Update README.md

README.md

@@ -4,9 +4,37 @@ tags:
 - generated_from_trainer
 datasets:
 - common_voice_8_0
+metrics:
+- wer
 model-index:
 - name: wav2vec2-large-xls-r-1b-frisian-cv-8
-  results: []
+  results:
+  - task:
+      name: Automatic Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: common_voice_8_0
+      type: common_voice_8_0
+      config: fy-NL
+      split: validation
+      args: fy-NL
+    metrics:
+    - name: Wer
+      type: wer
+      value: 0.14290815597771747
+  - task:
+      name: Automatic Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: common_voice_8_0
+      type: common_voice_8_0
+      config: fy-NL
+      split: test
+      args: fy-NL
+    metrics:
+    - name: Wer
+      type: wer
+      value: 0.1413499060557884
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -14,7 +42,13 @@ should probably proofread and complete it, then remove this comment. -->
 
 # wav2vec2-large-xls-r-1b-frisian-cv-8
 
-This model is a fine-tuned version of [greenw0lf/wav2vec2-large-xls-r-1b-frisian-cv-8](https://huggingface.co/greenw0lf/wav2vec2-large-xls-r-1b-frisian-cv-8) on the common_voice_8_0 dataset.
+This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the common_voice_8_0 dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.2131
+- Wer: 0.1429
+
+And on the test set:
+- Wer: 0.1413
 
 ## Model description
 
@@ -40,11 +74,43 @@ The following hyperparameters were used during training:
 - optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-08
 - lr_scheduler_type: linear
 - lr_scheduler_warmup_ratio: 0.1
-- num_epochs: 0.01
+- num_epochs: 50
 - mixed_precision_training: Native AMP
 
 ### Training results
 
+| Training Loss | Epoch | Step | Validation Loss | Wer    |
+|:-------------:|:-----:|:----:|:---------------:|:------:|
+| 6.0565        | 1.72  | 200  | 3.1053          | 1.0    |
+| 2.7675        | 3.45  | 400  | 1.1551          | 0.8611 |
+| 1.3474        | 5.17  | 600  | 0.4770          | 0.4397 |
+| 0.9617        | 6.9   | 800  | 0.3218          | 0.3343 |
+| 0.9058        | 8.62  | 1000 | 0.2741          | 0.2768 |
+| 0.9712        | 10.34 | 1200 | 0.2619          | 0.2505 |
+| 0.6908        | 12.07 | 1400 | 0.2288          | 0.2243 |
+| 0.745         | 13.79 | 1600 | 0.2288          | 0.2095 |
+| 0.7742        | 15.52 | 1800 | 0.2289          | 0.1979 |
+| 0.7231        | 17.24 | 2000 | 0.2198          | 0.1940 |
+| 0.6475        | 18.97 | 2200 | 0.2180          | 0.1992 |
+| 0.6421        | 20.69 | 2400 | 0.2133          | 0.1741 |
+| 0.5925        | 22.41 | 2600 | 0.1998          | 0.1747 |
+| 0.5608        | 24.14 | 2800 | 0.2212          | 0.1950 |
+| 0.5315        | 25.86 | 3000 | 0.2187          | 0.1624 |
+| 0.5362        | 27.59 | 3200 | 0.2057          | 0.1718 |
+| 0.563         | 29.31 | 3400 | 0.2090          | 0.1613 |
+| 0.4218        | 31.03 | 3600 | 0.2126          | 0.1531 |
+| 0.3826        | 32.76 | 3800 | 0.2084          | 0.1538 |
+| 0.356         | 34.48 | 4000 | 0.2115          | 0.1612 |
+| 0.2966        | 36.21 | 4200 | 0.2093          | 0.1536 |
+| 0.3377        | 37.93 | 4400 | 0.2061          | 0.1527 |
+| 0.321         | 39.66 | 4600 | 0.2121          | 0.1463 |
+| 0.2942        | 41.38 | 4800 | 0.2158          | 0.1441 |
+| 0.2931        | 43.1  | 5000 | 0.2173          | 0.1446 |
+| 0.2346        | 44.83 | 5200 | 0.2152          | 0.1436 |
+| 0.2543        | 46.55 | 5400 | 0.2066          | 0.1445 |
+| 0.2385        | 48.28 | 5600 | 0.2108          | 0.1432 |
+| 0.2726        | 50.0  | 5800 | 0.2131          | 0.1429 |
+
 
 
 ### Framework versions