Akira10
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distilbert-base-uncased-distilled-clinc

Text Classification

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Akira10 commited on May 31, 2023

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update model card README.md

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@@ -21,7 +21,7 @@ model-index:
     metrics:
     - name: Accuracy
       type: accuracy
-      value: 0.9464516129032258
 ---
 <!-- 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. -->
 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.2484
-- Accuracy: 0.9465
 ## Model description
@@ -63,16 +63,16 @@ The following hyperparameters were used during training:
 | Training Loss | Epoch | Step | Validation Loss | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:--------:|
-| 4.2545        | 1.0   | 318  | 3.1770          | 0.7516   |
-| 2.4158        | 2.0   | 636  | 1.5651          | 0.8629   |
-| 1.1628        | 3.0   | 954  | 0.7776          | 0.9081   |
-| 0.567         | 4.0   | 1272 | 0.4626          | 0.9339   |
-| 0.2987        | 5.0   | 1590 | 0.3368          | 0.9423   |
-| 0.176         | 6.0   | 1908 | 0.2865          | 0.9423   |
-| 0.1145        | 7.0   | 2226 | 0.2660          | 0.9455   |
-| 0.0848        | 8.0   | 2544 | 0.2509          | 0.9461   |
-| 0.0694        | 9.0   | 2862 | 0.2504          | 0.9458   |
-| 0.0626        | 10.0  | 3180 | 0.2484          | 0.9465   |
 ### Framework versions

     metrics:
     - name: Accuracy
       type: accuracy
+      value: 0.9438709677419355
 ---
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset.
 It achieves the following results on the evaluation set:
+- Loss: 0.2192
+- Accuracy: 0.9439
 ## Model description
 | Training Loss | Epoch | Step | Validation Loss | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:--------:|
+| 1.8173        | 1.0   | 318  | 1.2393          | 0.7426   |
+| 0.9618        | 2.0   | 636  | 0.6148          | 0.8590   |
+| 0.5073        | 3.0   | 954  | 0.3621          | 0.9158   |
+| 0.3189        | 4.0   | 1272 | 0.2748          | 0.9319   |
+| 0.2442        | 5.0   | 1590 | 0.2454          | 0.9394   |
+| 0.2143        | 6.0   | 1908 | 0.2330          | 0.9419   |
+| 0.1987        | 7.0   | 2226 | 0.2258          | 0.9432   |
+| 0.1905        | 8.0   | 2544 | 0.2218          | 0.9442   |
+| 0.1861        | 9.0   | 2862 | 0.2201          | 0.9439   |
+| 0.1836        | 10.0  | 3180 | 0.2192          | 0.9439   |
 ### Framework versions