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app.py
CHANGED
@@ -10,19 +10,21 @@ defaultTxt = "I hate you cancerous insects so much"
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txt = st.text_area("Text to analyze", defaultTxt)
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# Load tokenizer and model weights, try to default to RoBERTa.
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match
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tokenizerPath = "s-nlp/roberta_toxicity_classifier"
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modelPath = "s-nlp/roberta_toxicity_classifier"
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tokenizerPath = "citizenlab/distilbert-base-multilingual-cased-toxicity"
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modelPath = "citizenlab/distilbert-base-multilingual-cased-toxicity"
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tokenizerPath = "unitary/multilingual-toxic-xlm-roberta"
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modelPath = "unitary/multilingual-toxic-xlm-roberta"
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tokenizerPath = "s-nlp/roberta_toxicity_classifier"
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modelPath = "s-nlp/roberta_toxicity_classifier"
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tokenizer = AutoTokenizer.from_pretrained(tokenizerPath)
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model = AutoModelForSequenceClassification.from_pretrained(modelPath)
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txt = st.text_area("Text to analyze", defaultTxt)
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# Load tokenizer and model weights, try to default to RoBERTa.
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# Huggingface does not support Python 3.10 match statements and I'm too lazy to implement an equivalent.
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if (option == "RoBERTa"):
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tokenizerPath = "s-nlp/roberta_toxicity_classifier"
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modelPath = "s-nlp/roberta_toxicity_classifier"
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elif (option == "DistilBERT"):
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tokenizerPath = "citizenlab/distilbert-base-multilingual-cased-toxicity"
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modelPath = "citizenlab/distilbert-base-multilingual-cased-toxicity"
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elif (option == "XLM-RoBERTa"):
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tokenizerPath = "unitary/multilingual-toxic-xlm-roberta"
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modelPath = "unitary/multilingual-toxic-xlm-roberta"
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else:
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tokenizerPath = "s-nlp/roberta_toxicity_classifier"
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modelPath = "s-nlp/roberta_toxicity_classifier"
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tokenizer = AutoTokenizer.from_pretrained(tokenizerPath)
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model = AutoModelForSequenceClassification.from_pretrained(modelPath)
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