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--- |
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license: mit |
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base_model: microsoft/xtremedistil-l6-h384-uncased |
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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: xtremedistil-l6-h384-uncased-v4.0 |
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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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# xtremedistil-l6-h384-uncased-v4.0 |
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This model is a fine-tuned version of [microsoft/xtremedistil-l6-h384-uncased](https://huggingface.co/microsoft/xtremedistil-l6-h384-uncased) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5570 |
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- F1 Macro: 0.6744 |
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- F1 Micro: 0.6771 |
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- Accuracy Balanced: 0.6742 |
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- Accuracy: 0.6771 |
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- Precision Macro: 0.6748 |
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- Recall Macro: 0.6742 |
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- Precision Micro: 0.6771 |
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- Recall Micro: 0.6771 |
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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: 16 |
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- eval_batch_size: 128 |
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- seed: 40 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 32 |
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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: 0.06 |
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- num_epochs: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | F1 Macro | F1 Micro | Accuracy Balanced | Accuracy | Precision Macro | Recall Macro | Precision Micro | Recall Micro | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-----------------:|:--------:|:---------------:|:------------:|:---------------:|:------------:| |
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| 0.5719 | 1.69 | 200 | 0.5779 | 0.6387 | 0.6559 | 0.6420 | 0.6559 | 0.6609 | 0.6420 | 0.6559 | 0.6559 | |
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### eval result |
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|Datasets|asadfgglie/nli-zh-tw-all/test|asadfgglie/BanBan_2024-10-17-facial_expressions-nli/test|eval_dataset|test_dataset| |
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| :---: | :---: | :---: | :---: | :---: | |
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|eval_loss|0.558|0.728|0.56|0.557| |
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|eval_f1_macro|0.676|0.494|0.682|0.674| |
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|eval_f1_micro|0.679|0.531|0.685|0.677| |
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|eval_accuracy_balanced|0.676|0.523|0.682|0.674| |
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|eval_accuracy|0.679|0.531|0.685|0.677| |
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|eval_precision_macro|0.676|0.53|0.682|0.675| |
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|eval_recall_macro|0.676|0.523|0.682|0.674| |
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|eval_precision_micro|0.679|0.531|0.685|0.677| |
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|eval_recall_micro|0.679|0.531|0.685|0.677| |
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|eval_runtime|9.08|0.195|1.746|7.023| |
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|eval_samples_per_second|936.093|4861.442|973.854|968.275| |
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|eval_steps_per_second|7.379|41.112|8.02|7.689| |
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|Size of dataset|8500|946|1700|6800| |
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### Framework versions |
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- Transformers 4.33.3 |
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- Pytorch 2.5.1+cu121 |
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- Datasets 2.14.7 |
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- Tokenizers 0.13.3 |
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