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update app.py to include lab information (#2)
Browse files- update app.py to include lab information (3069af3b940d1eb578247a38e516f3ea2a5cbe76)
Co-authored-by: Zion van Wyk <[email protected]>
app.py
CHANGED
@@ -5,37 +5,77 @@ model_name = "dsfsi/nso-en-m2m100-gov"
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tokenizer = M2M100Tokenizer.from_pretrained(model_name)
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model = M2M100ForConditionalGeneration.from_pretrained(model_name)
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tokenizer.src_lang = "ns"
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model.config.forced_bos_token_id = tokenizer.get_lang_id("en")
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def translate(inp):
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inputs = tokenizer(inp, return_tensors="pt")
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translated_tokens = model.generate(**inputs, max_length=512, forced_bos_token_id=tokenizer.get_lang_id("en"))
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translated_text = tokenizer.decode(translated_tokens[0], skip_special_tokens=True)
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return translated_text
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"""
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tokenizer = M2M100Tokenizer.from_pretrained(model_name)
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model = M2M100ForConditionalGeneration.from_pretrained(model_name)
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tokenizer.src_lang = "ns"
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model.config.forced_bos_token_id = tokenizer.get_lang_id("en")
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def translate(inp):
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inputs = tokenizer(inp, return_tensors="pt")
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translated_tokens = model.generate(**inputs, max_length=512, forced_bos_token_id=tokenizer.get_lang_id("en"))
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translated_text = tokenizer.decode(translated_tokens[0], skip_special_tokens=True)
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return translated_text
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with gr.Blocks() as demo:
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with gr.Row():
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with gr.Column(scale=1):
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pass
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with gr.Column(scale=4, min_width=1000):
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gr.Image("logo_transparent_small.png", elem_id="logo", show_label=False, width=500)
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gr.Markdown("""
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<h1 style='text-align: center;'>Northern Sotho to English Translation</h1>
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<p style='text-align: center;'>This space provides a translation service from Northern Sotho to English using the M2M100 model, fine-tuned for low-resource languages. It supports researchers, linguists, and users working with Northern Sotho texts.</p>
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""")
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with gr.Column(scale=1):
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pass
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with gr.Column(variant="panel"):
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inp_text = gr.Textbox(lines=5, placeholder="Enter Northern Sotho text (maximum 5 lines)", label="Input", elem_id="centered-input")
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output_text = gr.Textbox(label="Output", elem_id="centered-output")
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translate_button = gr.Button("Translate", elem_id="centered-button")
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translate_button.click(translate, inputs=inp_text, outputs=output_text)
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gr.Markdown("""
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<div style='text-align: center;'>
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<a href='https://github.com/dsfsi/nso-en-m2m100-gov' target='_blank'>GitHub</a> |
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<a href='https://docs.google.com/forms/d/e/1FAIpQLSf7S36dyAUPx2egmXbFpnTBuzoRulhL5Elu-N1eoMhaO7v10w/viewform' target='_blank'>Feedback Form</a> |
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<a href='https://arxiv.org/abs/2303.03750' target='_blank'>Arxiv</a>
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</div>
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<br/>
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""")
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with gr.Accordion("More Information", open=False):
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gr.Markdown("""
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<h4 style="text-align: center;">Model Description</h4>
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<p style='text-align: center;'>This is a variant of the M2M100 model, fine-tuned on a multilingual dataset to support translation from Northern Sotho (Sepedi) to English. The model was trained with a focus on improving translation accuracy for low-resource languages.</p>
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""")
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gr.Markdown("""
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<h4 style="text-align: center;">Authors</h4>
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<div style='text-align: center;'>
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Vukosi Marivate, Matimba Shingange, Richard Lastrucci,
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Isheanesu Joseph Dzingirai, Jenalea Rajab
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</div>
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""")
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gr.Markdown("""
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<h4 style="text-align: center;">Citation</h4>
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<pre style="text-align: center; white-space: pre-wrap;">
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@inproceedings{lastrucci-etal-2023-preparing,
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title = "Preparing the Vuk{'}uzenzele and {ZA}-gov-multilingual {S}outh {A}frican multilingual corpora",
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author = "Richard Lastrucci and Isheanesu Dzingirai and Jenalea Rajab
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and Andani Madodonga and Matimba Shingange and Daniel Njini and Vukosi Marivate",
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booktitle = "Proceedings of the Fourth workshop on Resources for African Indigenous Languages (RAIL 2023)",
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month = may,
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year = "2023",
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address = "Dubrovnik, Croatia",
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publisher = "Association for Computational Linguistics",
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url = "https://aclanthology.org/2023.rail-1.3",
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pages = "18--25"
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}
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</pre>
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""")
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gr.Markdown("""
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<h4 style="text-align: center;">DOI</h4>
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<div style='text-align: center;'>
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<a href="https://doi.org/10.48550/arXiv.2303.03750" target="_blank">10.48550/arXiv.2303.03750</a>
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</div>
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""")
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demo.launch()
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