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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-f3-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-f3-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: 0.0188
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- Precision: 0.6653
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- Recall: 0.6157
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- F1: 0.6395
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- Accuracy: 0.9944
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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: 20
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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 | 32 | 0.1309 | 0.0 | 0.0 | 0.0 | 0.9903 |
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| No log | 2.0 | 64 | 0.0672 | 0.0 | 0.0 | 0.0 | 0.9903 |
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| No log | 3.0 | 96 | 0.0623 | 0.0 | 0.0 | 0.0 | 0.9903 |
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| No log | 4.0 | 128 | 0.0576 | 0.0 | 0.0 | 0.0 | 0.9903 |
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| No log | 5.0 | 160 | 0.0488 | 0.0 | 0.0 | 0.0 | 0.9903 |
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| No log | 6.0 | 192 | 0.0353 | 0.0 | 0.0 | 0.0 | 0.9903 |
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| No log | 7.0 | 224 | 0.0288 | 0.7921 | 0.5529 | 0.6513 | 0.9935 |
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| No log | 8.0 | 256 | 0.0256 | 0.7987 | 0.4824 | 0.6015 | 0.9931 |
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| No log | 9.0 | 288 | 0.0235 | 0.7975 | 0.5098 | 0.6220 | 0.9933 |
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| No log | 10.0 | 320 | 0.0221 | 0.7310 | 0.5647 | 0.6372 | 0.9938 |
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| No log | 11.0 | 352 | 0.0212 | 0.6912 | 0.5529 | 0.6144 | 0.9938 |
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| No log | 12.0 | 384 | 0.0205 | 0.6746 | 0.5529 | 0.6078 | 0.9937 |
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| No log | 13.0 | 416 | 0.0201 | 0.6774 | 0.5765 | 0.6229 | 0.9938 |
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| No log | 14.0 | 448 | 0.0196 | 0.6712 | 0.5843 | 0.6247 | 0.9940 |
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| No log | 15.0 | 480 | 0.0194 | 0.6581 | 0.6039 | 0.6299 | 0.9941 |
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| 0.0722 | 16.0 | 512 | 0.0192 | 0.6681 | 0.6 | 0.6322 | 0.9942 |
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| 0.0722 | 17.0 | 544 | 0.0190 | 0.6624 | 0.6078 | 0.6339 | 0.9943 |
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| 0.0722 | 18.0 | 576 | 0.0189 | 0.6542 | 0.6157 | 0.6343 | 0.9943 |
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| 0.0722 | 19.0 | 608 | 0.0188 | 0.6624 | 0.6157 | 0.6382 | 0.9944 |
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| 0.0722 | 20.0 | 640 | 0.0188 | 0.6653 | 0.6157 | 0.6395 | 0.9944 |
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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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