Keyurjotaniya007/minilm-emotion-en-v1

emotion-advance-classifier

This model is a fine-tuned version of microsoft/MiniLM-L12-H384-uncased on the emotion dataset. It achieves the following results on the evaluation set:

• Loss: 0.21979030966758728

• Accuracy: 0.929

• F1: 0.92925147890599

This model is trained and evaluated using 'emotion' dataset. A famous dataset from an article that explored how emotions are represented in English Twitter messages. Unlike most sentiment analysis datasets that involve just “positive” and “negative” polarities, this dataset con‐ tains six basic emotions: anger, love, fear, joy, sadness, and surprise

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e-05

• train_batch_size: 16

• eval_batch_size: 16

• seed: 42

• weight_decay: 0.01

• lr_scheduler_type: linear

• warmup_ratio: 0.1

• num_epochs: 2

Framework versions

• Transformers 4.31.0

• Pytorch 2.0.1+cu118

• Datasets 2.14.4

• Tokenizers 0.13.3