metadata

license: cc-by-4.0

Hatemoji Model

Model description

This model is a fine-tuned version of the DeBERTa base model. This model is cased. The model was trained on iterative rounds of adversarial data generation with human-and-model-in-the-loop. In each round, annotators are tasked with tricking the model-in-the-loop with emoji-containing statements that it will misclassify. Between each round, the model is retrained. This is the final model from the iterative process, referred to as R8-T in our paper. The intended task is to classify an emoji-containing statement as 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: [email protected]

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:

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
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 and explained in their paper.
A collection of widely available and publicly accessible datasets from https://hatespeechdata.com/

Train procedure

The model was trained using HuggingFace's run glue script, 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':5, 'R2':100, 'R3':1, 'R4':1 , 'R5':100, 'R6':1, 'R7':5}.

Variable and metrics

We wished to train a model which could effectively encode information about emoji-based hate, without worsening performance on text-only hate. Thus, we evaluate the model on:

HatemojiCheck, an evaluation checklist with 7 functionalities of emoji-based hate and contrast sets
HateCheck, an evaluation checklist contains 29 functional tests for hate speech and contrast sets.
The held-out tests sets from HatemojiBuild the three round of adversarially-generated data collection with emoji-containing examples (R5-7). Available on Huuggingface
The held-out test sets from the four rounds of adversarially-generated data collection with text-only examples (R1-4, from Vidgen et al.)

For the round-specific test sets, we used a weighted F1-score across them to choose the final model in each round. For more details, see our paper

Evaluation results

We compare our model in each iteration (R6-T, R7-T, R8-T) to:

P-IA: the identity attack attribute from Perspective API
P-TX: the toxicity attribute from Perspective API
B-D: A BERT model trained on the Davidson et al. (2017) dataset
B-F: A BERT model trained on the Founta et al. (2018) dataset

	Emoji Test Sets				Text Test Sets				All Rounds
	R5-R7		HmojiCheck		R1-R4		HateCheck		R1-R7
	Acc	F1	Acc	F1	Acc	F1	Acc	F1	Acc	F1
P-IA	0.508	0.394	0.689	0.754	0.679	0.720	0.765	0.839	0.658	0.689
P-TX	0.523	0.448	0.650	0.711	0.602	0.659	0.720	0.813	0.592	0.639
B-D	0.489	0.270	0.578	0.636	0.589	0.607	0.632	0.738	0.591	0.586
B-F	0.496	0.322	0.552	0.605	0.562	0.562	0.602	0.694	0.557	0.532
R6-T	0.757	0.769	0.879	0.910	0.823	0.837	0.961	0.971	0.813	0.825
R7-T	0.759	0.762	0.867	0.899	0.824	0.842	0.955	0.967	0.813	0.829
R8-T	0.744	0.755	0.871	0.904	0.827	0.844	0.966	0.975	0.814	0.829

For full discussion of the model results, see our paper.

A recent [paper][https://arxiv.org/pdf/2202.11176.pdf] A New Generation of Perspective API:Efficient Multilingual Character-level Transformers beats this model on the HatemojiCheck benchmark.