Jordan Myers
commited on
Commit
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2e7cfe8
1
Parent(s):
413dc1c
blocks test
Browse files
app.py
CHANGED
@@ -40,24 +40,59 @@ def translate(text, src_lang, tgt_lang, candidates:int):
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return output
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app = gr.Interface(
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# A simple English / toki pona Neural Machine Translation App!
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### toki a! 💬
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the *quality of data* found on Tatoeba directly influences the perfomance of projects like this!
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If you wish to contribute, please simply add high quality and diverse translations to Tatoeba!
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"""
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app.launch()
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return output
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# app = gr.Interface(
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# fn=translate,
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# inputs=[
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# gr.components.Textbox(label="Text"),
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# gr.components.Dropdown(label="Source Language", choices=list(LANG_CODES.keys())),
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# gr.components.Dropdown(label="Target Language", choices=list(LANG_CODES.keys())),
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# gr.Slider(label="Number of return sequences", value=3, minimum=1, maximum=12, step=1)
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# ],
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# outputs=["text"],
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# examples=[
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# ["Welcome to my translation app.", "English", "toki pona", 3],
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# ["Its not always perfect, but its pretty okay!", "English", "toki pona", 3],
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# ["ilo pi ante toki ni li pona a!", "toki pona", "English", 3],
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# ["kijetesantakalu li pona", "toki pona", "English", 3],
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# ["mi li toki e toki pona", "toki pona", "toki pona", 3]
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# ],
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# cache_examples=False,
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# article="""
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# # A simple English / toki pona Neural Machine Translation App!
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# ### toki a! 💬
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# This is a simple english to toki pona / toki pona to english neural machine translation app.
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# Input your text to translate, a source language and target language, and desired number of return sequences!
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# ### Grammaticality / Regularization
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# English -> English and/or toki pona -> toki pona will result in some form of regularization.
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# This can approximate grammaticality, but it isn't always the best.
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# For example, "mi li toki e toki pona" [src: toki pona, tgt: toki pona] will result in ['mi toki e toki pona.', 'mi toki pona.', 'mi toki e toki pona']
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# (Thus, the ungrammatical "li" is dropped)
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# ### Model and Data
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# This app utilizes a fine-tuned version of Facebook/Meta AI's M2M100 418M param model.
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# By leveraging the pretrained weights of the massively multilingual M2M100 model,
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# we can jumpstart our transfer learning to accomplish machine translation for toki pona!
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# The model was fine-tuned on the English/toki pona bitexts found at https://tatoeba.org/
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# ### This app is a work in progress and obviously not all translations will be perfect.
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# In addition to parameter quantity and the hyper-parameters used while training,
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# the *quality of data* found on Tatoeba directly influences the perfomance of projects like this!
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# If you wish to contribute, please simply add high quality and diverse translations to Tatoeba!
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# """,
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# title="English / toki pona Translation"
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# )
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with gr.Blocks() as app:
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gr.Markdown("""
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# A simple English / toki pona Neural Machine Translation App!
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### toki a! 💬
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the *quality of data* found on Tatoeba directly influences the perfomance of projects like this!
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If you wish to contribute, please simply add high quality and diverse translations to Tatoeba!
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"""
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)
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inputs=[
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gr.components.Textbox(label="Text"),
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gr.components.Dropdown(label="Source Language", choices=list(LANG_CODES.keys())),
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gr.components.Dropdown(label="Target Language", choices=list(LANG_CODES.keys())),
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gr.Slider(label="Number of return sequences", value=3, minimum=1, maximum=12, step=1)
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]
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translate_btn = gr.Button("Translate! | o ante toki!")
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translate_btn.click(translate, inputs=inputs, outputs=["text"])
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app.launch()
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