---
library_name: transformers
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
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: 0.2255
- Accuracy: 0.9883
- F1: 0.9883
- Precision: 0.9883
- Recall: 0.9883
- Confusion Matrix: [[960, 10], [6, 389]]

## 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: 64
- eval_batch_size: 64
- seed: 123
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20
- label_smoothing_factor: 0.1

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy | F1     | Precision | Recall | Confusion Matrix       |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:----------------------:|
| 0.3124        | 2.3256  | 100  | 0.2739          | 0.9641   | 0.9640 | 0.9640    | 0.9641 | [[948, 22], [27, 368]] |
| 0.2337        | 4.6512  | 200  | 0.2385          | 0.9736   | 0.9737 | 0.9737    | 0.9736 | [[950, 20], [16, 379]] |
| 0.2064        | 6.9767  | 300  | 0.2295          | 0.9832   | 0.9832 | 0.9832    | 0.9832 | [[958, 12], [11, 384]] |
| 0.2023        | 9.3023  | 400  | 0.2277          | 0.9868   | 0.9869 | 0.9870    | 0.9868 | [[957, 13], [5, 390]]  |
| 0.2003        | 11.6279 | 500  | 0.2254          | 0.9875   | 0.9876 | 0.9876    | 0.9875 | [[960, 10], [7, 388]]  |
| 0.2002        | 13.9535 | 600  | 0.2259          | 0.9875   | 0.9876 | 0.9876    | 0.9875 | [[959, 11], [6, 389]]  |
| 0.1994        | 16.2791 | 700  | 0.2255          | 0.9883   | 0.9883 | 0.9883    | 0.9883 | [[960, 10], [6, 389]]  |
| 0.1997        | 18.6047 | 800  | 0.2254          | 0.9883   | 0.9883 | 0.9883    | 0.9883 | [[960, 10], [6, 389]]  |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Tokenizers 0.19.1