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distilbert-finetuned-gesture-prediction-21-classes

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qfrodicio commited on Mar 10, 2023

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@@ -19,11 +19,11 @@ should probably proofread and complete it, then remove this comment. -->
 This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the None dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.9212
-- Accuracy: 0.8029
-- Precision: 0.7978
-- Recall: 0.8029
-- F1: 0.7974
 ## Model description
@@ -54,16 +54,16 @@ The following hyperparameters were used during training:
 | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
 |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
-| 2.1942        | 1.0   | 104  | 1.3566          | 0.7113   | 0.6581    | 0.7113 | 0.6683 |
-| 1.1713        | 2.0   | 208  | 1.0182          | 0.7626   | 0.7445    | 0.7626 | 0.7348 |
-| 0.8285        | 3.0   | 312  | 0.9028          | 0.7789   | 0.7626    | 0.7789 | 0.7607 |
-| 0.6374        | 4.0   | 416  | 0.8845          | 0.7959   | 0.7950    | 0.7959 | 0.7890 |
-| 0.4991        | 5.0   | 520  | 0.8936          | 0.7997   | 0.7949    | 0.7997 | 0.7865 |
-| 0.3908        | 6.0   | 624  | 0.8829          | 0.7910   | 0.7879    | 0.7910 | 0.7864 |
-| 0.3234        | 7.0   | 728  | 0.9004          | 0.7946   | 0.7898    | 0.7946 | 0.7888 |
-| 0.2688        | 8.0   | 832  | 0.8947          | 0.8047   | 0.7993    | 0.8047 | 0.7995 |
-| 0.2392        | 9.0   | 936  | 0.9154          | 0.8024   | 0.7980    | 0.8024 | 0.7961 |
-| 0.2103        | 10.0  | 1040 | 0.9212          | 0.8029   | 0.7978    | 0.8029 | 0.7974 |
 ### Framework versions

 This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the None dataset.
 It achieves the following results on the evaluation set:
+- Loss: 0.8880
+- Accuracy: 0.8077
+- Precision: 0.8072
+- Recall: 0.8077
+- F1: 0.8032
 ## Model description
 | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
 |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
+| 2.2082        | 1.0   | 104  | 1.3318          | 0.6956   | 0.6361    | 0.6956 | 0.6473 |
+| 1.1512        | 2.0   | 208  | 1.0114          | 0.7604   | 0.7463    | 0.7604 | 0.7368 |
+| 0.8152        | 3.0   | 312  | 0.8805          | 0.7860   | 0.7677    | 0.7860 | 0.7698 |
+| 0.6142        | 4.0   | 416  | 0.8486          | 0.8025   | 0.8035    | 0.8025 | 0.7961 |
+| 0.4726        | 5.0   | 520  | 0.8651          | 0.7992   | 0.7987    | 0.7992 | 0.7894 |
+| 0.3677        | 6.0   | 624  | 0.8430          | 0.8077   | 0.8063    | 0.8077 | 0.8038 |
+| 0.2967        | 7.0   | 728  | 0.8564          | 0.8037   | 0.8029    | 0.8037 | 0.7995 |
+| 0.2494        | 8.0   | 832  | 0.8567          | 0.8077   | 0.8054    | 0.8077 | 0.8041 |
+| 0.2163        | 9.0   | 936  | 0.8789          | 0.8075   | 0.8060    | 0.8075 | 0.8035 |
+| 0.193         | 10.0  | 1040 | 0.8880          | 0.8077   | 0.8072    | 0.8077 | 0.8032 |
 ### Framework versions