test_implementation

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

• Loss: 0.6736
• Accuracy: 0.5926
• Precision: 0.0090
• Recall: 0.3751
• F1: 0.0177
• Hamming: 0.4074

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: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• training_steps: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1	Hamming
0.7077	0.0	5	0.6857	0.5529	0.0089	0.4040	0.0173	0.4471
0.6801	0.0	10	0.6736	0.5926	0.0090	0.3751	0.0177	0.4074

Framework versions

• Transformers 4.37.2
• Pytorch 1.12.1+cu113
• Datasets 2.16.1
• Tokenizers 0.15.1