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--- |
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library_name: transformers |
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language: en |
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license: mit |
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--- |
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# BART-large-ocr |
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This model is released as part of the paper [Leveraging LLMs for Post-OCR Correction of Historical Newspapers](https://aclanthology.org/2024.lt4hala-1.14/) and designed to correct OCR text. [BART-large](https://huggingface.co/facebook/bart-large) is fine-tuned for post-OCR correction of historical English, using [BLN600](https://aclanthology.org/2024.lrec-main.219/), a parallel corpus of 19th century newspaper machine/human transcription. |
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## Usage |
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```python |
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline |
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model = AutoModelForSeq2SeqLM.from_pretrained('pykale/bart-large-ocr') |
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tokenizer = AutoTokenizer.from_pretrained('pykale/bart-large-ocr') |
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generator = pipeline('text2text-generation', model=model.to('cuda'), tokenizer=tokenizer, device='cuda', max_length=1024) |
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ocr = "The defendant wits'fined �5 and costs." |
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pred = generator(ocr)[0]['generated_text'] |
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print(pred) |
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``` |
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## Citation |
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``` |
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@inproceedings{thomas-etal-2024-leveraging, |
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title = "Leveraging {LLM}s for Post-{OCR} Correction of Historical Newspapers", |
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author = "Thomas, Alan and Gaizauskas, Robert and Lu, Haiping", |
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editor = "Sprugnoli, Rachele and Passarotti, Marco", |
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booktitle = "Proceedings of the Third Workshop on Language Technologies for Historical and Ancient Languages (LT4HALA) @ LREC-COLING-2024", |
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month = "may", |
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year = "2024", |
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address = "Torino, Italia", |
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publisher = "ELRA and ICCL", |
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url = "https://aclanthology.org/2024.lt4hala-1.14", |
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pages = "116--121", |
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} |
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``` |
