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update model card README.md
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README.md
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---
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license: mit
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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: userutterance_classification_verplus
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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: 0.9561290322580646
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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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# userutterance_classification_verplus
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This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the clinc_oos dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.2190
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- Accuracy: 0.9561
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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: 32
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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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- lr_scheduler_warmup_steps: 130
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- num_epochs: 5
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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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| 4.94 | 0.42 | 200 | 4.4744 | 0.1697 |
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| 3.8508 | 0.84 | 400 | 2.8790 | 0.6584 |
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| 2.5616 | 1.26 | 600 | 1.6906 | 0.8681 |
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| 1.59 | 1.68 | 800 | 0.9367 | 0.9152 |
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| 0.9566 | 2.1 | 1000 | 0.5392 | 0.9348 |
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| 0.5608 | 2.52 | 1200 | 0.3762 | 0.9465 |
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| 0.3678 | 2.94 | 1400 | 0.3008 | 0.9477 |
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| 0.2413 | 3.35 | 1600 | 0.2625 | 0.9497 |
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| 0.1837 | 3.77 | 1800 | 0.2367 | 0.9529 |
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| 0.136 | 4.19 | 2000 | 0.2193 | 0.9565 |
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| 0.1078 | 4.61 | 2200 | 0.2190 | 0.9561 |
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### Framework versions
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- Transformers 4.28.0
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- Pytorch 2.0.1+cu118
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- Datasets 2.12.0
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- Tokenizers 0.13.3
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