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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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- clinc_oos |
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metrics: |
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- accuracy |
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model-index: |
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- name: MiniLMv2-L12-H384-distilled-finetuned-clinc |
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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: clinc_oos |
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type: clinc_oos |
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args: plus |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.9529032258064516 |
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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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# MiniLMv2-L12-H384-distilled-finetuned-clinc |
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This model is a fine-tuned version of [nreimers/MiniLMv2-L12-H384-distilled-from-RoBERTa-Large](https://huggingface.co/nreimers/MiniLMv2-L12-H384-distilled-from-RoBERTa-Large) on the clinc_oos dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3058 |
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- Accuracy: 0.9529 |
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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: 64 |
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- eval_batch_size: 64 |
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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: 10 |
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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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| 1.9908 | 1.0 | 239 | 1.6816 | 0.3910 | |
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| 1.5212 | 2.0 | 478 | 1.2365 | 0.7697 | |
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| 1.129 | 3.0 | 717 | 0.9209 | 0.8706 | |
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| 0.8462 | 4.0 | 956 | 0.6978 | 0.9152 | |
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| 0.6497 | 5.0 | 1195 | 0.5499 | 0.9342 | |
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| 0.5124 | 6.0 | 1434 | 0.4447 | 0.9445 | |
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| 0.4196 | 7.0 | 1673 | 0.3797 | 0.9455 | |
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| 0.3587 | 8.0 | 1912 | 0.3358 | 0.95 | |
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| 0.3228 | 9.0 | 2151 | 0.3133 | 0.9513 | |
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| 0.3052 | 10.0 | 2390 | 0.3058 | 0.9529 | |
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### Framework versions |
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- Transformers 4.17.0 |
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- Pytorch 1.10.2+cu113 |
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- Datasets 1.18.4 |
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- Tokenizers 0.13.0 |
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