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