SciBERT_TwoWayLoss_25K_bs64_P10_N5

This model is a fine-tuned version of allenai/scibert_scivocab_uncased on the None dataset. It achieves the following results on the evaluation set:

• Loss: 15.1250
• Accuracy: 0.7066
• Precision: 0.0321
• Recall: 0.9982
• F1: 0.0622
• Hamming: 0.2934

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: 64
• eval_batch_size: 64
• 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: 25000

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1	Hamming
28.5732	0.16	5000	26.4288	0.6945	0.0307	0.9910	0.0595	0.3055
19.8755	0.32	10000	18.9620	0.7010	0.0315	0.9959	0.0610	0.2990
17.1294	0.47	15000	16.5587	0.7021	0.0316	0.9970	0.0613	0.2979
15.8209	0.63	20000	15.4919	0.7053	0.0320	0.9982	0.0620	0.2947
15.4304	0.79	25000	15.1250	0.7066	0.0321	0.9982	0.0622	0.2934

Framework versions

• Transformers 4.33.3
• Pytorch 2.2.0.dev20231002
• Datasets 2.7.1
• Tokenizers 0.13.3