xlm-r-large-phoner

Nhocmecode/xlm-r-large-phoner

xlm-r-large-phoner is a token classification model from Nhocmecode. Use it when you need labels on individual words, such as names. It is set up for transformers. The card lists the license as mit.

should probably proofread and complete it, then remove this comment. --

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At a glance

Task: Token Classification
Library: transformers
License: mit
Model type: xlm-roberta
Access: Public
Created: Apr 16, 2026
Updated: Apr 16, 2026
SHA: 81e76837

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Base models

Model card

From the Hugging Face model README

xlm-r-large-phoner

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

Loss: 0.3043
Micro Precision: 0.9548
Micro Recall: 0.9610
Micro F1: 0.9579

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: 16
eval_batch_size: 16
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 64
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 30
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Micro Precision	Micro Recall	Micro F1
No log	1.0	79	0.2085	0.8585	0.9376	0.8963
No log	2.0	158	0.1734	0.9036	0.9355	0.9193
No log	3.0	237	0.1471	0.9238	0.9473	0.9354
No log	4.0	316	0.1700	0.9384	0.9492	0.9438
No log	5.0	395	0.1788	0.9213	0.9453	0.9331
No log	6.0	474	0.1763	0.9364	0.9589	0.9475
0.7363	7.0	553	0.1738	0.9410	0.9528	0.9468
0.7363	8.0	632	0.1940	0.9400	0.9589	0.9494
0.7363	9.0	711	0.1892	0.9393	0.9575	0.9483
0.7363	10.0	790	0.2084	0.9423	0.9551	0.9487
0.7363	11.0	869	0.2393	0.9455	0.9536	0.9495
0.7363	12.0	948	0.2102	0.9491	0.9556	0.9524
0.0970	13.0	1027	0.2251	0.9422	0.9543	0.9482
0.0970	14.0	1106	0.2360	0.9443	0.9642	0.9541
0.0970	15.0	1185	0.2485	0.9453	0.9565	0.9509
0.0970	16.0	1264	0.2535	0.9480	0.9561	0.9521
0.0970	17.0	1343	0.2480	0.9464	0.9593	0.9528
0.0970	18.0	1422	0.2899	0.9453	0.9584	0.9518
0.0398	19.0	1501	0.2723	0.9503	0.9596	0.9550
0.0398	20.0	1580	0.2587	0.9482	0.9597	0.9539
0.0398	21.0	1659	0.2986	0.9508	0.9589	0.9549
0.0398	22.0	1738	0.2739	0.9535	0.9615	0.9575
0.0398	23.0	1817	0.2916	0.9524	0.9626	0.9574
0.0398	24.0	1896	0.2959	0.9532	0.9612	0.9572
0.0398	25.0	1975	0.2933	0.9535	0.9596	0.9565
0.0118	26.0	2054	0.2937	0.9541	0.9607	0.9574
0.0118	27.0	2133	0.3011	0.9570	0.9592	0.9581
0.0118	28.0	2212	0.3029	0.9557	0.9601	0.9579
0.0118	29.0	2291	0.3034	0.9543	0.9604	0.9573
0.0118	30.0	2370	0.3043	0.9548	0.9610	0.9579

Framework versions

Transformers 5.0.0
Pytorch 2.10.0+cu128
Datasets 4.8.3
Tokenizers 0.22.2

Task: Token Classification
Library: transformers
Type: xlm-roberta
License: mit
Created: Apr 16, 2026
Updated: Apr 16, 2026