update model card README.md

README.md

@@ -17,8 +17,8 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the OPENSLR_SLR66 - NA dataset.
 It achieves the following results on the evaluation set:
-- Loss: 3.3845
-- Wer: 0.9869
+- Loss: 0.3119
+- Wer: 0.2613
 
 ## Model description
 
@@ -37,32 +37,53 @@ More information needed
 ### Training hyperparameters
 
 The following hyperparameters were used during training:
-- learning_rate: 5e-07
+- learning_rate: 2e-05
 - train_batch_size: 16
 - eval_batch_size: 4
 - seed: 42
-- gradient_accumulation_steps: 4
-- total_train_batch_size: 64
+- gradient_accumulation_steps: 2
+- total_train_batch_size: 32
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
-- lr_scheduler_warmup_steps: 300
-- num_epochs: 100.0
+- lr_scheduler_warmup_steps: 2000
+- num_epochs: 150.0
 - mixed_precision_training: Native AMP
 
 ### Training results
 
-| Training Loss | Epoch | Step | Validation Loss | Wer    |
-|:-------------:|:-----:|:----:|:---------------:|:------:|
-| 3.5131        | 9.61  | 500  | 3.5294          | 1.0    |
-| 2.8596        | 19.23 | 1000 | 3.5708          | 1.0    |
-| 1.9055        | 28.84 | 1500 | 3.6433          | 1.0007 |
-| 1.4239        | 38.46 | 2000 | 3.6569          | 0.9995 |
-| 1.2168        | 48.08 | 2500 | 3.6079          | 0.9957 |
-| 1.1063        | 57.69 | 3000 | 3.5738          | 0.9925 |
-| 1.0404        | 67.31 | 3500 | 3.4857          | 0.9889 |
-| 1.001         | 76.92 | 4000 | 3.4882          | 0.9858 |
-| 0.982         | 86.54 | 4500 | 3.3851          | 0.9871 |
-| 0.9612        | 96.15 | 5000 | 3.3869          | 0.9873 |
+| Training Loss | Epoch  | Step  | Validation Loss | Wer    |
+|:-------------:|:------:|:-----:|:---------------:|:------:|
+| 2.9038        | 4.8    | 500   | 3.0125          | 1.0    |
+| 1.3777        | 9.61   | 1000  | 0.8681          | 0.8753 |
+| 1.1436        | 14.42  | 1500  | 0.6256          | 0.7961 |
+| 1.0997        | 19.23  | 2000  | 0.5244          | 0.6875 |
+| 1.0363        | 24.04  | 2500  | 0.4585          | 0.6276 |
+| 0.7996        | 28.84  | 3000  | 0.4072          | 0.5295 |
+| 0.825         | 33.65  | 3500  | 0.3590          | 0.5222 |
+| 0.8018        | 38.46  | 4000  | 0.3678          | 0.4671 |
+| 0.7545        | 43.27  | 4500  | 0.3474          | 0.3962 |
+| 0.7375        | 48.08  | 5000  | 0.3224          | 0.3869 |
+| 0.6198        | 52.88  | 5500  | 0.3233          | 0.3630 |
+| 0.6608        | 57.69  | 6000  | 0.3029          | 0.3308 |
+| 0.645         | 62.5   | 6500  | 0.3195          | 0.3722 |
+| 0.5249        | 67.31  | 7000  | 0.3004          | 0.3202 |
+| 0.4875        | 72.11  | 7500  | 0.2826          | 0.2992 |
+| 0.5171        | 76.92  | 8000  | 0.2962          | 0.2976 |
+| 0.4974        | 81.73  | 8500  | 0.2990          | 0.2933 |
+| 0.4387        | 86.54  | 9000  | 0.2834          | 0.2755 |
+| 0.4511        | 91.34  | 9500  | 0.2886          | 0.2787 |
+| 0.4112        | 96.15  | 10000 | 0.3093          | 0.2976 |
+| 0.4064        | 100.96 | 10500 | 0.3123          | 0.2863 |
+| 0.4047        | 105.77 | 11000 | 0.2968          | 0.2719 |
+| 0.3519        | 110.57 | 11500 | 0.3106          | 0.2832 |
+| 0.3719        | 115.38 | 12000 | 0.3030          | 0.2737 |
+| 0.3669        | 120.19 | 12500 | 0.2964          | 0.2714 |
+| 0.3386        | 125.0  | 13000 | 0.3101          | 0.2714 |
+| 0.3137        | 129.8  | 13500 | 0.3063          | 0.2710 |
+| 0.3008        | 134.61 | 14000 | 0.3082          | 0.2617 |
+| 0.301         | 139.42 | 14500 | 0.3121          | 0.2628 |
+| 0.3291        | 144.23 | 15000 | 0.3105          | 0.2612 |
+| 0.3133        | 149.04 | 15500 | 0.3114          | 0.2624 |
 
 
 ### Framework versions