LevonHakobyan
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testing_tensorboard_w_new_access_token

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 ---
-library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
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 ---
+license: mit
+base_model: facebook/w2v-bert-2.0
+tags:
+- generated_from_trainer
+datasets:
+- common_voice_17_0
+metrics:
+- wer
+model-index:
+- name: testing_tensorboard_w_new_access_token
+  results:
+  - task:
+      name: Automatic Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: common_voice_17_0
+      type: common_voice_17_0
+      config: hy-AM
+      split: test
+      args: hy-AM
+    metrics:
+    - name: Wer
+      type: wer
+      value: 1.0
 ---
+<!-- 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. -->
+# testing_tensorboard_w_new_access_token
+This model is a fine-tuned version of [facebook/w2v-bert-2.0](https://huggingface.co/facebook/w2v-bert-2.0) on the common_voice_17_0 dataset.
+It achieves the following results on the evaluation set:
+- Loss: 3.1867
+- Wer: 1.0
+- Cer: 0.9653
+## 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: 0.0001
+- 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: constant
+- num_epochs: 10
+- mixed_precision_training: Native AMP
+### Training results
+| Training Loss | Epoch  | Step | Validation Loss | Wer    | Cer    |
+|:-------------:|:------:|:----:|:---------------:|:------:|:------:|
+| 3.3114        | 0.6154 | 200  | 3.2030          | 1.0    | 1.0    |
+| 3.1797        | 1.2308 | 400  | 3.1973          | 1.0    | 1.0    |
+| 3.1791        | 1.8462 | 600  | 3.1899          | 1.0    | 1.0    |
+| 3.1767        | 2.4615 | 800  | 3.1787          | 1.0    | 1.0    |
+| 3.1681        | 3.0769 | 1000 | 3.1870          | 1.0    | 0.9987 |
+| 3.1783        | 3.6923 | 1200 | 3.1996          | 0.9998 | 0.9822 |
+| 3.167         | 4.3077 | 1400 | 3.1726          | 1.0    | 1.0    |
+| 3.171         | 4.9231 | 1600 | 3.1743          | 1.0    | 0.9653 |
+| 3.1654        | 5.5385 | 1800 | 3.1926          | 1.0000 | 0.9487 |
+| 3.1714        | 6.1538 | 2000 | 3.1700          | 1.0    | 0.9653 |
+| 3.1638        | 6.7692 | 2200 | 3.1688          | 1.0    | 0.9653 |
+| 3.164         | 7.3846 | 2400 | 3.1934          | 1.0000 | 0.9487 |
+| 3.1729        | 8.0    | 2600 | 3.1689          | 1.0    | 0.9653 |
+| 3.1652        | 8.6154 | 2800 | 3.1660          | 1.0    | 0.9653 |
+| 3.1569        | 9.2308 | 3000 | 3.1890          | 1.0000 | 0.9487 |
+| 3.1639        | 9.8462 | 3200 | 3.1867          | 1.0    | 0.9653 |
+### Framework versions
+- Transformers 4.41.2
+- Pytorch 2.3.0+cu121
+- Datasets 2.20.0
+- Tokenizers 0.19.1

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