update model card README.md

README.md

@@ -1,8 +1,6 @@
 ---
 license: apache-2.0
 tags:
-- automatic-speech-recognition
-- google/fleurs
 - generated_from_trainer
 datasets:
 - fleurs
@@ -15,15 +13,15 @@ model-index:
       name: Automatic Speech Recognition
       type: automatic-speech-recognition
     dataset:
-      name: GOOGLE/FLEURS - PS_AF
+      name: fleurs
       type: fleurs
       config: ps_af
       split: test
-      args: 'Config: ps_af, Training split: train+validation, Eval split: test'
+      args: ps_af
     metrics:
     - name: Wer
       type: wer
-      value: 0.5156036834924966
+      value: 0.5139836289222374
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -31,11 +29,11 @@ should probably proofread and complete it, then remove this comment. -->
 
 # facebook/wav2vec2-xls-r-300m
 
-This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the GOOGLE/FLEURS - PS_AF dataset.
+This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the fleurs dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.9162
-- Wer: 0.5156
-- Cer: 0.1969
+- Loss: 0.9287
+- Wer: 0.5140
+- Cer: 0.1972
 
 ## Model description
 
@@ -63,7 +61,7 @@ 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: 2000
-- training_steps: 4000
+- training_steps: 4500
 - mixed_precision_training: Native AMP
 
 ### Training results
@@ -77,7 +75,8 @@ The following hyperparameters were used during training:
 | 0.7838        | 31.65 | 2500 | 0.2152 | 0.8952          | 0.5556 |
 | 0.6665        | 37.97 | 3000 | 0.2017 | 0.8908          | 0.5252 |
 | 0.6265        | 44.3  | 3500 | 0.1954 | 0.9063          | 0.5133 |
-| 0.5935        | 50.63 | 4000 | 0.9162 | 0.5156          | 0.1969 |
+| 0.5935        | 50.63 | 4000 | 0.1969 | 0.9162          | 0.5156 |
+| 0.5174        | 56.96 | 4500 | 0.9287 | 0.5140          | 0.1972 |
 
 
 ### Framework versions