |
| No log        | 3.0   | 96   | 0.0623          | 0.0       | 0.0    | 0.0    | 0.9903   |
| No log        | 4.0   | 128  | 0.0576          | 0.0       | 0.0    | 0.0    | 0.9903   |
| No log        | 5.0   | 160  | 0.0488          | 0.0       | 0.0    | 0.0    | 0.9903   |
| No log        | 6.0   | 192  | 0.0353          | 0.0       | 0.0    | 0.0    | 0.9903   |
| No log        | 7.0   | 224  | 0.0288          | 0.7921    | 0.5529 | 0.6513 | 0.9935   |
| No log        | 8.0   | 256  | 0.0256          | 0.7987    | 0.4824 | 0.6015 | 0.9931   |
| No log        | 9.0   | 288  | 0.0235          | 0.7975    | 0.5098 | 0.6220 | 0.9933   |
| No log        | 10.0  | 320  | 0.0221          | 0.7310    | 0.5647 | 0.6372 | 0.9938   |
| No log        | 11.0  | 352  | 0.0212          | 0.6912    | 0.5529 | 0.6144 | 0.9938   |
| No log        | 12.0  | 384  | 0.0205          | 0.6746    | 0.5529 | 0.6078 | 0.9937   |
| No log        | 13.0  | 416  | 0.0201          | 0.6774    | 0.5765 | 0.6229 | 0.9938   |
| No log        | 14.0  | 448  | 0.0196          | 0.6712    | 0.5843 | 0.6247 | 0.9940   |
| No log        | 15.0  | 480  | 0.0194          | 0.6581    | 0.6039 | 0.6299 | 0.9941   |
| 0.0722        | 16.0  | 512  | 0.0192          | 0.6681    | 0.6    | 0.6322 | 0.9942   |
| 0.0722        | 17.0  | 544  | 0.0190          | 0.6624    | 0.6078 | 0.6339 | 0.9943   |
| 0.0722        | 18.0  | 576  | 0.0189          | 0.6542    | 0.6157 | 0.6343 | 0.9943   |
| 0.0722        | 19.0  | 608  | 0.0188          | 0.6624    | 0.6157 | 0.6382 | 0.9944   |
| 0.0722        | 20.0  | 640  | 0.0188          | 0.6653    | 0.6157 | 0.6395 | 0.9944   |


### Framework versions

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