update model card README.md

README.md

@@ -18,8 +18,8 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MULTILINGUAL_LIBRISPEECH - GERMAN dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.2157
-- Wer: 0.1562
+- Loss: 0.2398
+- Wer: 0.1520
 
 ## Model description
 
@@ -45,26 +45,40 @@ The following hyperparameters were used during training:
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
 - lr_scheduler_warmup_steps: 1000
-- num_epochs: 100.0
+- num_epochs: 200.0
 - mixed_precision_training: Native AMP
 
 ### Training results
 
-| Training Loss | Epoch | Step | Validation Loss | Wer    |
-|:-------------:|:-----:|:----:|:---------------:|:------:|
-| 3.0132        | 7.25  | 500  | 2.9393          | 1.0    |
-| 2.9241        | 14.49 | 1000 | 2.8734          | 1.0    |
-| 1.0766        | 21.74 | 1500 | 0.2773          | 0.2488 |
-| 0.8416        | 28.99 | 2000 | 0.2224          | 0.1990 |
-| 0.8048        | 36.23 | 2500 | 0.2063          | 0.1792 |
-| 0.7664        | 43.48 | 3000 | 0.2088          | 0.1748 |
-| 0.6571        | 50.72 | 3500 | 0.2042          | 0.1668 |
-| 0.7014        | 57.97 | 4000 | 0.2136          | 0.1649 |
-| 0.6171        | 65.22 | 4500 | 0.2139          | 0.1641 |
-| 0.6609        | 72.46 | 5000 | 0.2144          | 0.1621 |
-| 0.6318        | 79.71 | 5500 | 0.2129          | 0.1600 |
-| 0.6222        | 86.96 | 6000 | 0.2124          | 0.1582 |
-| 0.588         | 94.2  | 6500 | 0.2143          | 0.1560 |
+| Training Loss | Epoch  | Step  | Validation Loss | Wer    |
+|:-------------:|:------:|:-----:|:---------------:|:------:|
+| 3.0132        | 7.25   | 500   | 2.9393          | 1.0    |
+| 2.9241        | 14.49  | 1000  | 2.8734          | 1.0    |
+| 1.0766        | 21.74  | 1500  | 0.2773          | 0.2488 |
+| 0.8416        | 28.99  | 2000  | 0.2224          | 0.1990 |
+| 0.8048        | 36.23  | 2500  | 0.2063          | 0.1792 |
+| 0.7664        | 43.48  | 3000  | 0.2088          | 0.1748 |
+| 0.6571        | 50.72  | 3500  | 0.2042          | 0.1668 |
+| 0.7014        | 57.97  | 4000  | 0.2136          | 0.1649 |
+| 0.6171        | 65.22  | 4500  | 0.2139          | 0.1641 |
+| 0.6609        | 72.46  | 5000  | 0.2144          | 0.1621 |
+| 0.6318        | 79.71  | 5500  | 0.2129          | 0.1600 |
+| 0.6222        | 86.96  | 6000  | 0.2124          | 0.1582 |
+| 0.608         | 94.2   | 6500  | 0.2255          | 0.1639 |
+| 0.6099        | 101.45 | 7000  | 0.2265          | 0.1622 |
+| 0.6069        | 108.7  | 7500  | 0.2246          | 0.1593 |
+| 0.5929        | 115.94 | 8000  | 0.2323          | 0.1617 |
+| 0.6218        | 123.19 | 8500  | 0.2287          | 0.1566 |
+| 0.5751        | 130.43 | 9000  | 0.2275          | 0.1563 |
+| 0.5181        | 137.68 | 9500  | 0.2316          | 0.1579 |
+| 0.6306        | 144.93 | 10000 | 0.2372          | 0.1556 |
+| 0.5874        | 152.17 | 10500 | 0.2362          | 0.1533 |
+| 0.5546        | 159.42 | 11000 | 0.2342          | 0.1543 |
+| 0.6294        | 166.67 | 11500 | 0.2381          | 0.1536 |
+| 0.5989        | 173.91 | 12000 | 0.2360          | 0.1527 |
+| 0.5697        | 181.16 | 12500 | 0.2399          | 0.1526 |
+| 0.5379        | 188.41 | 13000 | 0.2375          | 0.1523 |
+| 0.5022        | 195.65 | 13500 | 0.2395          | 0.1519 |
 
 
 ### Framework versions