mehmet0sahinn/distilbert-emotion

distilbert-sentiment-analysis

This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.1395
• Accuracy: 0.933

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: 5e-05
• train_batch_size: 128
• eval_batch_size: 128
• seed: 42
• optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• num_epochs: 4

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	1.0	125	0.2232	0.9215
No log	2.0	250	0.1552	0.9385
No log	3.0	375	0.1469	0.9375
0.2724	4.0	500	0.1395	0.933

Framework versions

• Transformers 4.51.1
• Pytorch 2.5.1+cu124
• Datasets 3.5.0
• Tokenizers 0.21.0

📓 Notebook & Demo

• Kaggle Notebook: Sentiment Analysis with DistilBERT
• This notebook contains the full training and evaluation pipeline for this model. Feel free to fork and experiment!