metadata

license: apache-2.0
base_model: distilbert/distilbert-base-multilingual-cased
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: distil-multilingual-cased-fire-classification-silvanus
    results: []
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: >-
      3 nov 2023 21:57 - 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

distil-multilingual-cased-fire-classification-silvanus

This model is a fine-tuned version of distilbert/distilbert-base-multilingual-cased on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.4181
Accuracy: 0.8798

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: 2e-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	Accuracy
No log	1.0	233	0.3467	0.8755
No log	2.0	466	0.4038	0.8755
0.3216	3.0	699	0.4181	0.8798

Framework versions

Transformers 4.35.0
Pytorch 2.1.0+cu118
Datasets 2.14.6
Tokenizers 0.14.1