update model card README.md

README.md

@@ -13,8 +13,8 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [Doogie/Waynehills-STT-doogie](https://huggingface.co/Doogie/Waynehills-STT-doogie) on an unknown dataset.
 It achieves the following results on the evaluation set:
-- Loss: 2.8554
-- Wer: 0.9881
+- Loss: 10.3564
+- Wer: 1.0405
 
 ## Model description
 
@@ -34,34 +34,49 @@ More information needed
 
 The following hyperparameters were used during training:
 - learning_rate: 0.0001
-- train_batch_size: 1
+- train_batch_size: 4
 - eval_batch_size: 8
 - seed: 42
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
 - lr_scheduler_warmup_steps: 1000
-- num_epochs: 20
+- num_epochs: 60
 
 ### Training results
 
 | Training Loss | Epoch | Step  | Validation Loss | Wer    |
 |:-------------:|:-----:|:-----:|:---------------:|:------:|
-| 4.6882        | 1.2   | 1000  | 4.8098          | 0.9970 |
-| 2.4927        | 2.4   | 2000  | 2.9959          | 1.0416 |
-| 1.6066        | 3.59  | 3000  | 2.5789          | 0.9442 |
-| 1.2902        | 4.79  | 4000  | 2.6152          | 1.0439 |
-| 1.0486        | 5.99  | 5000  | 2.5047          | 1.0861 |
-| 0.8648        | 7.19  | 6000  | 2.4007          | 0.9347 |
-| 0.7659        | 8.38  | 7000  | 2.5087          | 0.9602 |
-| 0.6357        | 9.58  | 8000  | 2.5803          | 1.0018 |
-| 0.5684        | 10.78 | 9000  | 2.6190          | 0.9656 |
-| 0.5647        | 11.98 | 10000 | 2.6129          | 0.9436 |
-| 0.5048        | 13.17 | 11000 | 2.7174          | 0.9816 |
-| 0.4405        | 14.37 | 12000 | 2.7107          | 0.9519 |
-| 0.4273        | 15.57 | 13000 | 2.7672          | 0.9673 |
-| 0.4049        | 16.77 | 14000 | 2.8277          | 0.9869 |
-| 0.3731        | 17.96 | 15000 | 2.8238          | 0.9786 |
-| 0.3761        | 19.16 | 16000 | 2.8554          | 0.9881 |
+| 4.6722        | 1.92  | 1000  | 5.5301          | 1.0    |
+| 4.3024        | 3.84  | 2000  | 6.4368          | 1.0    |
+| 3.8135        | 5.76  | 3000  | 6.9063          | 1.0    |
+| 3.4163        | 7.68  | 4000  | 6.9737          | 1.0018 |
+| 3.1162        | 9.6   | 5000  | 7.1260          | 1.0027 |
+| 2.8724        | 11.52 | 6000  | 7.2143          | 1.0009 |
+| 2.6694        | 13.44 | 7000  | 7.4370          | 1.0050 |
+| 2.4808        | 15.36 | 8000  | 7.9850          | 1.0090 |
+| 2.2994        | 17.27 | 9000  | 8.1296          | 1.0198 |
+| 2.1436        | 19.19 | 10000 | 8.1327          | 1.0081 |
+| 2.0331        | 21.11 | 11000 | 8.2656          | 1.0135 |
+| 1.9278        | 23.03 | 12000 | 8.5640          | 1.0176 |
+| 1.8417        | 24.95 | 13000 | 8.5057          | 1.0212 |
+| 1.7496        | 26.87 | 14000 | 8.8110          | 1.0207 |
+| 1.6494        | 28.79 | 15000 | 9.0795          | 1.0306 |
+| 1.5882        | 30.71 | 16000 | 9.1341          | 1.0338 |
+| 1.5279        | 32.63 | 17000 | 9.2713          | 1.0284 |
+| 1.4712        | 34.55 | 18000 | 9.3591          | 1.0333 |
+| 1.4065        | 36.47 | 19000 | 9.4739          | 1.0293 |
+| 1.3637        | 38.39 | 20000 | 9.6498          | 1.0351 |
+| 1.3024        | 40.31 | 21000 | 9.7285          | 1.0365 |
+| 1.2737        | 42.23 | 22000 | 9.7353          | 1.0329 |
+| 1.2459        | 44.15 | 23000 | 10.0423         | 1.0374 |
+| 1.2079        | 46.07 | 24000 | 10.1164         | 1.0419 |
+| 1.1791        | 47.98 | 25000 | 10.1437         | 1.0437 |
+| 1.1593        | 49.9  | 26000 | 10.2292         | 1.0446 |
+| 1.1512        | 51.82 | 27000 | 10.2338         | 1.0405 |
+| 1.1041        | 53.74 | 28000 | 10.3070         | 1.0459 |
+| 1.1064        | 55.66 | 29000 | 10.3700         | 1.0419 |
+| 1.0783        | 57.58 | 30000 | 10.3950         | 1.0455 |
+| 1.0762        | 59.5  | 31000 | 10.3564         | 1.0405 |
 
 
 ### Framework versions