bert-uncased-massive-intent-classification-finetuned-banking

This model is a fine-tuned version of gokuls/bert-uncased-massive-intent-classification on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 2.5965
• Accuracy: 0.12

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: 2e-05
• train_batch_size: 6
• eval_batch_size: 6
• 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	Accuracy
2.731	1.0	3	2.6423	0.1067
2.4424	2.0	6	2.6178	0.1067
2.2005	3.0	9	2.6028	0.1111
2.1954	4.0	12	2.5965	0.12
2.0599	5.0	15	2.5935	0.12

Framework versions

• Transformers 4.24.0
• Pytorch 1.12.1
• Datasets 2.3.2
• Tokenizers 0.12.1