Bajiyo
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w2v-bert-2_7_datasets

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 ---
 library_name: transformers
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 ---
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 ---
 library_name: transformers
+license: mit
+base_model: facebook/w2v-bert-2.0
+tags:
+- generated_from_trainer
+datasets:
+- common_voice_17_0
+metrics:
+- wer
+model-index:
+- name: w2v-bert-2_6_datasets
+  results:
+  - task:
+      name: Automatic Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: common_voice_17_0
+      type: common_voice_17_0
+      config: ml
+      split: validation
+      args: ml
+    metrics:
+    - name: Wer
+      type: wer
+      value: 0.43922053819981444
 ---
+<!-- 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. -->
+# w2v-bert-2_6_datasets
+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: 0.5077
+- Wer: 0.4392
+## 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
+- lr_scheduler_warmup_steps: 500
+- num_epochs: 10
+- mixed_precision_training: Native AMP
+### Training results
+| Training Loss | Epoch  | Step  | Validation Loss | Wer    |
+|:-------------:|:------:|:-----:|:---------------:|:------:|
+| 1.1114        | 0.4038 | 600   | 0.6364          | 0.6514 |
+| 0.1782        | 0.8075 | 1200  | 0.5620          | 0.6127 |
+| 0.1374        | 1.2113 | 1800  | 0.4943          | 0.5654 |
+| 0.1156        | 1.6151 | 2400  | 0.4415          | 0.5376 |
+| 0.1068        | 2.0188 | 3000  | 0.4187          | 0.5249 |
+| 0.0838        | 2.4226 | 3600  | 0.4778          | 0.5320 |
+| 0.0834        | 2.8264 | 4200  | 0.4186          | 0.5091 |
+| 0.0703        | 3.2301 | 4800  | 0.4538          | 0.5363 |
+| 0.0636        | 3.6339 | 5400  | 0.4287          | 0.5314 |
+| 0.0609        | 4.0377 | 6000  | 0.4013          | 0.4989 |
+| 0.0462        | 4.4415 | 6600  | 0.4053          | 0.4964 |
+| 0.047         | 4.8452 | 7200  | 0.4289          | 0.4766 |
+| 0.0377        | 5.2490 | 7800  | 0.3875          | 0.4933 |
+| 0.0352        | 5.6528 | 8400  | 0.3906          | 0.4881 |
+| 0.033         | 6.0565 | 9000  | 0.4192          | 0.4667 |
+| 0.0243        | 6.4603 | 9600  | 0.4113          | 0.4723 |
+| 0.0244        | 6.8641 | 10200 | 0.4393          | 0.4708 |
+| 0.0189        | 7.2678 | 10800 | 0.4255          | 0.4630 |
+| 0.0167        | 7.6716 | 11400 | 0.4219          | 0.4646 |
+| 0.0157        | 8.0754 | 12000 | 0.4398          | 0.4429 |
+| 0.0107        | 8.4791 | 12600 | 0.4546          | 0.4507 |
+| 0.0095        | 8.8829 | 13200 | 0.4949          | 0.4426 |
+| 0.0072        | 9.2867 | 13800 | 0.4972          | 0.4473 |
+| 0.0059        | 9.6904 | 14400 | 0.5077          | 0.4392 |
+### Framework versions
+- Transformers 4.44.2
+- Pytorch 2.1.1+cu121
+- Datasets 2.20.0
+- Tokenizers 0.19.1