| --- |
| license: mit |
| base_model: xlm-roberta-base |
| metrics: |
| - accuracy |
| model-index: |
| - name: xlm-roberta-base-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 |
| language: |
| - id |
| - en |
| - es |
| - it |
| - sk |
| --- |
| |
| <!-- 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-fire-classification-silvanus |
|
|
| This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the Twitter (X) dataset based on the "forest fire" keyword. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.5255 |
| - Accuracy: 0.8884 |
|
|
| ## 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: 3e-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 | 0.5431 | 0.8670 | |
| | No log | 2.0 | 466 | 0.5125 | 0.8670 | |
| | 0.4162 | 3.0 | 699 | 0.5255 | 0.8884 | |
|
|
|
|
| ### Framework versions |
|
|
| - Transformers 4.35.0 |
| - Pytorch 2.1.0+cu118 |
| - Datasets 2.14.6 |
| - Tokenizers 0.14.1 |
