End of training

README.md

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+---
+base_model: daila/wav2vec2-large-xls-r-300m-vi-colab
+tags:
+- generated_from_trainer
+datasets:
+- common_voice_16_1
+metrics:
+- wer
+model-index:
+- name: wav2vec2-large-xls-r-300m-vi-colab
+  results:
+  - task:
+      name: Automatic Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: common_voice_16_1
+      type: common_voice_16_1
+      config: vi
+      split: test
+      args: vi
+    metrics:
+    - name: Wer
+      type: wer
+      value: 0.5894672631150875
+---
+
+<!-- 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-vi-colab
+
+This model is a fine-tuned version of [daila/wav2vec2-large-xls-r-300m-vi-colab](https://huggingface.co/daila/wav2vec2-large-xls-r-300m-vi-colab) on the common_voice_16_1 dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.6432
+- Wer: 0.5895
+
+## 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    |
+|:-------------:|:-----:|:----:|:---------------:|:------:|
+| 0.0916        | 4.52  | 400  | 1.5440          | 0.6357 |
+| 0.1344        | 9.04  | 800  | 1.6043          | 0.6543 |
+| 0.0926        | 13.56 | 1200 | 1.7226          | 0.6365 |
+| 0.0703        | 18.08 | 1600 | 1.5989          | 0.6048 |
+| 0.0557        | 22.6  | 2000 | 1.6714          | 0.6001 |
+| 0.051         | 27.12 | 2400 | 1.6432          | 0.5895 |
+
+
+### Framework versions
+
+- Transformers 4.35.2
+- Pytorch 2.1.0+cu121
+- Datasets 2.16.1
+- Tokenizers 0.15.1

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