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--- |
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license: apache-2.0 |
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base_model: distilbert-base-uncased |
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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: distilbert-base-uncased-distilled-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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config: plus |
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split: validation |
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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: 0.9490322580645161 |
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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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# distilbert-base-uncased-distilled-clinc |
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co./distilbert-base-uncased) on the clinc_oos dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1852 |
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- Accuracy: 0.9490 |
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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: 0.0004 |
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- train_batch_size: 1280 |
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- eval_batch_size: 1280 |
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- seed: 42 |
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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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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| 1.9692 | 1.0 | 12 | 1.3486 | 0.6574 | |
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| 1.1867 | 2.0 | 24 | 0.5409 | 0.8884 | |
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| 0.5614 | 3.0 | 36 | 0.2845 | 0.9387 | |
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| 0.295 | 4.0 | 48 | 0.2234 | 0.9471 | |
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| 0.1729 | 5.0 | 60 | 0.2021 | 0.9487 | |
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| 0.1574 | 6.0 | 72 | 0.1942 | 0.9513 | |
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| 0.1477 | 7.0 | 84 | 0.1895 | 0.9510 | |
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| 0.1446 | 8.0 | 96 | 0.1870 | 0.9497 | |
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| 0.1405 | 9.0 | 108 | 0.1856 | 0.9494 | |
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| 0.1382 | 10.0 | 120 | 0.1852 | 0.9490 | |
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### Framework versions |
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- Transformers 4.33.1 |
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- Pytorch 2.0.1+cu117 |
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- Datasets 2.14.0 |
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- Tokenizers 0.13.3 |
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