action-policy-plans-classifier

This model is a fine-tuned version of sentence-transformers/all-mpnet-base-v2 on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.6839
• Precision Micro: 0.7089
• Precision Weighted: 0.7043
• Precision Samples: 0.4047
• Recall Micro: 0.7066
• Recall Weighted: 0.7066
• Recall Samples: 0.4047
• F1-score: 0.4041

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: 2.915e-05
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 32
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 300
• num_epochs: 7

Training results

Training Loss	Epoch	Step	Validation Loss	Precision Micro	Precision Weighted	Precision Samples	Recall Micro	Recall Weighted	Recall Samples	F1-score
0.7333	1.0	253	0.5828	0.625	0.6422	0.4047	0.7098	0.7098	0.4065	0.4047
0.5905	2.0	506	0.5593	0.6292	0.6318	0.4437	0.7760	0.7760	0.4446	0.4434
0.4934	3.0	759	0.5269	0.6630	0.6637	0.4319	0.7571	0.7571	0.4347	0.4325
0.4018	4.0	1012	0.5645	0.6449	0.6479	0.4456	0.7792	0.7792	0.4465	0.4453
0.3235	5.0	1265	0.6101	0.6964	0.6929	0.4220	0.7382	0.7382	0.4229	0.4217
0.2638	6.0	1518	0.6692	0.6888	0.6841	0.4111	0.7192	0.7192	0.4120	0.4108
0.2197	7.0	1771	0.6839	0.7089	0.7043	0.4047	0.7066	0.7066	0.4047	0.4041

Framework versions

• Transformers 4.28.0
• Pytorch 2.0.1+cu118
• Datasets 2.12.0
• Tokenizers 0.13.3