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Rename to dialect-fair-hate-classifier (drop 'Guardian')

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  1. README.md +7 -7
README.md CHANGED
@@ -15,11 +15,11 @@ metrics:
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  - recall
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  ---
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- # Guardian — Dialect-Fair Hate Classifier (ModernBERT-large)
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  A binary hate-speech classifier fine-tuned from [ModernBERT-large](https://huggingface.co/answerdotai/ModernBERT-large), explicitly debiased against **African-American English (AAE) false positives**.
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- Off-the-shelf toxicity/hate classifiers flag benign AAE text as hateful at **2x+** the rate of benign General-American English (Sap et al. 2019). Guardian targets and measures that gap directly: it catches **93% of hate** while keeping the benign-AAE false-positive rate within **~0.04** of the benign-GAE rate.
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  Author: **Guy Grigsby** (Aeryx-ai). License: MIT.
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@@ -45,8 +45,8 @@ The dialect FP gap is near-parity across the whole frontier (raw bias is ~5x low
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  import torch
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  from transformers import AutoModelForSequenceClassification, AutoTokenizer
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- tok = AutoTokenizer.from_pretrained("Aeryx-ai/guardian-dialect-fair-hate")
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- model = AutoModelForSequenceClassification.from_pretrained("Aeryx-ai/guardian-dialect-fair-hate").eval()
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  def hate_prob(text):
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  enc = tok(text, return_tensors="pt", truncation=True, max_length=256)
@@ -74,11 +74,11 @@ Loss reweighting and DANN-style adversarial debiasing were tried; **data volume
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  ## Citation
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  ```
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- @misc{grigsby2026guardian,
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- title = {Guardian: A Dialect-Fair Hate Classifier},
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  author = {Grigsby, Guy},
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  year = {2026},
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- howpublished = {\url{https://huggingface.co/Aeryx-ai/guardian-dialect-fair-hate}}
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  }
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  ```
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  - recall
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  ---
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+ # Dialect-Fair Hate Classifier (ModernBERT-large)
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  A binary hate-speech classifier fine-tuned from [ModernBERT-large](https://huggingface.co/answerdotai/ModernBERT-large), explicitly debiased against **African-American English (AAE) false positives**.
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+ Off-the-shelf toxicity/hate classifiers flag benign AAE text as hateful at **2x+** the rate of benign General-American English (Sap et al. 2019). This classifier targets and measures that gap directly: it catches **93% of hate** while keeping the benign-AAE false-positive rate within **~0.04** of the benign-GAE rate.
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  Author: **Guy Grigsby** (Aeryx-ai). License: MIT.
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  import torch
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  from transformers import AutoModelForSequenceClassification, AutoTokenizer
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+ tok = AutoTokenizer.from_pretrained("Aeryx-ai/dialect-fair-hate-classifier")
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+ model = AutoModelForSequenceClassification.from_pretrained("Aeryx-ai/dialect-fair-hate-classifier").eval()
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  def hate_prob(text):
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  enc = tok(text, return_tensors="pt", truncation=True, max_length=256)
 
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  ## Citation
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  ```
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+ @misc{grigsby2026dialectfair,
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+ title = {A Dialect-Fair Hate Classifier},
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  author = {Grigsby, Guy},
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  year = {2026},
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+ howpublished = {\url{https://huggingface.co/Aeryx-ai/dialect-fair-hate-classifier}}
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  }
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  ```
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