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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-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
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