LevonHakobyan
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adapter_freezed_base_const_lr_1-e3

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 ---
-library_name: transformers
-tags: []
 ---
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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: adapter_freezed_base_const_lr_1-e3
+  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: 0.9564916295314947
 ---
+<!-- 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. -->
+# adapter_freezed_base_const_lr_1-e3
+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: 1.0297
+- Wer: 0.9565
+- Cer: 0.2641
+## 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: 0.001
+- 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    |
+|:-------------:|:------:|:----:|:---------------:|:------:|:------:|
+| 0.856         | 1.5385 | 500  | 0.9286          | 0.9535 | 0.2798 |
+| 0.7189        | 3.0769 | 1000 | 0.8544          | 0.9296 | 0.2557 |
+| 0.6114        | 4.6154 | 1500 | 0.9302          | 0.9596 | 0.2675 |
+| 0.4397        | 6.1538 | 2000 | 0.9972          | 0.9294 | 0.2585 |
+| 0.4507        | 7.6923 | 2500 | 0.9594          | 0.9363 | 0.2589 |
+| 0.3154        | 9.2308 | 3000 | 1.0297          | 0.9565 | 0.2641 |
+### Framework versions
+- Transformers 4.41.2
+- Pytorch 2.3.0+cu121
+- Datasets 2.20.0
+- Tokenizers 0.19.1

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