mikr
/

w2v-bert-2.0-czech-colab-cv16

@@ -1,201 +1,91 @@
 ---
-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_16_0
+metrics:
+- wer
+model-index:
+- name: w2v-bert-2.0-czech-colab-cv16
+  results:
+  - task:
+      name: Automatic Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: common_voice_16_0
+      type: common_voice_16_0
+      config: cs
+      split: test
+      args: cs
+    metrics:
+    - name: Wer
+      type: wer
+      value: 0.05733702722973076
 ---
+<!-- 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-2.0-czech-colab-cv16
+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_16_0 dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1023
+- Wer: 0.0573
+## 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: 64
+- eval_batch_size: 8
+- seed: 42
+- 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    |
+|:-------------:|:-----:|:----:|:---------------:|:------:|
+| 1.5297        | 0.66  | 300  | 0.1448          | 0.1299 |
+| 0.0886        | 1.32  | 600  | 0.1353          | 0.1051 |
+| 0.0717        | 1.98  | 900  | 0.1157          | 0.0861 |
+| 0.0463        | 2.64  | 1200 | 0.0994          | 0.0759 |
+| 0.0404        | 3.3   | 1500 | 0.1054          | 0.0724 |
+| 0.0314        | 3.96  | 1800 | 0.0915          | 0.0694 |
+| 0.0227        | 4.63  | 2100 | 0.0926          | 0.0664 |
+| 0.0205        | 5.29  | 2400 | 0.0992          | 0.0652 |
+| 0.0161        | 5.95  | 2700 | 0.0932          | 0.0654 |
+| 0.0124        | 6.61  | 3000 | 0.0902          | 0.0629 |
+| 0.0097        | 7.27  | 3300 | 0.0970          | 0.0612 |
+| 0.0081        | 7.93  | 3600 | 0.0946          | 0.0602 |
+| 0.0054        | 8.59  | 3900 | 0.0962          | 0.0588 |
+| 0.0048        | 9.25  | 4200 | 0.1029          | 0.0579 |
+| 0.0034        | 9.91  | 4500 | 0.1023          | 0.0573 |
+### Framework versions
+- Transformers 4.38.0.dev0
+- Pytorch 2.2.0+cu121
+- Datasets 2.16.2.dev0
+- Tokenizers 0.15.1

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