---
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: 0.0094
- Precision: 0.6364
- Recall: 0.7101
- F1: 0.6712
- Accuracy: 0.9973

## 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   | 0.1433          | 0.0       | 0.0    | 0.0    | 0.9961   |
| No log        | 2.0   | 64   | 0.0364          | 0.0       | 0.0    | 0.0    | 0.9961   |
| No log        | 3.0   | 96   | 0.0310          | 0.0       | 0.0    | 0.0    | 0.9961   |
| No log        | 4.0   | 128  | 0.0286          | 0.0       | 0.0    | 0.0    | 0.9961   |
| No log        | 5.0   | 160  | 0.0250          | 0.0       | 0.0    | 0.0    | 0.9961   |
| No log        | 6.0   | 192  | 0.0183          | 0.6667    | 0.0290 | 0.0556 | 0.9962   |
| No log        | 7.0   | 224  | 0.0141          | 0.5581    | 0.3478 | 0.4286 | 0.9965   |
| No log        | 8.0   | 256  | 0.0122          | 0.6111    | 0.4783 | 0.5366 | 0.9969   |
| No log        | 9.0   | 288  | 0.0111          | 0.6792    | 0.5217 | 0.5902 | 0.9971   |
| No log        | 10.0  | 320  | 0.0105          | 0.6154    | 0.5797 | 0.5970 | 0.9970   |
| No log        | 11.0  | 352  | 0.0101          | 0.5857    | 0.5942 | 0.5899 | 0.9971   |
| No log        | 12.0  | 384  | 0.0097          | 0.6143    | 0.6232 | 0.6187 | 0.9972   |
| No log        | 13.0  | 416  | 0.0096          | 0.6203    | 0.7101 | 0.6622 | 0.9972   |
| No log        | 14.0  | 448  | 0.0094          | 0.6282    | 0.7101 | 0.6667 | 0.9973   |
| No log        | 15.0  | 480  | 0.0094          | 0.6364    | 0.7101 | 0.6712 | 0.9973   |


### Framework versions

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