README.md

metadata

base_model: microsoft/mpnet-base
tags:
  - generated_from_trainer
metrics:
  - f1
model-index:
  - name: mpnet-base-airlines-news-multi-label
    results: []

mpnet-base-airlines-news-multi-label

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

• Loss: 0.2478
• F1: 0.8938
• Roc Auc: 0.6465

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: 7e-05
• train_batch_size: 32
• eval_batch_size: 32
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 40

Training results

Training Loss	Epoch	Step	Validation Loss	F1	Roc Auc
No log	1.0	57	0.3726	0.8319	0.5
No log	2.0	114	0.3361	0.8319	0.5
No log	3.0	171	0.3303	0.8319	0.5
No log	4.0	228	0.3249	0.8319	0.5
No log	5.0	285	0.3188	0.8319	0.5
No log	6.0	342	0.3141	0.8319	0.5
No log	7.0	399	0.3089	0.8319	0.5
No log	8.0	456	0.3042	0.8319	0.5
0.3595	9.0	513	0.2997	0.8319	0.5
0.3595	10.0	570	0.2940	0.8319	0.5
0.3595	11.0	627	0.2898	0.8319	0.5
0.3595	12.0	684	0.2856	0.8463	0.5032
0.3595	13.0	741	0.2819	0.8593	0.5096
0.3595	14.0	798	0.2789	0.8600	0.5128
0.3595	15.0	855	0.2757	0.8701	0.5220
0.3595	16.0	912	0.2723	0.8733	0.5312
0.3595	17.0	969	0.2698	0.8733	0.5312
0.2983	18.0	1026	0.2670	0.8808	0.5629
0.2983	19.0	1083	0.2652	0.8814	0.5661
0.2983	20.0	1140	0.2630	0.8786	0.5744
0.2983	21.0	1197	0.2612	0.8807	0.5840
0.2983	22.0	1254	0.2596	0.8818	0.5900
0.2983	23.0	1311	0.2580	0.8841	0.6024
0.2983	24.0	1368	0.2562	0.8878	0.6153
0.2983	25.0	1425	0.2555	0.8851	0.6056
0.2983	26.0	1482	0.2544	0.8860	0.6088
0.2747	27.0	1539	0.2535	0.8868	0.6148
0.2747	28.0	1596	0.2527	0.8878	0.6153
0.2747	29.0	1653	0.2519	0.8869	0.6121
0.2747	30.0	1710	0.2512	0.8875	0.6180
0.2747	31.0	1767	0.2501	0.8900	0.6277
0.2747	32.0	1824	0.2495	0.8923	0.6401
0.2747	33.0	1881	0.2492	0.8907	0.6337
0.2747	34.0	1938	0.2488	0.8922	0.6401
0.2747	35.0	1995	0.2485	0.8915	0.6369
0.2633	36.0	2052	0.2480	0.8922	0.6401
0.2633	37.0	2109	0.2478	0.8938	0.6465
0.2633	38.0	2166	0.2477	0.8930	0.6433
0.2633	39.0	2223	0.2476	0.8938	0.6465
0.2633	40.0	2280	0.2476	0.8938	0.6465

Framework versions

• Transformers 4.41.1
• Pytorch 2.3.0+cu121
• Datasets 2.19.1
• Tokenizers 0.19.1