indobertweet-base-uncased-emotion-recognition

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. -->

indobertweet-base-uncased-emotion-recognition

Model description

This model is a fine-tuned version of indolem/indobertweet-base-uncased on The PRDECT-ID Dataset, it is a compilation of Indonesian product reviews that come with emotion and sentiment labels. These reviews were gathered from one of Indonesia's largest e-commerce platforms, Tokopedia. It achieves the following results on the evaluation set:

• Loss: 0.6762
• Accuracy: 0.6981
• Precision: 0.7022
• Recall: 0.6981
• F1: 0.6963

It has been trained to classify text into six different emotion categories: happy, sadness, anger, love, and fear.

Training and evaluation data

I split my dataframe df into training, validation, and testing sets (train_df, val_df, test_df) using the train_test_split function from sklearn.model_selection. I set the test size to 20% for the initial split and further divided the remaining data equally between validation and testing sets. This process ensures that each split (val_df and test_df) maintains the same class distribution as the original dataset (stratify=df['label']).

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e-05
• train_batch_size: 16
• eval_batch_size: 64
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 5
• mixed_precision_training: Native AMP

Training results

Framework versions

• Transformers 4.41.2
• Pytorch 2.1.2
• Datasets 2.19.2
• Tokenizers 0.19.1