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Distilhubert Finetuned Babycry v7

This model is a fine-tuned version of 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...

Sourcehuggingface.co/qiaoen04/distilhubert-finetuned-babycry-v7/tree/f7e2fb52bd777c070693249f6de221d0bede82b3
TypeOtherModèle de basentu-spml/distilhubertVersionRevision f7e2fb5CClasse

Source du modèle

Type
Other
Taille du fichier
90,4 MB
Modèle de base
ntu-spml/distilhubert
Version
vRevision f7e2fb5
Mis à jour
22 sept. 2026
Téléchargements
4
Source
Hugging Face

Description de la source

distilhubert-finetuned-babycry-v7

This model is a fine-tuned version of 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 LossEpochStepValidation LossAccuracyF1PrecisionRecall
0.74170.5435250.5925{'accuracy': 0.8695652173913043}0.80890.75610.8696
0.72261.0870500.6167{'accuracy': 0.8695652173913043}0.80890.75610.8696
0.56061.6304750.6808{'accuracy': 0.8695652173913043}0.80890.75610.8696
0.88582.17391000.5850{'accuracy': 0.8695652173913043}0.80890.75610.8696
0.65732.71741250.5968{'accuracy': 0.8695652173913043}0.80890.75610.8696
0.79423.26091500.6142{'accuracy': 0.8695652173913043}0.80890.75610.8696
0.74973.80431750.5915{'accuracy': 0.8695652173913043}0.80890.75610.8696
0.74084.34782000.5899{'accuracy': 0.8695652173913043}0.80890.75610.8696
0.64994.89132250.5989{'accuracy': 0.8695652173913043}0.80890.75610.8696
0.67255.43482500.5865{'accuracy': 0.8695652173913043}0.80890.75610.8696
0.67975.97832750.5852{'accuracy': 0.8695652173913043}0.80890.75610.8696
0.65536.52173000.5861{'accuracy': 0.8695652173913043}0.80890.75610.8696
0.65357.06523250.5863{'accuracy': 0.8695652173913043}0.80890.75610.8696
0.72977.60873500.5865{'accuracy': 0.8695652173913043}0.80890.75610.8696

Framework versions

  • Transformers 4.44.2
  • Pytorch 2.4.1+cu121
  • Tokenizers 0.19.1

Sources

1 source
Hugging FacePrincipalVersion f7e2fb52bd777c070693249f6de221d0bede82b3
huggingface.co/qiaoen04/distilhubert-finetuned-babycry-v7/tree/f7e2fb52bd777c070693249f6de221d0bede82b3

Vérifié 6 sept.

Artefacts du modèle

1 artefact

model.safetensors

safetensors · 90,4 MB · SHA-256 7482a493b632…d964 · Hugging Face

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Extraits de sources

2 extraits
Readme6 sept.huggingface.co/qiaoen04/distilhubert-finetuned-babycry-v7

--- 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': 0.869565217...

Note6 sept.huggingface.co/qiaoen04/distilhubert-finetuned-babycry-v7

Source context: 4 downloads · 0 likes · Pipeline audio-classification · Library transformers · Repo qiaoen04/distilhubert-finetuned-babycry-v7