---
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-babycry-v7
  results: []
datasets:
- Nooon/Donate_a_cry
---

<!-- 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-babycry-v7

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: 0.5864
- Accuracy: {'accuracy': 0.8695652173913043}
- F1: 0.8089
- Precision: 0.7561
- Recall: 0.8696

## 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: 0.001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 8

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy                         | F1     | Precision | Recall |
|:-------------:|:------:|:----:|:---------------:|:--------------------------------:|:------:|:---------:|:------:|
| 0.7417        | 0.5435 | 25   | 0.5925          | {'accuracy': 0.8695652173913043} | 0.8089 | 0.7561    | 0.8696 |
| 0.7226        | 1.0870 | 50   | 0.6167          | {'accuracy': 0.8695652173913043} | 0.8089 | 0.7561    | 0.8696 |
| 0.5606        | 1.6304 | 75   | 0.6808          | {'accuracy': 0.8695652173913043} | 0.8089 | 0.7561    | 0.8696 |
| 0.8858        | 2.1739 | 100  | 0.5850          | {'accuracy': 0.8695652173913043} | 0.8089 | 0.7561    | 0.8696 |
| 0.6573        | 2.7174 | 125  | 0.5968          | {'accuracy': 0.8695652173913043} | 0.8089 | 0.7561    | 0.8696 |
| 0.7942        | 3.2609 | 150  | 0.6142          | {'accuracy': 0.8695652173913043} | 0.8089 | 0.7561    | 0.8696 |
| 0.7497        | 3.8043 | 175  | 0.5915          | {'accuracy': 0.8695652173913043} | 0.8089 | 0.7561    | 0.8696 |
| 0.7408        | 4.3478 | 200  | 0.5899          | {'accuracy': 0.8695652173913043} | 0.8089 | 0.7561    | 0.8696 |
| 0.6499        | 4.8913 | 225  | 0.5989          | {'accuracy': 0.8695652173913043} | 0.8089 | 0.7561    | 0.8696 |
| 0.6725        | 5.4348 | 250  | 0.5865          | {'accuracy': 0.8695652173913043} | 0.8089 | 0.7561    | 0.8696 |
| 0.6797        | 5.9783 | 275  | 0.5852          | {'accuracy': 0.8695652173913043} | 0.8089 | 0.7561    | 0.8696 |
| 0.6553        | 6.5217 | 300  | 0.5861          | {'accuracy': 0.8695652173913043} | 0.8089 | 0.7561    | 0.8696 |
| 0.6535        | 7.0652 | 325  | 0.5863          | {'accuracy': 0.8695652173913043} | 0.8089 | 0.7561    | 0.8696 |
| 0.7297        | 7.6087 | 350  | 0.5865          | {'accuracy': 0.8695652173913043} | 0.8089 | 0.7561    | 0.8696 |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Tokenizers 0.19.1