update model card README.md

README.md

@@ -17,7 +17,7 @@ model-index:
     metrics:
     - name: Wer
       type: wer
-      value: 0.6403468314731113
+      value: 0.39516649755557604
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -27,8 +27,8 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [evanarlian/distil-wav2vec2-xls-r-113m-id](https://huggingface.co/evanarlian/distil-wav2vec2-xls-r-113m-id) on the evanarlian/common_voice_11_0_id_filtered dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.5214
-- Wer: 0.6403
+- Loss: 0.3280
+- Wer: 0.3952
 
 ## Model description
 
@@ -51,26 +51,45 @@ The following hyperparameters were used during training:
 - train_batch_size: 8
 - eval_batch_size: 8
 - seed: 42
-- gradient_accumulation_steps: 2
-- total_train_batch_size: 16
+- gradient_accumulation_steps: 3
+- total_train_batch_size: 24
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
 - lr_scheduler_warmup_ratio: 0.3
-- num_epochs: 5.0
+- num_epochs: 25.0
 - mixed_precision_training: Native AMP
 
 ### Training results
 
-| Training Loss | Epoch | Step | Validation Loss | Wer    |
-|:-------------:|:-----:|:----:|:---------------:|:------:|
-| 2.8452        | 0.61  | 1000 | 2.8065          | 1.0    |
-| 1.3277        | 1.22  | 2000 | 1.0774          | 0.9330 |
-| 1.025         | 1.84  | 3000 | 0.8000          | 0.8474 |
-| 0.8497        | 2.45  | 4000 | 0.6812          | 0.7669 |
-| 0.7678        | 3.06  | 5000 | 0.6125          | 0.7186 |
-| 0.6886        | 3.67  | 6000 | 0.5758          | 0.6812 |
-| 0.6318        | 4.29  | 7000 | 0.5420          | 0.6570 |
-| 0.6086        | 4.9   | 8000 | 0.5214          | 0.6403 |
+| Training Loss | Epoch | Step  | Validation Loss | Wer    |
+|:-------------:|:-----:|:-----:|:---------------:|:------:|
+| 3.2512        | 0.92  | 1000  | 2.9098          | 1.0000 |
+| 2.163         | 1.84  | 2000  | 1.4810          | 0.9941 |
+| 1.2472        | 2.75  | 3000  | 0.9604          | 0.9196 |
+| 1.0166        | 3.67  | 4000  | 0.8240          | 0.8498 |
+| 0.8765        | 4.59  | 5000  | 0.6873          | 0.7741 |
+| 0.7712        | 5.51  | 6000  | 0.6083          | 0.7111 |
+| 0.6892        | 6.43  | 7000  | 0.5546          | 0.6592 |
+| 0.6314        | 7.35  | 8000  | 0.5022          | 0.6108 |
+| 0.5779        | 8.26  | 9000  | 0.4850          | 0.5825 |
+| 0.5245        | 9.18  | 10000 | 0.4665          | 0.5538 |
+| 0.4858        | 10.1  | 11000 | 0.4282          | 0.5279 |
+| 0.4616        | 11.02 | 12000 | 0.4053          | 0.5082 |
+| 0.421         | 11.94 | 13000 | 0.3809          | 0.4935 |
+| 0.4064        | 12.86 | 14000 | 0.3706          | 0.4781 |
+| 0.3758        | 13.77 | 15000 | 0.3743          | 0.4672 |
+| 0.3598        | 14.69 | 16000 | 0.3571          | 0.4521 |
+| 0.3441        | 15.61 | 17000 | 0.3455          | 0.4368 |
+| 0.3279        | 16.53 | 18000 | 0.3398          | 0.4386 |
+| 0.3086        | 17.45 | 19000 | 0.3512          | 0.4284 |
+| 0.3013        | 18.37 | 20000 | 0.3321          | 0.4233 |
+| 0.2963        | 19.28 | 21000 | 0.3391          | 0.4178 |
+| 0.2831        | 20.2  | 22000 | 0.3438          | 0.4114 |
+| 0.2801        | 21.12 | 23000 | 0.3336          | 0.4056 |
+| 0.2623        | 22.04 | 24000 | 0.3317          | 0.4012 |
+| 0.263         | 22.96 | 25000 | 0.3280          | 0.4005 |
+| 0.2529        | 23.88 | 26000 | 0.3268          | 0.3951 |
+| 0.2492        | 24.79 | 27000 | 0.3280          | 0.3952 |
 
 
 ### Framework versions