End of training

README.md

@@ -12,8 +12,8 @@ model-index:
 - name: w2v-bert-2.0-Vietnamese-colab-CV17.0
   results:
   - task:
-      type: automatic-speech-recognition
       name: Automatic Speech Recognition
+      type: automatic-speech-recognition
     dataset:
       name: common_voice_17_0
       type: common_voice_17_0
@@ -21,9 +21,9 @@ model-index:
       split: test
       args: vi
     metrics:
-    - type: wer
-      value: 0.21299227554767633
-      name: Wer
+    - name: Wer
+      type: wer
+      value: 0.26461245235069886
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -33,8 +33,8 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [facebook/w2v-bert-2.0](https://huggingface.co/facebook/w2v-bert-2.0) on the common_voice_17_0 dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.7230
-- Wer: 0.2130
+- Loss: 0.7535
+- Wer: 0.2646
 
 ## Model description
 
@@ -53,7 +53,7 @@ More information needed
 ### Training hyperparameters
 
 The following hyperparameters were used during training:
-- learning_rate: 5e-05
+- learning_rate: 3e-05
 - train_batch_size: 16
 - eval_batch_size: 8
 - seed: 42
@@ -69,14 +69,14 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch  | Step | Validation Loss | Wer    |
 |:-------------:|:------:|:----:|:---------------:|:------:|
-| 4.9411        | 3.2609 | 300  | 0.8347          | 0.3058 |
-| 0.1858        | 6.5217 | 600  | 0.7461          | 0.2676 |
-| 0.0492        | 9.7826 | 900  | 0.7230          | 0.2130 |
+| 3.2974        | 3.2609 | 300  | 0.6899          | 0.3559 |
+| 0.1432        | 6.5217 | 600  | 0.7335          | 0.2991 |
+| 0.0363        | 9.7826 | 900  | 0.7535          | 0.2646 |
 
 
 ### Framework versions
 
-- Transformers 4.48.3
+- Transformers 4.50.0
 - Pytorch 2.6.0+cu124
 - Datasets 3.4.1
 - Tokenizers 0.21.1