Update README.md

README.md

@@ -21,39 +21,42 @@ model-index:
       args: br
     metrics:
     - type: wer
-      value: 49.79811574697174
-      name: Wer
+      value: 41
+      name: WER
+    - type: cer
+      value: 14.7
+      name: CER
+language:
+- br
+pipeline_tag: automatic-speech-recognition
 ---
 
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
-
 # wav2vec2-xls-r-300m-br
 
-This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice_15_0 dataset.
-It achieves the following results on the evaluation set:
-- Loss: 0.8887
-- Wer: 49.7981
-- Cer: 17.3877
+This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on Mozilla Common Voice 15 Breton dataset and [Roadennoù](https://github.com/gweltou/roadennou) dataset. It achieves the following results on the MCV15-br test set:
+- Wer: 41.0
+- Cer: 14.7
 
 ## Model description
 
-More information needed
+This model was trained to assess the performance wav2vec2-xls-r-300m for fine-tuning a Breton ASR model. 
 
 ## Intended uses & limitations
 
-More information needed
+This model is a research model. Usage for production is not recommended.
 
 ## Training and evaluation data
 
-More information needed
+The training dataset consists of MCV15-br train dataset and 90% of the Roadennoù dataset.
+The validation dataset consists of  MCV15-br validation dataset and the remaining 10% of the Roadennoù dataset.
+The final test dataset consists of MCV15-br test dataset.
 
 ## Training procedure
 
 ### Training hyperparameters
 
 The following hyperparameters were used during training:
-- learning_rate: 5e-05
+- learning_rate: 6e-05
 - train_batch_size: 8
 - eval_batch_size: 8
 - seed: 42
@@ -65,33 +68,10 @@ The following hyperparameters were used during training:
 - num_epochs: 40
 - mixed_precision_training: Native AMP
 
-### Training results
-
-| Training Loss | Epoch | Step  | Validation Loss | Wer     | Cer     |
-|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|
-| 5.1153        | 2.18  | 1000  | 2.8854          | 100.0   | 100.0   |
-| 1.4117        | 4.36  | 2000  | 0.9161          | 71.2786 | 25.3180 |
-| 0.7888        | 6.54  | 3000  | 0.7753          | 62.7456 | 22.0767 |
-| 0.6316        | 8.71  | 4000  | 0.7550          | 58.1786 | 20.5383 |
-| 0.5434        | 10.89 | 5000  | 0.7508          | 56.5096 | 20.1168 |
-| 0.4672        | 13.07 | 6000  | 0.7844          | 54.9125 | 19.3835 |
-| 0.4237        | 15.25 | 7000  | 0.7786          | 53.2705 | 18.5765 |
-| 0.3899        | 17.43 | 8000  | 0.8050          | 53.0552 | 18.6105 |
-| 0.3607        | 19.61 | 9000  | 0.8280          | 51.9874 | 18.3024 |
-| 0.3355        | 21.79 | 10000 | 0.7967          | 51.5388 | 17.9811 |
-| 0.3098        | 23.97 | 11000 | 0.8296          | 51.2876 | 17.9547 |
-| 0.2937        | 26.14 | 12000 | 0.8544          | 50.9915 | 17.7827 |
-| 0.2793        | 28.32 | 13000 | 0.8909          | 51.5478 | 18.1286 |
-| 0.2641        | 30.5  | 14000 | 0.8740          | 50.4800 | 17.6561 |
-| 0.2552        | 32.68 | 15000 | 0.8832          | 49.9776 | 17.4463 |
-| 0.2467        | 34.86 | 16000 | 0.8753          | 50.3096 | 17.4765 |
-| 0.2378        | 37.04 | 17000 | 0.8895          | 49.8789 | 17.3952 |
-| 0.2337        | 39.22 | 18000 | 0.8887          | 49.7981 | 17.3877 |
-
 
 ### Framework versions
 
 - Transformers 4.39.1
 - Pytorch 2.0.1+cu117
 - Datasets 2.18.0
-- Tokenizers 0.15.2
+- Tokenizers 0.15.2