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--- |
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tags: |
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- generated_from_trainer |
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datasets: |
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- glue |
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metrics: |
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- accuracy |
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model-index: |
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- name: tiny-bert-mnli-distilled |
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results: |
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- task: |
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name: Text Classification |
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type: text-classification |
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dataset: |
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name: glue |
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type: glue |
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args: mnli |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.5818644931227712 |
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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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# tiny-bert-mnli-distilled |
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This model is a fine-tuned version of [M-FAC/bert-mini-finetuned-mnli](https://huggingface.co/M-FAC/bert-mini-finetuned-mnli) on the glue dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.5018 |
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- Accuracy: 0.5819 |
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- F1 score: 0.5782 |
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- Precision score: 0.6036 |
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- Metric recall: 0.5819 |
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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: 0.0005 |
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- train_batch_size: 64 |
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- eval_batch_size: 32 |
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- seed: 33 |
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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: 4 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 score | Precision score | Metric recall | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:---------------:|:-------------:| |
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| 1.4475 | 1.0 | 614 | 1.4296 | 0.4521 | 0.4070 | 0.5621 | 0.4521 | |
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| 1.3354 | 2.0 | 1228 | 1.4320 | 0.4805 | 0.4579 | 0.5276 | 0.4805 | |
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| 1.2244 | 3.0 | 1842 | 1.4786 | 0.5699 | 0.5602 | 0.5865 | 0.5699 | |
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| 1.1416 | 4.0 | 2456 | 1.5018 | 0.5819 | 0.5782 | 0.6036 | 0.5819 | |
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### Framework versions |
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- Transformers 4.18.0 |
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- Pytorch 1.9.1 |
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- Datasets 2.1.0 |
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- Tokenizers 0.11.6 |
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