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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: 0.9619354838709677 |
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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: 0.2270 |
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- Accuracy: 0.9619 |
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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: 16 |
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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: 500 |
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- num_epochs: 6 |
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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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| 5.0219 | 0.21 | 200 | 4.9813 | 0.0077 | |
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| 4.8915 | 0.42 | 400 | 4.5741 | 0.1155 | |
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| 4.2736 | 0.63 | 600 | 3.5359 | 0.4719 | |
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| 3.2701 | 0.84 | 800 | 2.4291 | 0.7429 | |
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| 2.3578 | 1.05 | 1000 | 1.5793 | 0.8413 | |
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| 1.5695 | 1.26 | 1200 | 1.0029 | 0.8994 | |
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| 1.0412 | 1.47 | 1400 | 0.6475 | 0.9187 | |
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| 0.7034 | 1.68 | 1600 | 0.4439 | 0.9303 | |
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| 0.501 | 1.89 | 1800 | 0.3400 | 0.9381 | |
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| 0.3187 | 2.1 | 2000 | 0.2793 | 0.9439 | |
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| 0.2185 | 2.31 | 2200 | 0.2538 | 0.9490 | |
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| 0.1669 | 2.52 | 2400 | 0.2210 | 0.9523 | |
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| 0.1081 | 2.73 | 2600 | 0.2225 | 0.9519 | |
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| 0.1004 | 2.94 | 2800 | 0.2136 | 0.9555 | |
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| 0.0665 | 3.14 | 3000 | 0.2078 | 0.9561 | |
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| 0.0509 | 3.35 | 3200 | 0.2155 | 0.9568 | |
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| 0.05 | 3.56 | 3400 | 0.2107 | 0.9581 | |
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| 0.0527 | 3.77 | 3600 | 0.2171 | 0.9568 | |
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| 0.0447 | 3.98 | 3800 | 0.2128 | 0.9590 | |
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| 0.0259 | 4.19 | 4000 | 0.2099 | 0.9587 | |
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| 0.0279 | 4.4 | 4200 | 0.2179 | 0.9577 | |
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| 0.0176 | 4.61 | 4400 | 0.2191 | 0.9574 | |
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| 0.0288 | 4.82 | 4600 | 0.2216 | 0.9590 | |
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| 0.0328 | 5.03 | 4800 | 0.2237 | 0.9606 | |
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| 0.0154 | 5.24 | 5000 | 0.2241 | 0.9616 | |
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| 0.0157 | 5.45 | 5200 | 0.2265 | 0.9603 | |
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| 0.023 | 5.66 | 5400 | 0.2276 | 0.9613 | |
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| 0.0178 | 5.87 | 5600 | 0.2270 | 0.9619 | |
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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 2.13.1 |
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- Tokenizers 0.13.3 |
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