Downloads · 30 days
10
20% of all-time downloads
langid-ner-xlm-v-base
mmaguero/langid-ner-xlm-v-base
langid-ner-xlm-v-base 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. --
All-time downloads
50
Public
Parameters
778M
3.2 GB on disk
Likes
0
Public
Repo makeup
Click a slice to open those files.
.safetensors3.1 GB · 98%
At a glance
- Task
- Token Classification
- Library
- transformers
- License
- mit
- Model type
- xlm-roberta
- Access
- Public
- Created
- Nov 7, 2025
- Updated
- Nov 7, 2025
- SHA
- 273aae7e
Try a prompt
Base models
Datasets
Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
langid-ner-xlm-v-base
This model is a fine-tuned version of facebook/xlm-v-base 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: 0.4369
- Precision: 0.8146
- Recall: 0.7939
- F1: 0.8041
- Accuracy: 0.9030
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: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 72 | 1.5829 | 0.5318 | 0.4706 | 0.4993 | 0.5808 |
| No log | 2.0 | 144 | 1.0009 | 0.5864 | 0.5495 | 0.5673 | 0.7504 |
| No log | 3.0 | 216 | 0.8142 | 0.6104 | 0.6124 | 0.6114 | 0.7966 |
| No log | 4.0 | 288 | 0.6806 | 0.6983 | 0.7047 | 0.7015 | 0.8404 |
| No log | 5.0 | 360 | 0.6066 | 0.7211 | 0.7223 | 0.7217 | 0.8448 |
| No log | 6.0 | 432 | 0.5607 | 0.7248 | 0.7357 | 0.7302 | 0.8672 |
| 0.9923 | 7.0 | 504 | 0.5318 | 0.7443 | 0.7450 | 0.7447 | 0.8802 |
| 0.9923 | 8.0 | 576 | 0.5072 | 0.7521 | 0.7584 | 0.7552 | 0.8806 |
| 0.9923 | 9.0 | 648 | 0.4955 | 0.7490 | 0.7584 | 0.7536 | 0.8796 |
| 0.9923 | 10.0 | 720 | 0.4924 | 0.7508 | 0.7609 | 0.7558 | 0.8802 |
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
- xlm-roberta
- License
- mit
- Languages
- es, gn, grn, gug
- Created
- Nov 7, 2025
- Updated
- Nov 7, 2025