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adapter_head_full_const_lr_1e-4_l20-l23_const_lr_1e-7_l1-l19

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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_head_full_const_lr_1e-4_l20-l23_const_lr_1e-7_l1-l19
+  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: 0.19462844754907865
 ---
+<!-- 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_head_full_const_lr_1e-4_l20-l23_const_lr_1e-7_l1-l19
+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: 0.3638
+- Wer: 0.1946
+- Cer: 0.0323
+## 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: 5e-05
+- 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
+- num_epochs: 100
+- mixed_precision_training: Native AMP
+### Training results
+| Training Loss | Epoch   | Step  | Validation Loss | Wer    | Cer    |
+|:-------------:|:-------:|:-----:|:---------------:|:------:|:------:|
+| 0.1716        | 2.3077  | 750   | 0.2372          | 0.3536 | 0.0576 |
+| 0.0888        | 4.6154  | 1500  | 0.2341          | 0.3066 | 0.0509 |
+| 0.0487        | 6.9231  | 2250  | 0.2555          | 0.2823 | 0.0467 |
+| 0.0221        | 9.2308  | 3000  | 0.2957          | 0.2668 | 0.0444 |
+| 0.0193        | 11.5385 | 3750  | 0.3013          | 0.2461 | 0.0411 |
+| 0.0162        | 13.8462 | 4500  | 0.3230          | 0.2584 | 0.0431 |
+| 0.0107        | 16.1538 | 5250  | 0.3377          | 0.2454 | 0.0408 |
+| 0.0106        | 18.4615 | 6000  | 0.3370          | 0.2473 | 0.0413 |
+| 0.0111        | 20.7692 | 6750  | 0.3457          | 0.2448 | 0.0414 |
+| 0.0084        | 23.0769 | 7500  | 0.3279          | 0.2302 | 0.0387 |
+| 0.0083        | 25.3846 | 8250  | 0.3402          | 0.2308 | 0.0382 |
+| 0.009         | 27.6923 | 9000  | 0.3411          | 0.2302 | 0.0384 |
+| 0.0085        | 30.0    | 9750  | 0.3311          | 0.2292 | 0.0375 |
+| 0.006         | 32.3077 | 10500 | 0.3492          | 0.2238 | 0.0371 |
+| 0.0063        | 34.6154 | 11250 | 0.3560          | 0.2330 | 0.0381 |
+| 0.0064        | 36.9231 | 12000 | 0.3584          | 0.2259 | 0.0379 |
+| 0.0054        | 39.2308 | 12750 | 0.3484          | 0.2123 | 0.0351 |
+| 0.0041        | 41.5385 | 13500 | 0.3565          | 0.2131 | 0.0356 |
+| 0.0044        | 43.8462 | 14250 | 0.3522          | 0.2171 | 0.0363 |
+| 0.0025        | 46.1538 | 15000 | 0.3702          | 0.2084 | 0.0350 |
+| 0.0073        | 48.4615 | 15750 | 0.3579          | 0.2203 | 0.0360 |
+| 0.0048        | 50.7692 | 16500 | 0.3462          | 0.2116 | 0.0353 |
+| 0.0053        | 53.0769 | 17250 | 0.3264          | 0.2014 | 0.0337 |
+| 0.0028        | 55.3846 | 18000 | 0.3560          | 0.2059 | 0.0343 |
+| 0.0039        | 57.6923 | 18750 | 0.3685          | 0.2081 | 0.0348 |
+| 0.0026        | 60.0    | 19500 | 0.3649          | 0.2075 | 0.0347 |
+| 0.0027        | 62.3077 | 20250 | 0.3636          | 0.2091 | 0.0350 |
+| 0.0038        | 64.6154 | 21000 | 0.3675          | 0.2147 | 0.0350 |
+| 0.0024        | 66.9231 | 21750 | 0.3707          | 0.2050 | 0.0341 |
+| 0.0045        | 69.2308 | 22500 | 0.3397          | 0.1961 | 0.0329 |
+| 0.0032        | 71.5385 | 23250 | 0.3645          | 0.1985 | 0.0332 |
+| 0.0041        | 73.8462 | 24000 | 0.3451          | 0.2047 | 0.0338 |
+| 0.0018        | 76.1538 | 24750 | 0.3468          | 0.1935 | 0.0321 |
+| 0.0045        | 78.4615 | 25500 | 0.3366          | 0.1982 | 0.0332 |
+| 0.0023        | 80.7692 | 26250 | 0.3551          | 0.1996 | 0.0336 |
+| 0.0022        | 83.0769 | 27000 | 0.3778          | 0.1948 | 0.0331 |
+| 0.0026        | 85.3846 | 27750 | 0.3622          | 0.1950 | 0.0328 |
+| 0.0013        | 87.6923 | 28500 | 0.3600          | 0.1908 | 0.0319 |
+| 0.0032        | 90.0    | 29250 | 0.3632          | 0.1945 | 0.0324 |
+| 0.0027        | 92.3077 | 30000 | 0.3436          | 0.1913 | 0.0320 |
+| 0.002         | 94.6154 | 30750 | 0.3721          | 0.1985 | 0.0334 |
+| 0.0022        | 96.9231 | 31500 | 0.3659          | 0.1966 | 0.0330 |
+| 0.0025        | 99.2308 | 32250 | 0.3638          | 0.1946 | 0.0323 |
+### Framework versions
+- Transformers 4.41.2
+- Pytorch 2.3.0+cu121
+- Datasets 2.20.0
+- Tokenizers 0.19.1

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