undersampled-review-clf

This model is a fine-tuned version of cardiffnlp/twitter-roberta-base-sentiment-latest on justina/yelp-boba-reviews dataset. Undersampling techniques were used to optimize the model for predicting Yelp review ratings.

It achieves the following results on the evaluation set:

• Loss: 0.4412
• F1 Macro: 0.7799
• Aucpr Macro: 0.8286
• Accuracy: 0.8464

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 1e-05
• train_batch_size: 16
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	F1 Macro	Aucpr Macro	Accuracy
0.9348	1.22	100	0.7286	0.6132	0.6244	0.6962
0.7438	2.44	200	0.7857	0.6232	0.6215	0.6735
0.6275	3.66	300	0.8317	0.5976	0.6092	0.6778
0.5561	4.88	400	0.8176	0.6200	0.6238	0.6868

Framework versions

• Transformers 4.32.1
• Pytorch 2.0.1+cu118
• Datasets 2.14.4
• Tokenizers 0.13.3