from transformers import pipeline

pipe = pipeline(model='minuva/MiniLMv2-goemotions-v2', task='text-classification')
pipe("I am angry")
# [{'label': 'anger', 'score': 0.9722517132759094}]

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 6e-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

Metrics (comparison with teacher model)

Teacher (params)	Student (params)	Set	Score (teacher)	Score (student)
tasinhoque/text-classification-goemotions (355M)	MiniLMv2-goemotions-v2 (30M)	Validation	0.514252	0.484898
tasinhoque/text-classification-goemotions (355M)	MiniLMv2-goemotions-v2 (30M)	Test	0.501937	0.486890

Deployment

Check out our fast-nlp-text-emotion repository for a FastAPI based server to easily deploy this model on CPU devices.