mbert-lv-emotions-ekman

Fine-tuned Multilingual BERT for multi-label emotion classification task.

Model was trained on lv_emotions dataset. This dataset is Latvian translation of GoEmotions and Twitter Emotions dataset. Google Translate was used to generate the machine translation.

Original 26 emotions were mapped to 6 base emotions as per Dr. Ekman theory.

Labels predicted by classifier:

0: anger
1: disgust
2: fear
3: joy
4: sadness
5: surprise
6: neutral

Seed used for random number generator is 42:

def set_seed(seed=42):
    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    if torch.cuda.is_available():
        torch.cuda.manual_seed_all(seed)

Training parameters:

max_length: null
batch_size: 32
shuffle: True
num_workers: 4
pin_memory: False
drop_last: False
optimizer: adam
lr: 0.00001
weight_decay: 0
problem_type: multi_label_classification
num_epochs: 4

Evaluation results on test split of lv_go_emotions

Evaluation results on test split of lv_twitter_emotions