NTCAL/norbert2_sentiment_norec_en_gpu_500_rader_max_noder_task

KJøretid

#SBATCH --nodes=2

#SBATCH --ntasks-per-node=3

#SBATCH --gres=gpu:A100m40:1

{'train_runtime': 60.0918, 'train_samples_per_second': 41.603, 'train_steps_per_second': 0.166, 'train_loss': 0.6561894416809082, 'epoch': 5.0}

Time: 60.09

Samples/second: 41.60

norbert2_sentiment_norec_en_gpu_500_rader_max_noder_task

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: 0.6280
• Compute Metrics: :
• Accuracy: 0.678
• Balanced Accuracy: 0.4889
• F1 Score: 0.8076
• Recall: 0.9713
• Precision: 0.6912

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	0.6324	:	0.696	0.5	0.8208	1.0	0.696
No log	2.0	4	0.6264	:	0.692	0.4971	0.8180	0.9943	0.6948
No log	3.0	6	0.6180	:	0.696	0.5	0.8208	1.0	0.696
No log	4.0	8	0.6236	:	0.694	0.5023	0.8185	0.9914	0.6970
0.6562	5.0	10	0.6280	:	0.678	0.4889	0.8076	0.9713	0.6912

Framework versions

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