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--- |
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license: mit |
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tags: |
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- generated_from_trainer |
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base_model: Microsoft/Multilingual-MiniLM-L12-H384 |
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metrics: |
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- accuracy |
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- f1 |
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- precision |
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- recall |
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model-index: |
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- name: my-model-MiniLM-Area |
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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-model-MiniLM-Area |
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This model is a fine-tuned version of [Microsoft/Multilingual-MiniLM-L12-H384](https://huggingface.co/Microsoft/Multilingual-MiniLM-L12-H384) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.5228 |
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- Accuracy: 0.4323 |
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- F1: 0.3979 |
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- Precision: 0.3932 |
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- Recall: 0.4323 |
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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: 5e-05 |
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- train_batch_size: 30 |
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- eval_batch_size: 5 |
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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 | Accuracy | F1 | Precision | Recall | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
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| 1.8812 | 1.0 | 25 | 1.8038 | 0.2839 | 0.1709 | 0.2712 | 0.2839 | |
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| 1.8043 | 2.0 | 50 | 1.7540 | 0.3742 | 0.2586 | 0.2046 | 0.3742 | |
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| 1.7687 | 3.0 | 75 | 1.6908 | 0.3806 | 0.2557 | 0.1927 | 0.3806 | |
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| 1.6959 | 4.0 | 100 | 1.6325 | 0.4 | 0.2695 | 0.2033 | 0.4 | |
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| 1.6178 | 5.0 | 125 | 1.6401 | 0.4129 | 0.3338 | 0.2874 | 0.4129 | |
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| 1.5189 | 6.0 | 150 | 1.5471 | 0.4581 | 0.3631 | 0.3030 | 0.4581 | |
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| 1.4393 | 7.0 | 175 | 1.5966 | 0.4258 | 0.3761 | 0.3451 | 0.4258 | |
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| 1.3757 | 8.0 | 200 | 1.5716 | 0.4452 | 0.3945 | 0.3556 | 0.4452 | |
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| 1.3032 | 9.0 | 225 | 1.5691 | 0.4387 | 0.3646 | 0.3443 | 0.4387 | |
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| 1.2434 | 10.0 | 250 | 1.5740 | 0.4452 | 0.4057 | 0.3798 | 0.4452 | |
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| 1.1837 | 11.0 | 275 | 1.5108 | 0.4645 | 0.3854 | 0.3852 | 0.4645 | |
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| 1.1231 | 12.0 | 300 | 1.5409 | 0.4516 | 0.3972 | 0.3561 | 0.4516 | |
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| 1.0815 | 13.0 | 325 | 1.5111 | 0.4774 | 0.4116 | 0.3865 | 0.4774 | |
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| 1.0555 | 14.0 | 350 | 1.5171 | 0.4645 | 0.4014 | 0.3674 | 0.4645 | |
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| 0.9964 | 15.0 | 375 | 1.4971 | 0.4581 | 0.3877 | 0.3504 | 0.4581 | |
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| 0.9627 | 16.0 | 400 | 1.5157 | 0.4516 | 0.4118 | 0.3882 | 0.4516 | |
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| 0.9247 | 17.0 | 425 | 1.4996 | 0.4387 | 0.3882 | 0.3664 | 0.4387 | |
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| 0.9286 | 18.0 | 450 | 1.4990 | 0.4452 | 0.4008 | 0.3856 | 0.4452 | |
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| 0.892 | 19.0 | 475 | 1.5288 | 0.4323 | 0.4025 | 0.4031 | 0.4323 | |
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| 0.8843 | 20.0 | 500 | 1.5228 | 0.4323 | 0.3979 | 0.3932 | 0.4323 | |
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### Framework versions |
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- Transformers 4.41.0 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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