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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: my_awesome_wnut_model_ |
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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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# my_awesome_wnut_model_ |
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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: 0.1997 |
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- Precision: 0.3076 |
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- Recall: 0.4690 |
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- F1: 0.3716 |
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- Accuracy: 0.9322 |
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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: 7 |
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- eval_batch_size: 7 |
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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: 24 |
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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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| 0.621 | 2.41 | 140 | 0.4025 | 0.0 | 0.0 | 0.0 | 0.9050 | |
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| 0.3224 | 4.83 | 280 | 0.2750 | 0.2036 | 0.2074 | 0.2055 | 0.9118 | |
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| 0.2421 | 7.24 | 420 | 0.2326 | 0.2706 | 0.3406 | 0.3016 | 0.9220 | |
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| 0.2061 | 9.66 | 560 | 0.2146 | 0.2968 | 0.4102 | 0.3444 | 0.9269 | |
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| 0.1779 | 12.07 | 700 | 0.2037 | 0.3125 | 0.4257 | 0.3604 | 0.9306 | |
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| 0.1606 | 14.48 | 840 | 0.2042 | 0.3044 | 0.4613 | 0.3668 | 0.9298 | |
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| 0.1544 | 16.9 | 980 | 0.2001 | 0.3101 | 0.4690 | 0.3734 | 0.9310 | |
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| 0.1402 | 19.31 | 1120 | 0.1991 | 0.3130 | 0.4690 | 0.3755 | 0.9316 | |
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| 0.139 | 21.72 | 1260 | 0.1997 | 0.3076 | 0.4690 | 0.3716 | 0.9322 | |
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### Framework versions |
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- Transformers 4.38.2 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.19.0 |
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- Tokenizers 0.15.2 |
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