xlm-roberta-large-finetuned-augument-visquad2-2-4-2023-3

This model is a fine-tuned version of xlm-roberta-large on the None dataset. It achieves the following results on the evaluation set:

• Best F1: 76.3263
• Loss: 2.9101
• Exact: 41.0887
• F1: 58.6813
• Total: 3821
• Hasans Exact: 56.0498
• Hasans F1: 81.3876
• Hasans Total: 2653
• Noans Exact: 7.1062
• Noans F1: 7.1062
• Noans Total: 1168
• Best Exact: 60.3769
• Best Exact Thresh: 0.7798
• Best F1 Thresh: 0.9874

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: 1e-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
• lr_scheduler_warmup_ratio: 0.2
• lr_scheduler_warmup_steps: 1000
• num_epochs: 10

Training results

Training Loss	Epoch	Step	Best F1	Validation Loss	Exact	F1	Total	Hasans Exact	Hasans F1	Hasans Total	Noans Exact	Noans F1	Noans Total	Best Exact	Best Exact Thresh	Best F1 Thresh
0.9242	1.0	2807	69.6410	1.0239	37.3201	55.1119	3821	53.7505	79.3752	2653	0.0	0.0	1168	55.0118	0.8222	0.8968
0.3756	2.0	5615	73.7526	1.0092	38.8642	55.8953	3821	55.9744	80.5035	2653	0.0	0.0	1168	59.4085	0.9128	0.9611
0.2595	3.0	8423	75.1395	1.0121	39.7278	56.5553	3821	57.1806	81.4165	2653	0.0856	0.0856	1168	60.6386	0.8138	0.9174
0.185	4.0	11231	75.2011	1.2309	39.2306	56.7010	3821	56.2005	81.3625	2653	0.6849	0.6849	1168	59.7749	0.7215	0.8729
0.1336	5.0	14038	75.0330	1.4052	38.4454	56.1488	3821	55.2582	80.7556	2653	0.2568	0.2568	1168	59.4085	0.6660	0.8646
0.0976	6.0	16846	75.4976	1.6109	38.5763	56.1952	3821	55.4467	80.8224	2653	0.2568	0.2568	1168	59.8534	0.6631	0.9605
0.072	7.0	19654	76.0690	1.9673	39.5970	56.9041	3821	56.0874	81.0142	2653	2.1404	2.1404	1168	60.5862	0.7197	0.9882
0.0526	8.0	22462	75.3652	2.2945	38.8903	56.5382	3821	55.3336	80.7511	2653	1.5411	1.5411	1168	59.8273	0.6659	0.9573
0.0389	9.0	25269	76.0674	2.6609	42.5281	59.8494	3821	56.0121	80.9591	2653	11.9007	11.9007	1168	60.4292	0.6494	0.9632
0.0291	10.0	28070	76.3263	2.9101	41.0887	58.6813	3821	56.0498	81.3876	2653	7.1062	7.1062	1168	60.3769	0.7798	0.9874

Framework versions

• Transformers 4.27.3
• Pytorch 1.13.1+cu117
• Datasets 2.10.1
• Tokenizers 0.13.2