README.md

metadata

license: apache-2.0
tags:
  - generated_from_trainer
base_model: facebook/bart-large
metrics:
  - accuracy
  - precision
  - recall
model-index:
  - name: bart-large-lora-no-grad
    results: []

bart-large-lora-no-grad

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

• Loss: 0.8724
• Accuracy: 0.8428
• Precision: 0.8414
• Recall: 0.8428
• Precision Macro: 0.8149
• Recall Macro: 0.7856
• Macro Fpr: 0.0144
• Weighted Fpr: 0.0138
• Weighted Specificity: 0.9778
• Macro Specificity: 0.9876
• Weighted Sensitivity: 0.8366
• Macro Sensitivity: 0.7856
• F1 Micro: 0.8366
• F1 Macro: 0.7922
• F1 Weighted: 0.8329

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
• 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
1.3548	1.0	643	0.7811	0.7568	0.7272	0.7568	0.4206	0.4734	0.0234	0.0224	0.9682	0.9817	0.7568	0.4734	0.7568	0.4364	0.7359
0.7738	2.0	1286	0.6572	0.7893	0.7848	0.7893	0.6529	0.5639	0.0196	0.0187	0.9732	0.9842	0.7893	0.5639	0.7893	0.5618	0.7783
0.6874	3.0	1929	0.6485	0.8009	0.7994	0.8009	0.6224	0.6498	0.0179	0.0174	0.9767	0.9852	0.8009	0.6498	0.8009	0.6248	0.7948
0.502	4.0	2572	0.6912	0.8257	0.8216	0.8257	0.7661	0.7399	0.0158	0.0149	0.9738	0.9866	0.8257	0.7399	0.8257	0.7393	0.8182
0.4443	5.0	3215	0.6655	0.8350	0.8324	0.8350	0.7584	0.7344	0.0146	0.0139	0.9781	0.9875	0.8350	0.7344	0.8350	0.7352	0.8308
0.3903	6.0	3858	0.7269	0.8304	0.8288	0.8304	0.7500	0.7407	0.0149	0.0144	0.9789	0.9873	0.8304	0.7407	0.8304	0.7363	0.8261
0.3398	7.0	4501	0.8292	0.8218	0.8264	0.8218	0.8274	0.7793	0.0161	0.0152	0.9752	0.9865	0.8218	0.7793	0.8218	0.7883	0.8163
0.2818	8.0	5144	0.8360	0.8218	0.8240	0.8218	0.8251	0.7683	0.0159	0.0152	0.9767	0.9866	0.8218	0.7683	0.8218	0.7744	0.8178
0.2572	9.0	5787	0.8456	0.8342	0.8328	0.8342	0.7999	0.7735	0.0146	0.0140	0.9787	0.9875	0.8342	0.7735	0.8342	0.7768	0.8310
0.2594	10.0	6430	0.8724	0.8428	0.8414	0.8428	0.8149	0.7891	0.0138	0.0132	0.9790	0.9881	0.8428	0.7891	0.8428	0.7955	0.8396
0.208	11.0	7073	0.9797	0.8335	0.8339	0.8335	0.8092	0.7870	0.0148	0.0141	0.9774	0.9874	0.8335	0.7870	0.8335	0.7896	0.8303
0.1786	12.0	7716	1.0180	0.8311	0.8323	0.8311	0.8100	0.7846	0.0149	0.0143	0.9777	0.9873	0.8311	0.7846	0.8311	0.7906	0.8285
0.1556	13.0	8359	1.0392	0.8358	0.8335	0.8358	0.8040	0.7830	0.0146	0.0138	0.9773	0.9875	0.8358	0.7830	0.8358	0.7876	0.8321
0.1419	14.0	9002	1.0568	0.8381	0.8362	0.8381	0.8110	0.7855	0.0143	0.0136	0.9779	0.9877	0.8381	0.7855	0.8381	0.7917	0.8349
0.1251	15.0	9645	1.0593	0.8366	0.8350	0.8366	0.8149	0.7856	0.0144	0.0138	0.9778	0.9876	0.8366	0.7856	0.8366	0.7922	0.8329

Framework versions

• Transformers 4.35.2
• Pytorch 2.1.0+cu121
• Datasets 2.19.0
• Tokenizers 0.15.1