roberta-large-mnli_nli

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

• Loss: 2.9476
• Accuracy: 0.6009
• Precision Macro: 0.6028
• Recall Macro: 0.6009
• F1 Macro: 0.6014
• F1 Weighted: 0.6012

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: 64
• eval_batch_size: 64
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 128
• optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: cosine
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 20
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision Macro	Recall Macro	F1 Macro	F1 Weighted
1.0485	1.0	143	0.9848	0.5162	0.5491	0.5191	0.4775	0.4758
0.9114	2.0	286	0.9839	0.5264	0.5642	0.5266	0.5150	0.5148
0.8746	3.0	429	0.9618	0.5517	0.5743	0.5522	0.5453	0.5451
0.7909	4.0	572	0.9498	0.5805	0.5859	0.5813	0.5766	0.5762
0.7105	5.0	715	0.9324	0.5956	0.6000	0.5960	0.5939	0.5936
0.6205	6.0	858	0.9797	0.5933	0.5958	0.5934	0.5927	0.5925
0.5113	7.0	1001	1.1925	0.5889	0.5918	0.5896	0.5857	0.5853
0.4181	8.0	1144	1.2665	0.5916	0.5922	0.5918	0.5918	0.5916
0.3218	9.0	1287	1.4587	0.5849	0.5866	0.5848	0.5849	0.5849
0.2543	10.0	1430	1.5554	0.5902	0.5910	0.5908	0.5892	0.5889
0.1851	11.0	1573	1.8125	0.5787	0.5829	0.5782	0.5786	0.5787
0.1316	12.0	1716	2.0182	0.5827	0.5837	0.5826	0.5826	0.5825
0.0884	13.0	1859	2.1233	0.5809	0.5823	0.5810	0.5812	0.5811
0.0708	14.0	2002	2.2924	0.5938	0.5936	0.5943	0.5935	0.5931
0.0527	15.0	2145	2.4595	0.5916	0.5923	0.5919	0.5918	0.5916
0.0334	16.0	2288	2.6315	0.5991	0.6009	0.5991	0.5996	0.5995
0.0186	17.0	2431	2.8367	0.5947	0.5979	0.5946	0.5953	0.5952
0.0179	18.0	2574	2.9197	0.6004	0.6032	0.6004	0.6010	0.6009
0.0113	19.0	2717	2.9423	0.5982	0.6003	0.5982	0.5987	0.5986
0.0134	20.0	2860	2.9476	0.6009	0.6028	0.6009	0.6014	0.6012

Framework versions

• Transformers 4.55.0
• Pytorch 2.7.0+cu126
• Datasets 4.0.0
• Tokenizers 0.21.4