End of training

README.md

@@ -1,22 +1,22 @@
 ---
-base_model: openai/whisper-base
-datasets:
-- fleurs
+library_name: transformers
 language:
 - pt
-library_name: transformers
 license: apache-2.0
-metrics:
-- wer
+base_model: openai/whisper-base
 tags:
 - hf-asr-leaderboard
 - generated_from_trainer
+datasets:
+- fleurs
+metrics:
+- wer
 model-index:
 - name: Whisper Base Portugese Punctuation 5k - Chee Li
   results:
   - task:
-      type: automatic-speech-recognition
       name: Automatic Speech Recognition
+      type: automatic-speech-recognition
     dataset:
       name: Google Fleurs
       type: fleurs
@@ -24,9 +24,9 @@ model-index:
       split: None
       args: 'config: pt split: test'
     metrics:
-    - type: wer
-      value: 33.39913517578492
-      name: Wer
+    - name: Wer
+      type: wer
+      value: 32.52491069749953
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -36,8 +36,8 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the Google Fleurs dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.8430
-- Wer: 33.3991
+- Loss: 0.8518
+- Wer: 32.5249
 
 ## Model description
 
@@ -70,8 +70,8 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch   | Step | Validation Loss | Wer     |
 |:-------------:|:-------:|:----:|:---------------:|:-------:|
-| 0.0431        | 5.0251  | 1000 | 0.8316          | 38.6163 |
-| 0.0012        | 10.0503 | 2000 | 0.8430          | 33.3991 |
+| 0.0416        | 5.0251  | 1000 | 0.8330          | 38.7902 |
+| 0.0012        | 10.0503 | 2000 | 0.8518          | 32.5249 |
 
 
 ### Framework versions