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README.md
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- accuracy
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# Model Card: POLLCHECK/
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## Model Details
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**Model Name:** POLLCHECK/
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**Model Description:** This is a fine-tuned PaliGemma model for news classification e.g. "biased" or "unbiased". In this particular task, the term 'biased' represents disinformation, propaganda, loaded language, negative associations, generalization, harm, hatred, satire
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whereas 'unbiased' represents real news without the spread of misinformation, disinformation, and propaganda. The model can be used to identify potential bias in text and images, which is useful for applications in media analysis, content moderation, and research on bias in written communication.
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@@ -50,7 +50,7 @@ from transformers import AutoProcessor, PaliGemmaForConditionalGeneration
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device = "cuda:0"
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dtype = torch.bfloat16
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# Load the fine-tuned model and tokenizer
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model_id = "POLLCHECK/
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model = PaliGemmaForConditionalGeneration.from_pretrained(
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model_id,
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torch_dtype=dtype,
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- accuracy
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# Model Card: POLLCHECK/Paligemma-bias-classifier
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## Model Details
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**Model Name:** POLLCHECK/Paligemma-bias-classifier
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**Model Description:** This is a fine-tuned PaliGemma model for news classification e.g. "biased" or "unbiased". In this particular task, the term 'biased' represents disinformation, propaganda, loaded language, negative associations, generalization, harm, hatred, satire
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whereas 'unbiased' represents real news without the spread of misinformation, disinformation, and propaganda. The model can be used to identify potential bias in text and images, which is useful for applications in media analysis, content moderation, and research on bias in written communication.
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device = "cuda:0"
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dtype = torch.bfloat16
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# Load the fine-tuned model and tokenizer
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model_id = "POLLCHECK/Paligemma-bias-classifier" # path of the model
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model = PaliGemmaForConditionalGeneration.from_pretrained(
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model_id,
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torch_dtype=dtype,
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