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---
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
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