cibfaye
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w2v-bert-2.0-wol-v1

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 ---
-library_name: transformers
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 ---
+license: mit
+base_model: facebook/w2v-bert-2.0
+tags:
+- generated_from_trainer
+metrics:
+- wer
+model-index:
+- name: w2v-bert-2.0-wol-v1
+  results: []
 ---
+<!-- 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-wol-v1
+This model is a fine-tuned version of [facebook/w2v-bert-2.0](https://huggingface.co/facebook/w2v-bert-2.0) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1008
+- Wer: 0.0792
+## 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    |
+|:-------------:|:------:|:----:|:---------------:|:------:|
+| 1.6351        | 0.6857 | 300  | 0.2974          | 0.3040 |
+| 0.4591        | 1.3714 | 600  | 0.2215          | 0.2307 |
+| 0.3833        | 2.0571 | 900  | 0.1950          | 0.1900 |
+| 0.329         | 2.7429 | 1200 | 0.1637          | 0.1614 |
+| 0.2797        | 3.4286 | 1500 | 0.1515          | 0.1479 |
+| 0.2558        | 4.1143 | 1800 | 0.1435          | 0.1337 |
+| 0.2166        | 4.8    | 2100 | 0.1296          | 0.1295 |
+| 0.1876        | 5.4857 | 2400 | 0.1178          | 0.1129 |
+| 0.1695        | 6.1714 | 2700 | 0.1107          | 0.1005 |
+| 0.137         | 6.8571 | 3000 | 0.1064          | 0.0933 |
+| 0.1078        | 7.5429 | 3300 | 0.1049          | 0.0929 |
+| 0.0904        | 8.2286 | 3600 | 0.1002          | 0.0871 |
+| 0.0685        | 8.9143 | 3900 | 0.0973          | 0.0810 |
+| 0.049         | 9.6    | 4200 | 0.1008          | 0.0792 |
+### Framework versions
+- Transformers 4.41.2
+- Pytorch 2.3.1+cu121
+- Datasets 2.19.2
+- Tokenizers 0.19.1

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