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End of training
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README.md
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---
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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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- speech-commands
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metrics:
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- accuracy
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model-index:
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- name: distilhubert-finetuned-speech_commands
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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: Speech_command_RK
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type: speech-commands
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.9975728155339806
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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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# distilhubert-finetuned-speech_commands
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This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the Speech_command_RK dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.1895
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- Accuracy: 0.9976
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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: 5e-05
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- train_batch_size: 264
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- eval_batch_size: 264
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- seed: 42
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- lr_scheduler_warmup_ratio: 0.1
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- num_epochs: 5
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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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| 2.1911 | 1.0 | 25 | 1.9352 | 0.8859 |
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| 1.0366 | 2.0 | 50 | 0.8334 | 0.9915 |
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| 0.4879 | 3.0 | 75 | 0.3774 | 0.9939 |
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| 0.297 | 4.0 | 100 | 0.2254 | 0.9951 |
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| 0.2422 | 5.0 | 125 | 0.1895 | 0.9976 |
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### Framework versions
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- Transformers 4.40.0
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- Pytorch 2.3.0+cu121
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- Datasets 2.19.0
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- Tokenizers 0.19.1
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runs/Apr30_16-48-52_4d444b023f0e/events.out.tfevents.1714496355.4d444b023f0e.6780.2
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version https://git-lfs.github.com/spec/v1
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oid sha256:29963e3715143c7c39f8eb5015306d569e3717a980f28a1ca49fb54c7cba16b8
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size 722
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