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Update app.py
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app.py
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@@ -7,7 +7,7 @@ from difflib import Differ
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from concurrent.futures import ThreadPoolExecutor
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import os
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description = """#
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📲⌚🎅🏻OCRonos-Vintage is a small specialized model for OCR correction of cultural heritage archives pre-trained with llm.c. OCRonos-Vintage is only 124 million parameters. It can run easily on CPU or provide correction at scale on GPUs (>10k tokens/seconds) while providing a quality of correction comparable to GPT-4 or the llama version of OCRonos for English-speaking cultural archives.
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### Join us :
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@@ -16,7 +16,6 @@ description = """# 👋🏻Welcome to Tonic's On-Device📲⌚🎅🏻OCR Correc
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model_name = "PleIAs/OCRonos-Vintage"
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device = "cuda" if torch.cuda.is_available() else "cpu"
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🙋🏻♂️Welcome to Tonic's ⌚🎅🏻Vintage OCRonos Corrector (CPU)
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model = GPT2LMHeadModel.from_pretrained(model_name).to(device)
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tokenizer = GPT2Tokenizer.from_pretrained(model_name)
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from concurrent.futures import ThreadPoolExecutor
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import os
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description = """# 🙋🏻♂️Welcome to Tonic's On-Device📲⌚🎅🏻OCR Corrector (CPU)
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📲⌚🎅🏻OCRonos-Vintage is a small specialized model for OCR correction of cultural heritage archives pre-trained with llm.c. OCRonos-Vintage is only 124 million parameters. It can run easily on CPU or provide correction at scale on GPUs (>10k tokens/seconds) while providing a quality of correction comparable to GPT-4 or the llama version of OCRonos for English-speaking cultural archives.
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### Join us :
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model_name = "PleIAs/OCRonos-Vintage"
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device = "cuda" if torch.cuda.is_available() else "cpu"
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model = GPT2LMHeadModel.from_pretrained(model_name).to(device)
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tokenizer = GPT2Tokenizer.from_pretrained(model_name)
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