aydink
/

wav2vec2-base-finetuned-common_voice

@@ -20,13 +20,13 @@ should probably proofread and complete it, then remove this comment. -->
 This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.0001
-- Accuracy: 1.0
-- F1: 1.0
-- Recall: 1.0
-- Precision: 1.0
-- Mcc: 1.0
-- Auc: 1.0
 ## Model description
@@ -57,23 +57,23 @@ The following hyperparameters were used during training:
 ### Training results
-| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Recall | Precision | Mcc    | Auc |
-|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|:------:|:---:|
-| 0.0033        | 1.0   | 200  | 0.0113          | 0.9975   | 0.9975 | 0.9975 | 0.9975    | 0.9969 | 1.0 |
-| 0.0003        | 2.0   | 400  | 0.0002          | 1.0      | 1.0    | 1.0    | 1.0       | 1.0    | 1.0 |
-| 0.0002        | 3.0   | 600  | 0.0001          | 1.0      | 1.0    | 1.0    | 1.0       | 1.0    | 1.0 |
-| 0.0002        | 4.0   | 800  | 0.0001          | 1.0      | 1.0    | 1.0    | 1.0       | 1.0    | 1.0 |
-| 0.0001        | 5.0   | 1000 | 0.0001          | 1.0      | 1.0    | 1.0    | 1.0       | 1.0    | 1.0 |
-| 0.0002        | 6.0   | 1200 | 0.0001          | 1.0      | 1.0    | 1.0    | 1.0       | 1.0    | 1.0 |
-| 0.1282        | 7.0   | 1400 | 0.0001          | 1.0      | 1.0    | 1.0    | 1.0       | 1.0    | 1.0 |
-| 0.0001        | 8.0   | 1600 | 0.0001          | 1.0      | 1.0    | 1.0    | 1.0       | 1.0    | 1.0 |
-| 0.0001        | 9.0   | 1800 | 0.0001          | 1.0      | 1.0    | 1.0    | 1.0       | 1.0    | 1.0 |
-| 0.0001        | 10.0  | 2000 | 0.0001          | 1.0      | 1.0    | 1.0    | 1.0       | 1.0    | 1.0 |
 ### Framework versions
-- Transformers 4.40.2
-- Pytorch 2.2.1+cu121
 - Datasets 2.19.1
 - Tokenizers 0.19.1

 This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
 It achieves the following results on the evaluation set:
+- Loss: 0.0870
+- Accuracy: 0.9875
+- F1: 0.9875
+- Recall: 0.9875
+- Precision: 0.9877
+- Mcc: 0.9844
+- Auc: 0.9968
 ## Model description
 ### Training results
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Recall | Precision | Mcc    | Auc    |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|:------:|:------:|
+| 0.9826        | 1.0   | 200  | 0.9330          | 0.715    | 0.6769 | 0.7150 | 0.7516    | 0.6708 | 0.9379 |
+| 0.2818        | 2.0   | 400  | 0.5294          | 0.8425   | 0.8362 | 0.8425 | 0.8731    | 0.8133 | 0.9738 |
+| 0.1229        | 3.0   | 600  | 0.2185          | 0.945    | 0.9455 | 0.945  | 0.9476    | 0.9317 | 0.9917 |
+| 0.0094        | 4.0   | 800  | 0.2905          | 0.9425   | 0.9428 | 0.9425 | 0.9476    | 0.9293 | 0.9932 |
+| 0.0256        | 5.0   | 1000 | 0.1565          | 0.97     | 0.9702 | 0.97   | 0.9720    | 0.9629 | 0.9972 |
+| 0.0032        | 6.0   | 1200 | 0.1577          | 0.9775   | 0.9775 | 0.9775 | 0.9778    | 0.9720 | 0.9941 |
+| 0.0869        | 7.0   | 1400 | 0.1017          | 0.9825   | 0.9824 | 0.9825 | 0.9826    | 0.9782 | 0.9965 |
+| 0.0019        | 8.0   | 1600 | 0.1194          | 0.9775   | 0.9776 | 0.9775 | 0.9783    | 0.9720 | 0.9967 |
+| 0.0017        | 9.0   | 1800 | 0.0947          | 0.985    | 0.9850 | 0.9850 | 0.9851    | 0.9813 | 0.9972 |
+| 0.0016        | 10.0  | 2000 | 0.0870          | 0.9875   | 0.9875 | 0.9875 | 0.9877    | 0.9844 | 0.9968 |
 ### Framework versions
+- Transformers 4.41.0
+- Pytorch 2.3.0+cu121
 - Datasets 2.19.1
 - Tokenizers 0.19.1