norbert2_sentiment_norec_8

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

• Loss: 0.5046
• Start: ------------------------------------------------------------
• Accuracy: 0.8
• Balanced Accuracy: 0.5
• F1 Score: 0.8889
• Recall: 1.0
• Precision: 0.8
• End: ------------------------------------------------------------

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: 5e-05
• train_batch_size: 1
• eval_batch_size: 1
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 1
• num_epochs: 4
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Start	Accuracy	Balanced Accuracy	F1 Score	Recall	Precision	End
0.4534	1.0	5	0.6422	------------------------------------------------------------	0.8	0.5	0.8889	1.0	0.8	------------------------------------------------------------
0.5493	2.0	10	0.5159	------------------------------------------------------------	0.8	0.5	0.8889	1.0	0.8	------------------------------------------------------------
0.3452	3.0	15	0.5453	------------------------------------------------------------	0.8	0.5	0.8889	1.0	0.8	------------------------------------------------------------
0.2336	4.0	20	0.5046	------------------------------------------------------------	0.8	0.5	0.8889	1.0	0.8	------------------------------------------------------------

Framework versions

• Transformers 4.26.0
• Pytorch 1.13.1+cu117
• Datasets 2.9.0
• Tokenizers 0.13.2