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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-UnidicUnigram |
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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-UnidicUnigram |
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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: 1.1279 |
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- Accuracy: 0.7455 |
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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: 14.0 |
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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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| 1.5872 | 1.0 | 69473 | 1.4531 | 0.6867 | |
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| 1.4695 | 2.0 | 138946 | 1.3340 | 0.7073 | |
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| 1.4136 | 3.0 | 208419 | 1.2793 | 0.7173 | |
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| 1.3779 | 4.0 | 277892 | 1.2490 | 0.7227 | |
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| 1.3546 | 5.0 | 347365 | 1.2227 | 0.7277 | |
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| 1.3353 | 6.0 | 416838 | 1.2070 | 0.7307 | |
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| 1.3182 | 7.0 | 486311 | 1.1895 | 0.7334 | |
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| 1.3058 | 8.0 | 555784 | 1.1777 | 0.7360 | |
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| 1.2974 | 9.0 | 625257 | 1.1660 | 0.7378 | |
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| 1.2857 | 10.0 | 694730 | 1.1543 | 0.7401 | |
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| 1.2755 | 11.0 | 764203 | 1.1514 | 0.7408 | |
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| 1.2694 | 12.0 | 833676 | 1.1377 | 0.7431 | |
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| 1.2623 | 13.0 | 903149 | 1.1338 | 0.7442 | |
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| 1.2587 | 14.0 | 972622 | 1.1279 | 0.7455 | |
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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 2.9.0 |
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- Tokenizers 0.12.1 |
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