README.md

metadata

license: apache-2.0
base_model: facebook/bart-base
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - precision
  - recall
model-index:
  - name: bart-base-lora
    results: []

bart-base-lora

This model is a fine-tuned version of facebook/bart-base on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.6655
• Accuracy: 0.7963
• Precision: 0.7841
• Recall: 0.7963
• Precision Macro: 0.5968
• Recall Macro: 0.6325
• Macro Fpr: 0.0186
• Weighted Fpr: 0.0179
• Weighted Specificity: 0.9749
• Macro Specificity: 0.9847
• Weighted Sensitivity: 0.7963
• Macro Sensitivity: 0.6325
• F1 Micro: 0.7963
• F1 Macro: 0.6074
• F1 Weighted: 0.7859

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: 5e-05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 32
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 15

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	Precision Macro	Recall Macro	Macro Fpr	Weighted Fpr	Weighted Specificity	Macro Specificity	Weighted Sensitivity	Macro Sensitivity	F1 Micro	F1 Macro	F1 Weighted
No log	1.0	160	1.2642	0.6313	0.5477	0.6313	0.3009	0.3127	0.0428	0.0400	0.9351	0.9711	0.6313	0.3127	0.6313	0.2941	0.5769
No log	2.0	321	0.8962	0.7119	0.6939	0.7119	0.3937	0.4525	0.0285	0.0281	0.9669	0.9786	0.7119	0.4525	0.7119	0.4107	0.6960
No log	3.0	482	0.8204	0.7196	0.6953	0.7196	0.3974	0.4468	0.0278	0.0271	0.9653	0.9790	0.7196	0.4468	0.7196	0.3998	0.6885
1.2731	4.0	643	0.7519	0.7436	0.7186	0.7436	0.4131	0.4673	0.0244	0.0240	0.9695	0.9809	0.7436	0.4673	0.7436	0.4272	0.7248
1.2731	5.0	803	0.7364	0.7475	0.7524	0.7475	0.6132	0.5050	0.0243	0.0236	0.9679	0.9810	0.7475	0.5050	0.7475	0.4905	0.7286
1.2731	6.0	964	0.7273	0.7514	0.7423	0.7514	0.5784	0.5258	0.0237	0.0231	0.9699	0.9814	0.7514	0.5258	0.7514	0.5150	0.7311
0.7243	7.0	1125	0.6993	0.7645	0.7478	0.7645	0.5498	0.5565	0.0222	0.0215	0.9721	0.9824	0.7645	0.5565	0.7645	0.5453	0.7538
0.7243	8.0	1286	0.6952	0.7769	0.7639	0.7769	0.5682	0.5888	0.0207	0.0201	0.9731	0.9833	0.7769	0.5888	0.7769	0.5700	0.7649
0.7243	9.0	1446	0.6759	0.7823	0.7708	0.7823	0.5764	0.5877	0.0201	0.0195	0.9739	0.9838	0.7823	0.5877	0.7823	0.5699	0.7697
0.6098	10.0	1607	0.6705	0.7847	0.7720	0.7847	0.5899	0.6176	0.0199	0.0192	0.9732	0.9839	0.7847	0.6176	0.7847	0.5935	0.7724
0.6098	11.0	1768	0.6794	0.7909	0.7737	0.7909	0.5882	0.6237	0.0193	0.0185	0.9736	0.9843	0.7909	0.6237	0.7909	0.5988	0.7773
0.6098	12.0	1929	0.6836	0.7909	0.7816	0.7909	0.5973	0.6285	0.0192	0.0185	0.9742	0.9843	0.7909	0.6285	0.7909	0.6034	0.7802
0.5239	13.0	2089	0.6508	0.7932	0.7783	0.7932	0.5965	0.6273	0.0189	0.0183	0.9738	0.9845	0.7932	0.6273	0.7932	0.6046	0.7821
0.5239	14.0	2250	0.6588	0.7963	0.7823	0.7963	0.5957	0.6290	0.0186	0.0179	0.9746	0.9847	0.7963	0.6290	0.7963	0.6055	0.7852
0.5239	14.93	2400	0.6655	0.7963	0.7841	0.7963	0.5968	0.6325	0.0186	0.0179	0.9749	0.9847	0.7963	0.6325	0.7963	0.6074	0.7859

Framework versions

• Transformers 4.35.2
• Pytorch 2.1.0+cu121
• Datasets 2.18.0
• Tokenizers 0.15.1