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---
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license: mit
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base_model: cointegrated/rubert-tiny2
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tags:
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- generated_from_trainer
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metrics:
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- precision
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- recall
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- f1
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- accuracy
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model-index:
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- name: rubert-tiny2-odonata-extended-305-1-ner
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# rubert-tiny2-odonata-extended-305-1-ner
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This model is a fine-tuned version of [cointegrated/rubert-tiny2](https://huggingface.co/cointegrated/rubert-tiny2) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.0171
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- Precision: 0.5782
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- Recall: 0.6289
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- F1: 0.6025
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- Accuracy: 0.9940
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 2e-05
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- train_batch_size: 16
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- eval_batch_size: 16
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- seed: 42
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- num_epochs: 30
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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| No log | 1.0 | 25 | 0.2514 | 0.0 | 0.0 | 0.0 | 0.9913 |
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| No log | 2.0 | 50 | 0.0660 | 0.0 | 0.0 | 0.0 | 0.9913 |
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| No log | 3.0 | 75 | 0.0579 | 0.0 | 0.0 | 0.0 | 0.9913 |
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| No log | 4.0 | 100 | 0.0557 | 0.0 | 0.0 | 0.0 | 0.9913 |
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| No log | 5.0 | 125 | 0.0528 | 0.0 | 0.0 | 0.0 | 0.9913 |
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| No log | 6.0 | 150 | 0.0465 | 0.0 | 0.0 | 0.0 | 0.9913 |
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| No log | 7.0 | 175 | 0.0359 | 1.0 | 0.0052 | 0.0103 | 0.9914 |
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| No log | 8.0 | 200 | 0.0278 | 0.5802 | 0.3918 | 0.4677 | 0.9921 |
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| No log | 9.0 | 225 | 0.0241 | 0.5940 | 0.4072 | 0.4832 | 0.9922 |
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| No log | 10.0 | 250 | 0.0223 | 0.6 | 0.4175 | 0.4924 | 0.9925 |
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| No log | 11.0 | 275 | 0.0212 | 0.5417 | 0.4691 | 0.5028 | 0.9930 |
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| No log | 12.0 | 300 | 0.0204 | 0.52 | 0.4691 | 0.4932 | 0.9931 |
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| No log | 13.0 | 325 | 0.0199 | 0.5579 | 0.5464 | 0.5521 | 0.9936 |
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| No log | 14.0 | 350 | 0.0194 | 0.5761 | 0.5464 | 0.5608 | 0.9939 |
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| No log | 15.0 | 375 | 0.0190 | 0.5761 | 0.5464 | 0.5608 | 0.9938 |
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| No log | 16.0 | 400 | 0.0187 | 0.5670 | 0.5670 | 0.5670 | 0.9939 |
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| No log | 17.0 | 425 | 0.0184 | 0.5685 | 0.5773 | 0.5729 | 0.9938 |
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| No log | 18.0 | 450 | 0.0182 | 0.5707 | 0.6031 | 0.5865 | 0.9939 |
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| No log | 19.0 | 475 | 0.0180 | 0.5680 | 0.6031 | 0.5850 | 0.9940 |
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| 0.0748 | 20.0 | 500 | 0.0177 | 0.5764 | 0.6031 | 0.5894 | 0.9941 |
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| 0.0748 | 21.0 | 525 | 0.0176 | 0.5907 | 0.5876 | 0.5891 | 0.9941 |
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| 0.0748 | 22.0 | 550 | 0.0176 | 0.5769 | 0.6186 | 0.5970 | 0.9941 |
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| 0.0748 | 23.0 | 575 | 0.0174 | 0.5939 | 0.6031 | 0.5985 | 0.9942 |
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| 0.0748 | 24.0 | 600 | 0.0173 | 0.5854 | 0.6186 | 0.6015 | 0.9942 |
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| 0.0748 | 25.0 | 625 | 0.0172 | 0.5902 | 0.6237 | 0.6065 | 0.9942 |
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| 0.0748 | 26.0 | 650 | 0.0172 | 0.5865 | 0.6289 | 0.6070 | 0.9942 |
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| 0.0748 | 27.0 | 675 | 0.0171 | 0.5862 | 0.6134 | 0.5995 | 0.9941 |
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| 0.0748 | 28.0 | 700 | 0.0171 | 0.5882 | 0.6186 | 0.6030 | 0.9942 |
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| 0.0748 | 29.0 | 725 | 0.0171 | 0.5817 | 0.6237 | 0.6020 | 0.9941 |
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| 0.0748 | 30.0 | 750 | 0.0171 | 0.5782 | 0.6289 | 0.6025 | 0.9940 |
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### Framework versions
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- Transformers 4.41.2
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- Pytorch 2.3.1+cpu
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- Datasets 2.19.2
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- Tokenizers 0.19.1
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