xlm_r_idiom_classifier

This model is a fine-tuned version of xlm-roberta-base on the gsarti/magpie dataset. It achieves the following results on the evaluation set:

• Loss: 0.2194
• Accuracy: 0.9447
• F1: 0.9637
• Precision: 0.9717
• Recall: 0.9558

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: 2e-05
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 64
• optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 500
• num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
2.2827	0.1599	100	0.5334	0.7679	0.8687	0.7679	1.0
1.7807	0.3199	200	0.4303	0.8030	0.8662	0.9052	0.8304
1.5338	0.4798	300	0.3397	0.8621	0.9109	0.9037	0.9183
1.4698	0.6397	400	0.2908	0.8878	0.9289	0.9045	0.9546
1.3709	0.7997	500	0.2463	0.9094	0.9428	0.9142	0.9733
1.0718	0.9596	600	0.2248	0.9094	0.9400	0.9552	0.9253
0.9184	1.1184	700	0.2056	0.9186	0.9460	0.9647	0.9279
0.8307	1.2783	800	0.2476	0.9006	0.9324	0.9751	0.8934
0.8787	1.4382	900	0.1804	0.9357	0.9585	0.9507	0.9663
0.7389	1.5982	1000	0.1726	0.9375	0.9591	0.9631	0.9552
0.7945	1.7581	1100	0.1800	0.9330	0.9559	0.9662	0.9458
0.7269	1.9180	1200	0.2204	0.9170	0.9438	0.9829	0.9077
0.4623	2.0768	1300	0.1766	0.9415	0.9617	0.9668	0.9566
0.4865	2.2367	1400	0.1940	0.9348	0.9567	0.9748	0.9394
0.4601	2.3966	1500	0.1675	0.9417	0.9616	0.9747	0.9487
0.4357	2.5566	1600	0.1919	0.9370	0.9583	0.9751	0.9420
0.4754	2.7165	1700	0.1699	0.9411	0.9613	0.9707	0.9520
0.4924	2.8764	1800	0.1600	0.9453	0.9642	0.9712	0.9572
0.3183	3.0352	1900	0.1744	0.9426	0.9623	0.9722	0.9525
0.3434	3.1951	2000	0.1726	0.9458	0.9644	0.9718	0.9572
0.2877	3.3551	2100	0.1888	0.9483	0.9662	0.9682	0.9643
0.2688	3.5150	2200	0.2472	0.9307	0.9536	0.9817	0.9271
0.3482	3.6749	2300	0.1953	0.9476	0.9659	0.9644	0.9675
0.3906	3.8349	2400	0.1884	0.9458	0.9644	0.9734	0.9555
0.2702	3.9948	2500	0.1887	0.9505	0.9678	0.9672	0.9684
0.2212	4.1535	2600	0.2325	0.9429	0.9623	0.9759	0.9490
0.2978	4.3135	2700	0.2086	0.9453	0.9641	0.9723	0.9561
0.2364	4.4734	2800	0.2204	0.9471	0.9653	0.9729	0.9578
0.1388	4.6333	2900	0.2203	0.9462	0.9647	0.9720	0.9575
0.3332	4.7933	3000	0.2168	0.9476	0.9657	0.9707	0.9607
0.2389	4.9532	3100	0.2194	0.9447	0.9637	0.9717	0.9558

Framework versions

• Transformers 5.2.0
• Pytorch 2.9.0+cu126
• Datasets 4.0.0
• Tokenizers 0.22.2