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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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- audiofolder
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metrics:
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- accuracy
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- f1
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- precision
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- recall
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model-index:
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- name: distilhubert-finetuned-donateacry
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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: audiofolder
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type: audiofolder
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config: default
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split: train
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args: default
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.7317073170731707
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- name: F1
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type: f1
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value: 0.6183442116111302
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- name: Precision
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type: precision
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value: 0.5353955978584176
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- name: Recall
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type: recall
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value: 0.7317073170731707
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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-donateacry
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This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the audiofolder dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.0007
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- Accuracy: 0.7317
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- F1: 0.6183
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- Precision: 0.5354
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- Recall: 0.7317
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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: 0.0005
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- train_batch_size: 8
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- eval_batch_size: 8
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- seed: 42
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- gradient_accumulation_steps: 8
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- total_train_batch_size: 64
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: cosine
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- lr_scheduler_warmup_ratio: 0.001
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- num_epochs: 2
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
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|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
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| No log | 0.9032 | 7 | 1.0816 | 0.7317 | 0.6183 | 0.5354 | 0.7317 |
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| No log | 1.8065 | 14 | 1.0007 | 0.7317 | 0.6183 | 0.5354 | 0.7317 |
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### Framework versions
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- Transformers 4.44.2
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- Pytorch 2.4.0+cu118
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- Datasets 2.21.0
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- Tokenizers 0.19.1
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