metadata

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: []

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: 0.5860
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
1.005	0.5435	25	0.6526	{'accuracy': 0.8695652173913043}	0.8089	0.7561	0.8696
0.7736	1.0870	50	0.6396	{'accuracy': 0.8695652173913043}	0.8089	0.7561	0.8696
0.5618	1.6304	75	0.6990	{'accuracy': 0.8695652173913043}	0.8089	0.7561	0.8696
0.8623	2.1739	100	0.5802	{'accuracy': 0.8695652173913043}	0.8089	0.7561	0.8696
0.6532	2.7174	125	0.6205	{'accuracy': 0.8695652173913043}	0.8089	0.7561	0.8696
0.8076	3.2609	150	0.6168	{'accuracy': 0.8695652173913043}	0.8089	0.7561	0.8696
0.7581	3.8043	175	0.5917	{'accuracy': 0.8695652173913043}	0.8089	0.7561	0.8696
0.7525	4.3478	200	0.5988	{'accuracy': 0.8695652173913043}	0.8089	0.7561	0.8696
0.6566	4.8913	225	0.5997	{'accuracy': 0.8695652173913043}	0.8089	0.7561	0.8696
0.6845	5.4348	250	0.5815	{'accuracy': 0.8695652173913043}	0.8089	0.7561	0.8696
0.6812	5.9783	275	0.5830	{'accuracy': 0.8695652173913043}	0.8089	0.7561	0.8696
0.6548	6.5217	300	0.5855	{'accuracy': 0.8695652173913043}	0.8089	0.7561	0.8696
0.6555	7.0652	325	0.5859	{'accuracy': 0.8695652173913043}	0.8089	0.7561	0.8696
0.7294	7.6087	350	0.5861	{'accuracy': 0.8695652173913043}	0.8089	0.7561	0.8696

Framework versions

Transformers 4.44.2
Pytorch 2.4.1+cu121
Tokenizers 0.19.1