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---
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 |