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--- |
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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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model-index: |
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- name: bert-small-spm |
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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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# bert-small-spm |
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This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.5919 |
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- Accuracy: 0.5095 |
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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: 0.0001 |
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- train_batch_size: 256 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- num_devices: 3 |
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- total_train_batch_size: 768 |
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- total_eval_batch_size: 24 |
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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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- lr_scheduler_warmup_ratio: 0.01 |
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- num_epochs: 14 |
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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 | |
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|:-------------:|:-----:|:------:|:---------------:|:--------:| |
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| 3.3946 | 1.0 | 69473 | 3.2473 | 0.4299 | |
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| 3.1526 | 2.0 | 138946 | 2.9987 | 0.4583 | |
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| 3.0496 | 3.0 | 208419 | 2.8875 | 0.4715 | |
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| 2.9923 | 4.0 | 277892 | 2.8258 | 0.4788 | |
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| 2.9429 | 5.0 | 347365 | 2.7765 | 0.4849 | |
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| 2.912 | 6.0 | 416838 | 2.7482 | 0.4890 | |
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| 2.8813 | 7.0 | 486311 | 2.7103 | 0.4938 | |
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| 2.8609 | 8.0 | 555784 | 2.6881 | 0.4963 | |
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| 2.8352 | 9.0 | 625257 | 2.6702 | 0.4991 | |
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| 2.8163 | 10.0 | 694730 | 2.6510 | 0.5010 | |
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| 2.8026 | 11.0 | 764203 | 2.6246 | 0.5046 | |
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| 2.7894 | 12.0 | 833676 | 2.6172 | 0.5055 | |
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| 2.7728 | 13.0 | 903149 | 2.5994 | 0.5083 | |
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| 2.761 | 14.0 | 972622 | 2.5919 | 0.5095 | |
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### Framework versions |
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- Transformers 4.19.2 |
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- Pytorch 1.12.0+cu116 |
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- Datasets 2.2.2 |
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- Tokenizers 0.12.1 |
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