---
library_name: transformers
license: mit
base_model: mmaguero/multilingual-bert-gn-base-cased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: langid-ner-multilingual-bert-gn-base-cased
  results: []
datasets:
- mmaguero/gua-spa-2023-task-1-2
language:
- gn
- es
- grn
- gug
---

<!-- 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-multilingual-bert-gn-base-cased

This model is a fine-tuned version of [mmaguero/multilingual-bert-gn-base-cased](https://huggingface.co/mmaguero/multilingual-bert-gn-base-cased) on the  [task 1 and task 2 of GUA-SPA@IberLEF 2023 shared task](https://huggingface.co/datasets/mmaguero/gua-spa-2023-task-1-2) 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