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distilbert-base-uncased-distilled-clinc

Text Classification Transformers PyTorch
distilbert generated_from_trainer Eval Results Inference Endpoints
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update model card README.md

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@@ -16,12 +16,12 @@ model-index:
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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: train
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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: 0.9448387096774193
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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
@@ -31,8 +31,8 @@ should probably proofread and complete it, then remove this comment. -->
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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: 0.1894
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- - Accuracy: 0.9448
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  ## Model description
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@@ -63,21 +63,21 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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- | 1.6133 | 1.0 | 318 | 1.0679 | 0.7290 |
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- | 0.8231 | 2.0 | 636 | 0.5164 | 0.8652 |
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- | 0.4289 | 3.0 | 954 | 0.3019 | 0.9168 |
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- | 0.2722 | 4.0 | 1272 | 0.2336 | 0.9335 |
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- | 0.214 | 5.0 | 1590 | 0.2117 | 0.94 |
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- | 0.1914 | 6.0 | 1908 | 0.2007 | 0.9445 |
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- | 0.1785 | 7.0 | 2226 | 0.1947 | 0.9435 |
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- | 0.1716 | 8.0 | 2544 | 0.1919 | 0.9468 |
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- | 0.1674 | 9.0 | 2862 | 0.1901 | 0.9452 |
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- | 0.1659 | 10.0 | 3180 | 0.1894 | 0.9448 |
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  ### Framework versions
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- - Transformers 4.25.1
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- - Pytorch 1.13.0+cu116
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- - Datasets 2.8.0
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  - Tokenizers 0.13.2
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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: 0.9496774193548387
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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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  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: 0.2961
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+ - Accuracy: 0.9497
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | 3.0996 | 1.0 | 318 | 2.2986 | 0.7529 |
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+ | 1.7705 | 2.0 | 636 | 1.1762 | 0.8635 |
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+ | 0.9169 | 3.0 | 954 | 0.6479 | 0.9197 |
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+ | 0.5201 | 4.0 | 1272 | 0.4447 | 0.9358 |
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+ | 0.3452 | 5.0 | 1590 | 0.3640 | 0.9445 |
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+ | 0.2659 | 6.0 | 1908 | 0.3303 | 0.9455 |
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+ | 0.2245 | 7.0 | 2226 | 0.3105 | 0.9497 |
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+ | 0.2018 | 8.0 | 2544 | 0.3026 | 0.9487 |
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+ | 0.1896 | 9.0 | 2862 | 0.2987 | 0.9484 |
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+ | 0.1849 | 10.0 | 3180 | 0.2961 | 0.9497 |
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  ### Framework versions
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+ - Transformers 4.26.1
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+ - Pytorch 1.13.1+cu116
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+ - Datasets 2.10.1
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  - Tokenizers 0.13.2