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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: HsscBERT_abs_and_full |
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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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# HsscBERT_abs_and_full |
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This model is a fine-tuned version of [/home/hscrc/pretrained_models/bert-base-chinese](https://huggingface.co//home/hscrc/pretrained_models/bert-base-chinese) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6037 |
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- Accuracy: 0.8504 |
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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: 2e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- num_devices: 9 |
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- total_train_batch_size: 288 |
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- total_eval_batch_size: 144 |
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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: 5.0 |
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### Training results |
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| Training Loss | Epoch | Step | Accuracy | Validation Loss | |
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|:-------------:|:-----:|:-----:|:--------:|:---------------:| |
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| 0.8163 | 0.19 | 5000 | 0.8326 | 0.6971 | |
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| 0.7942 | 0.38 | 10000 | 0.8364 | 0.6761 | |
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| 0.7817 | 0.57 | 15000 | 0.8384 | 0.6651 | |
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| 0.7751 | 0.75 | 20000 | 0.8402 | 0.6563 | |
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| 0.7654 | 0.94 | 25000 | 0.8415 | 0.6490 | |
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| 0.7546 | 1.13 | 30000 | 0.8427 | 0.6441 | |
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| 0.7527 | 1.32 | 35000 | 0.8434 | 0.6398 | |
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| 0.7484 | 1.51 | 40000 | 0.8444 | 0.6345 | |
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| 0.7443 | 1.7 | 45000 | 0.8450 | 0.6318 | |
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| 0.74 | 1.88 | 50000 | 0.8456 | 0.6292 | |
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| 0.738 | 2.07 | 55000 | 0.8460 | 0.6268 | |
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| 0.734 | 2.26 | 60000 | 0.8464 | 0.6246 | |
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| 0.7335 | 2.45 | 65000 | 0.8467 | 0.6229 | |
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| 0.7299 | 2.64 | 70000 | 0.8470 | 0.6212 | |
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| 0.7291 | 2.83 | 75000 | 0.8473 | 0.6201 | |
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### Framework versions |
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- Transformers 4.26.1 |
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- Pytorch 1.10.0+cu113 |
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- Datasets 2.9.0 |
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- Tokenizers 0.13.2 |
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