This model was an experiment BUT NOT THE FINAL MODEL.

The final model was annahaz/xlm-roberta-base-misogyny-sexism-indomain-mix-bal (https://huggingface.co/annahaz/xlm-roberta-base-misogyny-sexism-indomain-mix-bal)

Please consider using/trying that model instead.

This model was an experiment for the following paper BUT THIS MODEL IS NOT THE FINAL MODEL:

@InProceedings{10.1007/978-3-031-43129-6_9,
author="Chang, Rong-Ching
and May, Jonathan
and Lerman, Kristina",
editor="Thomson, Robert
and Al-khateeb, Samer
and Burger, Annetta
and Park, Patrick
and A. Pyke, Aryn",
title="Feedback Loops and Complex Dynamics of Harmful Speech in Online Discussions",
booktitle="Social, Cultural, and Behavioral Modeling",
year="2023",
publisher="Springer Nature Switzerland",
address="Cham",
pages="85--94",
abstract="Harmful and toxic speech contribute to an unwelcoming online environment that suppresses participation and conversation. Efforts have focused on detecting and mitigating harmful speech; however, the mechanisms by which toxicity degrades online discussions are not well understood. This paper makes two contributions. First, to comprehensively model harmful comments, we introduce a multilingual misogyny and sexist speech detection model (https://huggingface.co/annahaz/xlm-roberta-base-misogyny-sexism-indomain-mix-bal). Second, we model the complex dynamics of online discussions as feedback loops in which harmful comments lead to negative emotions which prompt even more harmful comments. To quantify the feedback loops, we use a combination of mutual Granger causality and regression to analyze discussions on two political forums on Reddit: the moderated political forum r/Politics and the moderated neutral political forum r/NeutralPolitics. Our results suggest that harmful comments and negative emotions create self-reinforcing feedback loops in forums. Contrarily, moderation with neutral discussion appears to tip interactions into self-extinguishing feedback loops that reduce harmful speech and negative emotions. Our study sheds more light on the complex dynamics of harmful speech and the role of moderation and neutral discussion in mitigating these dynamics.",
isbn="978-3-031-43129-6"
}

---

license: mit tags:

• generated_from_trainer metrics:
• accuracy
• f1
• precision
• recall model-index:
• name: xlm-roberta-base-misogyny-sexism-tweets results: []

---

xlm-roberta-base-misogyny-sexism-tweets

This model is a fine-tuned version of xlm-roberta-base on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.5009
• Accuracy: 0.796
• F1: 0.8132
• Precision: 0.75
• Recall: 0.888
• Mae: 0.204
• Tn: 352
• Fp: 148
• Fn: 56
• Tp: 444

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: 16
• eval_batch_size: 16
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 4

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall	Mae	Tn	Fp	Fn	Tp
0.4947	1.0	1646	0.4683	0.765	0.7866	0.7205	0.866	0.235	332	168	67	433
0.4285	2.0	3292	0.4514	0.779	0.8004	0.7298	0.886	0.221	336	164	57	443
0.3721	3.0	4938	0.4430	0.781	0.8060	0.7234	0.91	0.219	326	174	45	455
0.3127	4.0	6584	0.5009	0.796	0.8132	0.75	0.888	0.204	352	148	56	444

Framework versions

• Transformers 4.20.1
• Pytorch 1.12.0+cu102
• Datasets 2.3.2
• Tokenizers 0.12.1