# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("sileod/deberta-v3-base-tasksource-toxicity")
model = AutoModelForSequenceClassification.from_pretrained("sileod/deberta-v3-base-tasksource-toxicity")Quick Links
Multi-task fine-tune of deberta-base-tasksource for hate detection
| Test Name | Test Accuracy (%) | Test Pearson (%) |
|---|---|---|
| dynahate | 82.8 | |
| toxic_conversations | 96.0 | |
| implicit-hate-stg1 | 78.0 | |
| HatemojiBuild | 70.6 | |
| tweet_eval/hate | 55.8 | |
| oasst2_dense_flat/toxicity | 50.996 | |
| civil_comments/toxicity | 73.046 | |
| toxigen-data | 78.217 |
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="sileod/deberta-v3-base-tasksource-toxicity")