WAXAL Sog Asr W2v Bert 2.0

--- library_name: transformers license: mit base_model: facebook/w2v-bert-2.0 tags: - generated_from_trainer model-index: - name: WAXAL-sog_asr-w2v-bert-2.0 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. --> # WAXAL-sog_asr-w2v-bert-2.0 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: 0.9170 - Cer: 0.1860 ## 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: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Cer | |:-------------:|:------:|:----:|:---------------:|:------:| | 3.5241 | 2.2222 | 300 | 0.8205 | 0.2086 | | 0.6695 | 4.4444 | 600 | 0.7797 | 0.1980 | | 0.4814 | 6.6667 | 900 | 0.7923 | 0.1877 | | 0.3031 | 8.8889 | 1200 | 0.8854 | 0.1853 | | 0.3031 | 10.0 | 1350 | 0.9170 | 0.1860 | ### Framework versions - Transformers 5.12.1 - Pytorch 2.11.0+cu128 - Datasets 4.0.0 - Tokenizers 0.22.2

Source context: 5 downloads · 0 likes · Pipeline automatic-speech-recognition · Library transformers · Repo dhasmana/WAXAL-sog_asr-w2v-bert-2.0