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---
library_name: transformers
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: distilhubert-finetuned-cry-detector
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilhubert-finetuned-cry-detector
This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0404
- Accuracy: 0.9905
- Precision: 0.9906
- Recall: 0.9905
- F1: 0.9905
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 123
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.001
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| No log | 0.9956 | 85 | 0.1607 | 0.9429 | 0.9455 | 0.9429 | 0.9435 |
| No log | 1.9912 | 170 | 0.1011 | 0.9670 | 0.9669 | 0.9670 | 0.9669 |
| No log | 2.9985 | 256 | 0.0582 | 0.9853 | 0.9857 | 0.9853 | 0.9854 |
| No log | 3.9941 | 341 | 0.0424 | 0.9883 | 0.9884 | 0.9883 | 0.9883 |
| No log | 4.9898 | 426 | 0.0489 | 0.9868 | 0.9870 | 0.9868 | 0.9869 |
| 0.0815 | 5.9971 | 512 | 0.0408 | 0.9883 | 0.9883 | 0.9883 | 0.9883 |
| 0.0815 | 6.9927 | 597 | 0.0395 | 0.9890 | 0.9891 | 0.9890 | 0.9890 |
| 0.0815 | 8.0 | 683 | 0.0400 | 0.9905 | 0.9906 | 0.9905 | 0.9905 |
| 0.0815 | 8.9956 | 768 | 0.0406 | 0.9905 | 0.9906 | 0.9905 | 0.9905 |
| 0.0815 | 9.9561 | 850 | 0.0404 | 0.9905 | 0.9906 | 0.9905 | 0.9905 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Tokenizers 0.19.1
|