emotiscan_model_2

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

• Loss: 0.5213
• Accuracy: 0.8184

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: 16
• eval_batch_size: 16
• seed: 42
• distributed_type: multi-GPU
• num_devices: 2
• gradient_accumulation_steps: 4
• total_train_batch_size: 128
• total_eval_batch_size: 32
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.4986	1.0	4157	0.4979	0.8194
0.453	2.0	8315	0.4985	0.8200
0.4098	3.0	12471	0.5213	0.8184

Framework versions

• Transformers 4.29.0
• Pytorch 2.1.2
• Datasets 2.14.6
• Tokenizers 0.13.3