NTCAL/norbert2_sentiment_norec_en_gpu_500_rader_9

Kjøretid

{'train_runtime': 291.2967, 'train_samples_per_second': 51.494, 'train_steps_per_second': 0.189, 'train_loss': 0.6998663252050227, 'epoch': 4.94}

Time: 291.30

Samples/second: 51.49

GPU memory occupied: 6267 MB.

norbert2_sentiment_norec_en_gpu_500_rader_9

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: 0.6255
• Compute Metrics: :
• Accuracy: 0.694
• Balanced Accuracy: 0.5023
• F1 Score: 0.8185
• Recall: 0.9914
• Precision: 0.6970

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: 32
• eval_batch_size: 64
• seed: 42
• gradient_accumulation_steps: 8
• total_train_batch_size: 256
• 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	Compute Metrics	Accuracy	Balanced Accuracy	F1 Score	Recall	Precision
No log	1.0	2	0.6208	:	0.684	0.5006	0.8101	0.9684	0.6963
No log	2.0	4	0.6262	:	0.692	0.4990	0.8175	0.9914	0.6956
No log	3.0	6	0.6338	:	0.672	0.4846	0.8034	0.9626	0.6893
No log	4.0	8	0.6240	:	0.698	0.5051	0.8213	0.9971	0.6982
0.6681	5.0	10	0.6255	:	0.694	0.5023	0.8185	0.9914	0.6970

Framework versions

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