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: 0.3026260059296908
-      name: Wer
+    - name: Wer
+      type: wer
+      value: 0.2728716645489199
 ---
 
 <!-- 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: 0.7833
-- Wer: 0.3026
+- Loss: 1.0607
+- Wer: 0.2729
 
 ## Model description
 
@@ -62,22 +62,27 @@ The following hyperparameters were used during training:
 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
 - lr_scheduler_type: linear
 - lr_scheduler_warmup_steps: 500
-- num_epochs: 15
+- num_epochs: 30
 - mixed_precision_training: Native AMP
 
 ### Training results
 
 | Training Loss | Epoch   | Step | Validation Loss | Wer    |
 |:-------------:|:-------:|:----:|:---------------:|:------:|
-| 5.0336        | 3.2609  | 300  | 3.2141          | 1.0132 |
-| 2.5587        | 6.5217  | 600  | 1.3607          | 0.7407 |
-| 0.2529        | 9.7826  | 900  | 0.8210          | 0.3589 |
-| 0.0571        | 13.0435 | 1200 | 0.7833          | 0.3026 |
+| 2.8799        | 3.2609  | 300  | 0.7434          | 0.3899 |
+| 0.1626        | 6.5217  | 600  | 0.8157          | 0.3578 |
+| 0.0823        | 9.7826  | 900  | 0.8759          | 0.3704 |
+| 0.04          | 13.0435 | 1200 | 0.9129          | 0.3195 |
+| 0.0169        | 16.3043 | 1500 | 0.9113          | 0.2904 |
+| 0.0056        | 19.5652 | 1800 | 0.9906          | 0.2809 |
+| 0.0016        | 22.8261 | 2100 | 1.0506          | 0.2848 |
+| 0.0005        | 26.0870 | 2400 | 1.0502          | 0.2730 |
+| 0.0002        | 29.3478 | 2700 | 1.0607          | 0.2729 |
 
 
 ### Framework versions
 
 - Transformers 4.50.0
 - Pytorch 2.6.0+cu124
-- Datasets 3.4.1
+- Datasets 3.5.0
 - Tokenizers 0.21.1