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README.md
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---
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library_name: transformers
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license: apache-2.0
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base_model: ntu-spml/distilhubert
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tags:
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- generated_from_trainer
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datasets:
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- common_language
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metrics:
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- accuracy
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model-index:
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- name: demo_LID_ntu-spml_distilhubert
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results:
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- task:
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name: Audio Classification
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type: audio-classification
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dataset:
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name: common_language
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type: common_language
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config: full
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split: validation
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args: full
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.6554008152173914
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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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# demo_LID_ntu-spml_distilhubert
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This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the common_language dataset.
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It achieves the following results on the evaluation set:
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- Loss: 2.2545
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- Accuracy: 0.6554
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 0.0003
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- train_batch_size: 8
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- eval_batch_size: 1
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- seed: 0
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- gradient_accumulation_steps: 4
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- total_train_batch_size: 32
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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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- num_epochs: 10.0
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- mixed_precision_training: Native AMP
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy |
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|:-------------:|:------:|:----:|:---------------:|:--------:|
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| 9.6557 | 0.9989 | 693 | 2.6549 | 0.2614 |
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| 6.1707 | 1.9989 | 1386 | 1.8478 | 0.4681 |
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| 3.7871 | 2.9989 | 2079 | 1.6941 | 0.5474 |
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| 2.7966 | 3.9989 | 2772 | 1.8580 | 0.5579 |
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| 1.5871 | 4.9989 | 3465 | 1.6663 | 0.6140 |
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| 0.7355 | 5.9989 | 4158 | 1.9491 | 0.6155 |
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| 0.4492 | 6.9989 | 4851 | 2.0594 | 0.6379 |
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| 0.1528 | 7.9989 | 5544 | 2.1739 | 0.6403 |
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| 0.0468 | 8.9989 | 6237 | 2.3125 | 0.6505 |
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| 0.0045 | 9.9989 | 6930 | 2.2545 | 0.6554 |
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### Framework versions
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- Transformers 4.47.1
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- Pytorch 2.5.1+cu124
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- Datasets 3.2.0
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- Tokenizers 0.21.0
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