langid-ner-multilingual-bert-gn-base-cased

mmaguero/langid-ner-multilingual-bert-gn-base-cased

langid-ner-multilingual-bert-gn-base-cased is a token classification model from mmaguero. 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: bert
Access: Public
Created: Nov 7, 2025
Updated: Nov 7, 2025
SHA: c7e6e068

Base models

mmaguero/multilingual-bert-gn-base-cased

Datasets

mmaguero/gua-spa-2023-task-1-2

Model card

From the Hugging Face model README

langid-ner-multilingual-bert-gn-base-cased

This model is a fine-tuned version of mmaguero/multilingual-bert-gn-base-cased on the task 1 and task 2 of GUA-SPA@IberLEF 2023 shared task dataset. It achieves the following results on the evaluation set:

Loss: 0.5980
Precision: 0.7389
Recall: 0.7464
F1: 0.7426
Accuracy: 0.8673

Model description

More information needed

Intended uses & limitations

NER (PER, LOC, ORG)
Token-based language identification (es, gn, mix, foreign)

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
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
num_epochs: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	72	1.1288	0.5004	0.4849	0.4925	0.7053
No log	2.0	144	0.7476	0.5986	0.5881	0.5933	0.7979
No log	3.0	216	0.6203	0.6329	0.6334	0.6331	0.8244
No log	4.0	288	0.5784	0.6413	0.6644	0.6527	0.8387
No log	5.0	360	0.5487	0.6626	0.6871	0.6746	0.8464
No log	6.0	432	0.5396	0.6929	0.7097	0.7012	0.8581
0.692	7.0	504	0.5446	0.6918	0.7156	0.7035	0.8555
0.692	8.0	576	0.5528	0.6932	0.7240	0.7082	0.8602
0.692	9.0	648	0.5614	0.6987	0.7257	0.7119	0.8602
0.692	10.0	720	0.5751	0.7071	0.7290	0.7179	0.8598
0.692	11.0	792	0.5865	0.6991	0.7232	0.7109	0.8602
0.692	12.0	864	0.5910	0.7102	0.7341	0.7219	0.8648
0.692	13.0	936	0.6068	0.7131	0.7383	0.7255	0.8602
0.161	14.0	1008	0.6168	0.7066	0.7374	0.7217	0.8635
0.161	15.0	1080	0.6177	0.7061	0.7357	0.7206	0.8628
0.161	16.0	1152	0.6280	0.7130	0.7399	0.7262	0.8632
0.161	17.0	1224	0.6293	0.7071	0.7391	0.7227	0.8628
0.161	18.0	1296	0.6330	0.7104	0.7408	0.7253	0.8638
0.161	19.0	1368	0.6365	0.7088	0.7391	0.7236	0.8625
0.161	20.0	1440	0.6367	0.7093	0.7391	0.7239	0.8615

Framework versions

Transformers 4.57.1
Pytorch 2.8.0+cu126
Datasets 4.0.0
Tokenizers 0.22.1

Task: Token Classification
Library: transformers
Type: bert
License: mit
Languages: gn, es, grn, gug
Created: Nov 7, 2025
Updated: Nov 7, 2025