---
license: mit
base_model: cointegrated/rubert-tiny2
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: rubert-tiny2-odonata-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-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: 0.0048
- Precision: 0.4157
- Recall: 0.3274
- F1: 0.3663
- Accuracy: 0.9985

## 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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 188  | 0.0144          | 0.0       | 0.0    | 0.0    | 0.9985   |
| No log        | 2.0   | 376  | 0.0133          | 0.0       | 0.0    | 0.0    | 0.9985   |
| 0.0582        | 3.0   | 564  | 0.0100          | 0.0       | 0.0    | 0.0    | 0.9985   |
| 0.0582        | 4.0   | 752  | 0.0069          | 0.5       | 0.0177 | 0.0342 | 0.9985   |
| 0.0582        | 5.0   | 940  | 0.0058          | 0.6667    | 0.0177 | 0.0345 | 0.9985   |
| 0.0084        | 6.0   | 1128 | 0.0053          | 0.5       | 0.1593 | 0.2416 | 0.9985   |
| 0.0084        | 7.0   | 1316 | 0.0052          | 0.4487    | 0.3097 | 0.3665 | 0.9985   |
| 0.0057        | 8.0   | 1504 | 0.0049          | 0.4533    | 0.3009 | 0.3617 | 0.9985   |
| 0.0057        | 9.0   | 1692 | 0.0048          | 0.4302    | 0.3274 | 0.3719 | 0.9985   |
| 0.0057        | 10.0  | 1880 | 0.0048          | 0.4157    | 0.3274 | 0.3663 | 0.9985   |


### Framework versions

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