Model save

README.md

@@ -6,36 +6,17 @@ tags:
 - generated_from_trainer
 datasets:
 - common_voice_17_0
-metrics:
-- wer
 model-index:
-- name: result_data-1
-  results:
-  - task:
-      name: Automatic Speech Recognition
-      type: automatic-speech-recognition
-    dataset:
-      name: common_voice_17_0
-      type: common_voice_17_0
-      config: uk
-      split: test
-      args: uk
-    metrics:
-    - name: Wer
-      type: wer
-      value: 0.36512878573450325
+- name: best_model_copy
+  results: []
 ---
 
 <!-- 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. -->
 
-# result_data-1
+# best_model_copy
 
 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_17_0 dataset.
-It achieves the following results on the evaluation set:
-- Loss: 0.2220
-- Wer: 0.3651
-- Cer: 0.1691
 
 ## Model description
 
@@ -54,7 +35,7 @@ More information needed
 ### Training hyperparameters
 
 The following hyperparameters were used during training:
-- learning_rate: 6.532628754904162e-05
+- learning_rate: 5e-05
 - train_batch_size: 16
 - eval_batch_size: 16
 - seed: 42
@@ -64,21 +45,11 @@ The following hyperparameters were used during training:
 - total_eval_batch_size: 32
 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
 - lr_scheduler_type: linear
-- lr_scheduler_warmup_steps: 206
-- num_epochs: 7.0
+- training_steps: 20
 - mixed_precision_training: Native AMP
 
 ### Training results
 
-| Training Loss | Epoch  | Step | Validation Loss | Wer    | Cer    |
-|:-------------:|:------:|:----:|:---------------:|:------:|:------:|
-| 0.6324        | 0.9099 | 1000 | 0.5004          | 0.6083 | 0.2381 |
-| 0.3497        | 1.8198 | 2000 | 0.3087          | 0.4650 | 0.1965 |
-| 0.2642        | 2.7298 | 3000 | 0.2636          | 0.4249 | 0.1841 |
-| 0.2328        | 3.6397 | 4000 | 0.2431          | 0.3960 | 0.1789 |
-| 0.1933        | 4.5496 | 5000 | 0.2289          | 0.3773 | 0.1732 |
-| 0.1783        | 5.4595 | 6000 | 0.2300          | 0.3728 | 0.1711 |
-| 0.1617        | 6.3694 | 7000 | 0.2233          | 0.3637 | 0.1700 |
 
 
 ### Framework versions