batoula187
/

wav2vec2-large-xls-r-300m-arabic-colab

@@ -1,199 +1,90 @@
 ---
-library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
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 ---
+base_model: batoula187/wav2vec2-large-xls-r-300m-arabic-colab
+tags:
+- generated_from_trainer
+datasets:
+- common_voice_12_0
+metrics:
+- wer
+model-index:
+- name: wav2vec2-large-xls-r-300m-arabic-colab
+  results:
+  - task:
+      name: Automatic Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: common_voice_12_0
+      type: common_voice_12_0
+      config: ar
+      split: test[:10%]
+      args: ar
+    metrics:
+    - name: Wer
+      type: wer
+      value: 0.7661710754972002
 ---
+<!-- 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-large-xls-r-300m-arabic-colab
+This model is a fine-tuned version of [batoula187/wav2vec2-large-xls-r-300m-arabic-colab](https://huggingface.co/batoula187/wav2vec2-large-xls-r-300m-arabic-colab) on the common_voice_12_0 dataset.
+It achieves the following results on the evaluation set:
+- Loss: 2.0728
+- Wer: 0.7662
+## Model description
+More information needed
+## Intended uses & limitations
+More information needed
+## Training and evaluation data
+More information needed
+## Training procedure
+### Training hyperparameters
+The following hyperparameters were used during training:
+- learning_rate: 0.0003
+- train_batch_size: 16
+- eval_batch_size: 8
+- seed: 42
+- gradient_accumulation_steps: 2
+- total_train_batch_size: 32
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 500
+- num_epochs: 30
+- mixed_precision_training: Native AMP
+### Training results
+| Training Loss | Epoch   | Step | Validation Loss | Wer    |
+|:-------------:|:-------:|:----:|:---------------:|:------:|
+| 0.1061        | 2.2599  | 200  | 1.8297          | 0.8034 |
+| 0.1496        | 4.5198  | 400  | 1.6173          | 0.7955 |
+| 0.2105        | 6.7797  | 600  | 1.6220          | 0.8040 |
+| 0.1798        | 9.0395  | 800  | 2.2087          | 0.8405 |
+| 0.1389        | 11.2994 | 1000 | 1.7900          | 0.7868 |
+| 0.1143        | 13.5593 | 1200 | 1.7566          | 0.7886 |
+| 0.103         | 15.8192 | 1400 | 1.8148          | 0.7689 |
+| 0.0904        | 18.0791 | 1600 | 1.8059          | 0.7627 |
+| 0.0766        | 20.3390 | 1800 | 2.1398          | 0.7907 |
+| 0.0682        | 22.5989 | 2000 | 2.0384          | 0.7779 |
+| 0.0583        | 24.8588 | 2200 | 2.0727          | 0.7658 |
+| 0.0575        | 27.1186 | 2400 | 2.1649          | 0.7758 |
+| 0.0582        | 29.3785 | 2600 | 2.0728          | 0.7662 |
+### Framework versions
+- Transformers 4.40.2
+- Pytorch 2.2.1+cu121
+- Datasets 2.19.1
+- Tokenizers 0.19.1

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