End of training
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README.md
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@@ -11,7 +11,7 @@ metrics:
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- recall
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- f1
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model-index:
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-
- name: distilhubert-finetuned-speech_commands-finetuned-
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results:
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- task:
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name: Audio Classification
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name: audiofolder
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type: audiofolder
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config: default
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split:
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args: default
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metrics:
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- name: Precision
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type: precision
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value: 0.
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- name: Recall
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type: recall
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value: 0.
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- name: F1
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type: f1
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value: 0.
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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-finetuned-
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This model is a fine-tuned version of [imrajeshkr/distilhubert-finetuned-speech_commands](https://huggingface.co/imrajeshkr/distilhubert-finetuned-speech_commands) on the audiofolder dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.
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- Precision: 0.
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- Recall: 0.
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- F1: 0.
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## Model description
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|
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| 0.
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### Framework versions
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- recall
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- f1
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model-index:
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- name: distilhubert-finetuned-speech_commands-finetuned-englishalphabets-classification
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results:
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- task:
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name: Audio Classification
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name: audiofolder
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type: audiofolder
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config: default
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split: validation
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args: default
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metrics:
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- name: Precision
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type: precision
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value: 0.9855379188712521
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- name: Recall
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type: recall
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value: 0.9827160493827161
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- name: F1
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type: f1
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value: 0.9825011002108681
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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-finetuned-englishalphabets-classification
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This model is a fine-tuned version of [imrajeshkr/distilhubert-finetuned-speech_commands](https://huggingface.co/imrajeshkr/distilhubert-finetuned-speech_commands) on the audiofolder dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.0563
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- Precision: 0.9855
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- Recall: 0.9827
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- F1: 0.9825
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## Model description
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|
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| 0.2886 | 1.0 | 1216 | 0.2477 | 0.9098 | 0.9284 | 0.9127 |
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| 0.1426 | 2.0 | 2432 | 0.0792 | 0.9835 | 0.9802 | 0.9795 |
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| 0.0678 | 3.0 | 3648 | 0.0601 | 0.9827 | 0.9790 | 0.9780 |
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| 0.0062 | 4.0 | 4864 | 0.0601 | 0.9848 | 0.9815 | 0.9812 |
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| 0.027 | 5.0 | 6080 | 0.0563 | 0.9855 | 0.9827 | 0.9825 |
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### Framework versions
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