---
license: mit
base_model: xlm-roberta-base
tags:
- silvanus
datasets:
- id_nergrit_corpus
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: xlm-roberta-base-ner-silvanus
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: id_nergrit_corpus
      type: id_nergrit_corpus
      config: ner
      split: validation
      args: ner
    metrics:
    - name: Precision
      type: precision
      value: 0.918918918918919
    - name: Recall
      type: recall
      value: 0.9272727272727272
    - name: F1
      type: f1
      value: 0.9230769230769231
    - name: Accuracy
      type: accuracy
      value: 0.9858518778229216
language:
- id
- en
- es
- it
- sk
pipeline_tag: token-classification
widget:
- text: >-
    Kebakaran hutan dan lahan terus terjadi dan semakin meluas di Kota
    Palangkaraya, Kalimantan Tengah (Kalteng) pada hari Rabu, 15 Nopember 2023 20.00 WIB. Bahkan kobaran api mulai membakar
    pondok warga dan mendekati permukiman. BZK #RCTINews #SeputariNews #News
    #Karhutla #KebakaranHutan #HutanKalimantan #SILVANUS_Italian_Pilot_Testing
  example_title: Indonesia
- text: >-
    Wildfire rages for a second day in Evia destroying a Natura 2000 protected
    pine forest. - 5:51 PM Aug 14, 2019
  example_title: English
- text: >-
    Incendio forestal obliga a la evacuación de hasta 850 personas cerca del
    pueblo de Montichelvo en Valencia.
  example_title: Spanish
- text: >-
    Incendi boschivi nell'est del Paese: 2 morti e oltre 50 case distrutte nello
    stato del Queensland.
  example_title: Italian
- text: Lesné požiare na Sicílii si vyžiadali dva ľudské životy a evakuáciu hotela http://dlvr.it/SwW3sC - 23. septembra 2023 20:57
  example_title: Slovak
---

<!-- 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. -->

# xlm-roberta-base-ner-silvanus

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the id_nergrit_corpus dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0595
- Precision: 0.9189
- Recall: 0.9273
- F1: 0.9231
- Accuracy: 0.9859

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1394        | 1.0   | 827  | 0.0559          | 0.8808    | 0.9257 | 0.9027 | 0.9842   |
| 0.0468        | 2.0   | 1654 | 0.0575          | 0.9107    | 0.9190 | 0.9148 | 0.9849   |
| 0.0279        | 3.0   | 2481 | 0.0595          | 0.9189    | 0.9273 | 0.9231 | 0.9859   |


### Framework versions

- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1