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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-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-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: 0.0048
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- Precision: 0.4157
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- Recall: 0.3274
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- F1: 0.3663
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- Accuracy: 0.9985
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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: 10
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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 | 188 | 0.0144 | 0.0 | 0.0 | 0.0 | 0.9985 |
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| No log | 2.0 | 376 | 0.0133 | 0.0 | 0.0 | 0.0 | 0.9985 |
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| 0.0582 | 3.0 | 564 | 0.0100 | 0.0 | 0.0 | 0.0 | 0.9985 |
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| 0.0582 | 4.0 | 752 | 0.0069 | 0.5 | 0.0177 | 0.0342 | 0.9985 |
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| 0.0582 | 5.0 | 940 | 0.0058 | 0.6667 | 0.0177 | 0.0345 | 0.9985 |
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| 0.0084 | 6.0 | 1128 | 0.0053 | 0.5 | 0.1593 | 0.2416 | 0.9985 |
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| 0.0084 | 7.0 | 1316 | 0.0052 | 0.4487 | 0.3097 | 0.3665 | 0.9985 |
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| 0.0057 | 8.0 | 1504 | 0.0049 | 0.4533 | 0.3009 | 0.3617 | 0.9985 |
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| 0.0057 | 9.0 | 1692 | 0.0048 | 0.4302 | 0.3274 | 0.3719 | 0.9985 |
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| 0.0057 | 10.0 | 1880 | 0.0048 | 0.4157 | 0.3274 | 0.3663 | 0.9985 |
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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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