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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-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: 0.0063
- Precision: 0.7067
- Recall: 0.7681
- F1: 0.7361
- Accuracy: 0.9981
## 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 | 32 | 0.1795 | 0.0 | 0.0 | 0.0 | 0.9961 |
| No log | 2.0 | 64 | 0.0386 | 0.0 | 0.0 | 0.0 | 0.9961 |
| No log | 3.0 | 96 | 0.0316 | 0.0 | 0.0 | 0.0 | 0.9961 |
| No log | 4.0 | 128 | 0.0292 | 0.0 | 0.0 | 0.0 | 0.9961 |
| No log | 5.0 | 160 | 0.0231 | 0.0 | 0.0 | 0.0 | 0.9961 |
| No log | 6.0 | 192 | 0.0152 | 0.6923 | 0.1304 | 0.2195 | 0.9964 |
| No log | 7.0 | 224 | 0.0123 | 0.6212 | 0.5942 | 0.6074 | 0.9971 |
| No log | 8.0 | 256 | 0.0108 | 0.5946 | 0.6377 | 0.6154 | 0.9970 |
| No log | 9.0 | 288 | 0.0099 | 0.6269 | 0.6087 | 0.6176 | 0.9972 |
| No log | 10.0 | 320 | 0.0092 | 0.5921 | 0.6522 | 0.6207 | 0.9971 |
| No log | 11.0 | 352 | 0.0087 | 0.6267 | 0.6812 | 0.6528 | 0.9974 |
| No log | 12.0 | 384 | 0.0083 | 0.65 | 0.7536 | 0.6980 | 0.9977 |
| No log | 13.0 | 416 | 0.0079 | 0.6456 | 0.7391 | 0.6892 | 0.9976 |
| No log | 14.0 | 448 | 0.0076 | 0.6375 | 0.7391 | 0.6846 | 0.9977 |
| No log | 15.0 | 480 | 0.0074 | 0.6667 | 0.7826 | 0.72 | 0.9979 |
| 0.0795 | 16.0 | 512 | 0.0072 | 0.6933 | 0.7536 | 0.7222 | 0.9980 |
| 0.0795 | 17.0 | 544 | 0.0071 | 0.6420 | 0.7536 | 0.6933 | 0.9978 |
| 0.0795 | 18.0 | 576 | 0.0069 | 0.6806 | 0.7101 | 0.6950 | 0.9979 |
| 0.0795 | 19.0 | 608 | 0.0068 | 0.68 | 0.7391 | 0.7083 | 0.9980 |
| 0.0795 | 20.0 | 640 | 0.0067 | 0.68 | 0.7391 | 0.7083 | 0.9980 |
| 0.0795 | 21.0 | 672 | 0.0066 | 0.6842 | 0.7536 | 0.7172 | 0.9980 |
| 0.0795 | 22.0 | 704 | 0.0065 | 0.6933 | 0.7536 | 0.7222 | 0.9980 |
| 0.0795 | 23.0 | 736 | 0.0065 | 0.6849 | 0.7246 | 0.7042 | 0.9980 |
| 0.0795 | 24.0 | 768 | 0.0064 | 0.7027 | 0.7536 | 0.7273 | 0.9981 |
| 0.0795 | 25.0 | 800 | 0.0063 | 0.7027 | 0.7536 | 0.7273 | 0.9981 |
| 0.0795 | 26.0 | 832 | 0.0063 | 0.7162 | 0.7681 | 0.7413 | 0.9981 |
| 0.0795 | 27.0 | 864 | 0.0063 | 0.7162 | 0.7681 | 0.7413 | 0.9981 |
| 0.0795 | 28.0 | 896 | 0.0063 | 0.7027 | 0.7536 | 0.7273 | 0.9981 |
| 0.0795 | 29.0 | 928 | 0.0063 | 0.7067 | 0.7681 | 0.7361 | 0.9981 |
| 0.0795 | 30.0 | 960 | 0.0063 | 0.7067 | 0.7681 | 0.7361 | 0.9981 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.1+cpu
- Datasets 2.19.2
- Tokenizers 0.19.1
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