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w2v-bert-2.0-luo_19_77h

Automatic Speech Recognition
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wav2vec2-bert
CLEAR-Global/luo_19_77h
Generated from Trainer
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README.md ADDED
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+ ---
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+ library_name: transformers
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+ license: mit
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+ base_model: facebook/w2v-bert-2.0
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+ tags:
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+ - generated_from_trainer
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+ metrics:
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+ - wer
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+ model-index:
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+ - name: w2v-bert-2.0-luo_19_77h
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+ results: []
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # w2v-bert-2.0-luo_19_77h
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+
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+ 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.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.2966
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+ - Wer: 0.2751
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+ - Cer: 0.0920
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 3e-05
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+ - train_batch_size: 32
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+ - eval_batch_size: 32
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+ - seed: 42
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+ - gradient_accumulation_steps: 2
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+ - total_train_batch_size: 64
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+ - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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+ - lr_scheduler_type: linear
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+ - lr_scheduler_warmup_ratio: 0.1
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+ - training_steps: 100000
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+ - mixed_precision_training: Native AMP
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
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+ |:-------------:|:-------:|:-----:|:---------------:|:------:|:------:|
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+ | 0.469 | 0.8446 | 1000 | 0.8816 | 0.6085 | 0.1993 |
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+ | 0.1171 | 1.6892 | 2000 | 0.6389 | 0.4184 | 0.1531 |
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+ | 0.0837 | 2.5338 | 3000 | 0.5226 | 0.3805 | 0.1395 |
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+ | 0.1017 | 3.3784 | 4000 | 0.3857 | 0.3532 | 0.1133 |
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+ | 0.036 | 4.2230 | 5000 | 0.3766 | 0.3457 | 0.1161 |
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+ | 0.0509 | 5.0676 | 6000 | 0.3433 | 0.3408 | 0.1149 |
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+ | 0.0444 | 5.9122 | 7000 | 0.2983 | 0.3082 | 0.0981 |
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+ | 0.0704 | 6.7568 | 8000 | 0.2803 | 0.2972 | 0.0968 |
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+ | 0.0516 | 7.6014 | 9000 | 0.3242 | 0.2932 | 0.1006 |
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+ | 0.0362 | 8.4459 | 10000 | 0.2760 | 0.3047 | 0.0967 |
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+ | 0.0253 | 9.2905 | 11000 | 0.2727 | 0.2782 | 0.0908 |
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+ | 0.026 | 10.1351 | 12000 | 0.2789 | 0.2959 | 0.1049 |
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+ | 0.0274 | 10.9797 | 13000 | 0.2542 | 0.2782 | 0.0922 |
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+ | 0.0218 | 11.8243 | 14000 | 0.2694 | 0.2646 | 0.0904 |
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+ | 0.0201 | 12.6689 | 15000 | 0.2575 | 0.3007 | 0.0922 |
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+ | 0.0201 | 13.5135 | 16000 | 0.2419 | 0.2901 | 0.0896 |
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+ | 0.0216 | 14.3581 | 17000 | 0.2478 | 0.2795 | 0.0933 |
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+ | 0.0079 | 15.2027 | 18000 | 0.2974 | 0.2844 | 0.0890 |
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+ | 0.0352 | 16.0473 | 19000 | 0.2596 | 0.2959 | 0.0930 |
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+ | 0.0302 | 16.8919 | 20000 | 0.2831 | 0.2491 | 0.0849 |
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+ | 0.0115 | 17.7365 | 21000 | 0.2966 | 0.2751 | 0.0920 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.48.1
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+ - Pytorch 2.6.0+cu124
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+ - Datasets 3.5.0
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+ - Tokenizers 0.21.1
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