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--- |
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license: mit |
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base_model: Microsoft/Multilingual-MiniLM-L12-H384 |
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tags: |
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- generated_from_trainer |
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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: 1.3835 |
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- Accuracy: 0.4748 |
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- F1: 0.4280 |
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- Precision: 0.4147 |
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- Recall: 0.4748 |
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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: 8 |
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- eval_batch_size: 8 |
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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 | Accuracy | F1 | Precision | Recall | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
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| 1.8171 | 1.0 | 81 | 1.7710 | 0.2302 | 0.0862 | 0.0530 | 0.2302 | |
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| 1.7264 | 2.0 | 162 | 1.6322 | 0.4101 | 0.2695 | 0.2007 | 0.4101 | |
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| 1.5603 | 3.0 | 243 | 1.5425 | 0.3885 | 0.2544 | 0.1943 | 0.3885 | |
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| 1.4237 | 4.0 | 324 | 1.5997 | 0.4317 | 0.3424 | 0.2883 | 0.4317 | |
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| 1.2819 | 5.0 | 405 | 1.5824 | 0.4101 | 0.3260 | 0.2763 | 0.4101 | |
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| 1.1493 | 6.0 | 486 | 1.4762 | 0.4460 | 0.3670 | 0.4151 | 0.4460 | |
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| 1.0688 | 7.0 | 567 | 1.4088 | 0.4748 | 0.4279 | 0.4418 | 0.4748 | |
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| 0.9746 | 8.0 | 648 | 1.4380 | 0.4604 | 0.3957 | 0.3835 | 0.4604 | |
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| 0.9039 | 9.0 | 729 | 1.4078 | 0.4748 | 0.4278 | 0.4147 | 0.4748 | |
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| 0.8577 | 10.0 | 810 | 1.3835 | 0.4748 | 0.4280 | 0.4147 | 0.4748 | |
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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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