---
license: mit
base_model: xlm-roberta-large
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: XLM_RoBERTa-Multilingual-Clickbait-Detection
  results: []
datasets:
- christinacdl/clickbait_detection_dataset
language:
- en
- el
- it
- es
- ro
- de
- fr
- pl
pipeline_tag: text-classification
---

<!-- 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-Multilingual-Clickbait-Detection

This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2192
- Micro F1: 0.9759
- Macro F1: 0.9758
- Accuracy: 0.9759

## Test Set Macro-F1 scores

- Multilingual test set: 97.28
- en test set: 97.83
- el test set: 97.32
- it test set: 97.54
- es test set: 97.67
- ro test set: 97.40
- de test set: 97.40
- fr test set: 96.90
- pl test set: 96.18


## Intended uses & limitations

- This model will be employed for an EU project.

## Training and evaluation data

- The "clickbait_detection_dataset" was translated from English to Greek, Italian, Spanish, Romanian, French and German using the Opus-mt.
- The dataset was also translated from English to Polish using the M2M NMT. 
- The "EasyNMT" library was utilized to employ the NMT models.

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4

### Framework versions

- Transformers 4.36.1
- Pytorch 2.1.0+cu121
- Datasets 2.13.1
- Tokenizers 0.15.0