Distilbert-finetuned-emotion

Distilbert is a variant of bert model(one of LLM models). This model with a classification head is used to classify the emotions of the input tweet. This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset. It achieves the following results on the evaluation set:

• Loss: 0.2195
• Accuracy: 0.9235
• F1: 0.9233

Emotion Labels

• label_0: Sadness
• label_1: Joy
• label_2: Love
• label_3: Anger
• label_4: Fear
• label_5: Surprise

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e-05
• train_batch_size: 64
• eval_batch_size: 64
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 2

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
0.8537	1.0	250	0.3235	0.897	0.8958
0.2506	2.0	500	0.2195	0.9235	0.9233

Validation metrics

• test_loss : 0.2194512039422989
• test_accuracy : 0.9235
• test_f1 : 0.923296474937779

Framework versions

• Transformers 4.37.2
• Pytorch 2.1.0+cu121
• Datasets 2.16.1
• Tokenizers 0.15.1