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import gradio as gr |
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM |
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tokenizer = AutoTokenizer.from_pretrained("facebook/bart-base") |
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infer = AutoModelForSeq2SeqLM.from_pretrained("Qilex/bart-largeEN-ME") |
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def translate(sentence): |
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input_ids = tokenizer(sentence, return_tensors="pt").input_ids |
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outputs = infer.generate(input_ids, max_new_tokens = len(sentence.split(' '))*10) |
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return tokenizer.decode(outputs[0], skip_special_tokens=True) |
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def translate_multiline(sentence): |
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if '\n' in sentence: |
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lines = sentence.split('\n') |
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translated_lines = [translate(line) for line in lines if len(line) > 0] |
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return '\n'.join(translated_lines) |
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else: |
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return translate(sentence) |
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title = "Modern English to Middle English Translator" |
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description = """ |
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This translator is trained on about 70,000 English/Middle English paired sentences. |
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<br> |
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It's still a work in progress. |
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<br> |
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""" |
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article = ''' |
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<br> |
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You can improve results by removing contractions (hadn't -> had not) |
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''' |
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gr.Interface( |
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fn=translate_multiline, |
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inputs=gr.Textbox(lines=1, placeholder="Enter text to translate."), |
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outputs="text", |
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title=title, |
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description=description, |
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article = article, |
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).launch() |
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