---
license: cc-by-4.0
---

# Hatemoji Model

## Model description

This model is a fine-tuned version of the [DeBERTa base model](https://huggingface.co/microsoft/deberta-base). This model is cased. The model was trained on iterative rounds of adversarial data generation with human-and-model-in-the-loop. Each round of data has emoji-containing statements which are either non-hateful (LABEL 0.0) or hateful (LABEL 1.0).
- **Data Repository:** https://github.com/HannahKirk/Hatemoji
- **Paper:** https://arxiv.org/abs/2108.05921
- **Point of Contact:** hannah.kirk@oii.ox.ac.uk

## Intended uses & limitations
The intended use of the model is to classify English-language, emoji-containing, short-form text documents as a binary task: non-hateful vs hateful. The model has demonstrated strengths compared to commercial and academic models on classifying emoji-based hate, but is also a strong classifier of text-only hate. Because the model was trained on synthetic, adversarially-generated data, it may have some weaknesses when it comes to empirical emoji-based hate 'in-the-wild'. 

## How to use
The model can be used with pipeline:
```python
from transformers import pipeline
classifier = pipeline("text-classification",model='HannahRoseKirk/Hatemoji', return_all_scores=True)
prediction = classifier("I 💜💙💚 emoji 😍", )
print(prediction)
"""
Output
[[{'label': 'LABEL_0', 'score': 0.9999157190322876}, {'label': 'LABEL_1', 'score': 8.425049600191414e-05}]]
"""
```

### Training data
The model was trained on:
* The three rounds of emoji-containing, adversarially-generated texts from [HatemojiBuild](https://huggingface.co/datasets/HannahRoseKirk/HatemojiBuild)
* The four rounds of text-only, adversarially-generated texts from Vidgen et al., (2021). _Learning from the worst: Dynamically generated datasets to improve online hate detection_. Available on [Github](https://github.com/bvidgen/Dynamically-Generated-Hate-Speech-Dataset) and explained in their [paper](https://arxiv.org/abs/2012.15761).
* A collection of widely available and publicly accessible datasets from [https://hatespeechdata.com/](hatespeechdata.com)

## Train procedure
The model was trained using HuggingFace's [run glue script](https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py), using the following parameters:
```
python3 transformers/examples/pytorch/text-classification/run_glue.py \
--model_name_or_path microsoft/deberta-base \
--validation_file path_to_data/dev.csv \
--train_file path_to_data/train.csv \
--do_train --do_eval --max_seq_length 512 --learning_rate 2e-5 \
--num_train_epochs 3 --evaluation_strategy epoch \
--load_best_model_at_end --output_dir path_to_outdir/deberta123/ \
--seed 123 \
--cache_dir /.cache/huggingface/transformers/ \
--overwrite_output_dir > ./log_deb 2> ./err_deb
```

We experimented with upsampling the train split of each round to improve performance with increments of [1, 5, 10, 100], with the optimum upsampling taken
forward to all subsequent rounds. The optimal upsampling ratios for R1-R4 (text rounds from Vidgen et al.,) are carried forward. This model is trained on upsampling ratios of `{'R0': 1, 'R1':, 'R2':, 'R3':, 'R4': , 'R5':, 'R6':, 'R7':}.

## Variable and metrics

## Evaluation results