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

{'trainruntime': 60.0918, 'trainsamplespersecond': 41.603, 'trainstepspersecond': 0.166, 'trainloss': 0.6561894416809082, 'epoch': 5.0}

Time: 60.09

Samples/second: 41.60

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norbert2sentimentnorecengpu500radermaxnoder_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
• —trainbatchsize: 32
• —evalbatchsize: 64
• —seed: 42
• —gradientaccumulationsteps: 8
• —totaltrainbatch_size: 256
• —optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• —lrschedulertype: linear
• —num_epochs: 5

Training results

Framework versions

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