The model has been trained to classify text inputs into distinct emotional categories based on the fine-tuned understanding of the emotions dataset. The fine-tuned model has demonstrated high accuracy and F1 scores on the evaluation set.

Intended uses & limitations

Intended Uses

Sentiment analysis
Emotional classification in text
Emotion-based recommendation systems

Limitations

May show biases based on the training dataset
Optimized for emotional classification and may not cover nuanced emotional subtleties

Training and evaluation data

Emotions dataset with labeled emotional categories here.

The emotional categories are as follows:

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: 32
eval_batch_size: 32
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
0.5929	1.0	500	0.2345	0.9185	0.9180
0.1642	2.0	1000	0.1716	0.9335	0.9342
0.1163	3.0	1500	0.1501	0.9405	0.9407
0.0911	4.0	2000	0.1698	0.933	0.9331
0.0741	5.0	2500	0.1926	0.932	0.9323
0.0559	6.0	3000	0.2033	0.935	0.9353
0.0464	7.0	3500	0.2156	0.935	0.9353
0.0335	8.0	4000	0.2354	0.9405	0.9408
0.0257	9.0	4500	0.2410	0.9395	0.9396
0.0214	10.0	5000	0.2428	0.9395	0.9396

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu118
Datasets 2.15.0
Tokenizers 0.15.0

agoor97/distilbert-base-uncased-finetuned-depi2-emotion-2

distilbert-base-uncased-finetuned-emotions-dataset

Model description