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Duplicate from kadirnar/Multilingual-Translation
Browse filesCo-authored-by: Kadir Nar <kadirnar@users.noreply.huggingface.co>
- .gitattributes +34 -0
- README.md +14 -0
- app.py +61 -0
- requirements.txt +4 -0
- utils.py +128 -0
.gitattributes
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README.md
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---
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title: Multilingual Translation
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emoji: 🌍
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colorFrom: yellow
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colorTo: pink
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sdk: gradio
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sdk_version: 3.17.0
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app_file: app.py
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pinned: false
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license: mit
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duplicated_from: kadirnar/Multilingual-Translation
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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app.py
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from multilingual_translation import text_to_text_generation
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from utils import lang_ids, data_scraping
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import gradio as gr
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lang_list = list(lang_ids.keys())
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model_list = data_scraping()
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def multilingual_translate(
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prompt: str,
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model_id: str,
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target_lang: str
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):
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return text_to_text_generation(
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prompt=prompt,
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model_id=model_id,
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device='cpu',
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target_lang=lang_ids[target_lang]
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)
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inputs = [
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gr.Textbox(lines=4, value="Hello world!", label="Input Text"),
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gr.Dropdown(model_list, value="facebook/m2m100_418M", label="Model"),
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gr.Dropdown(lang_list, value="Turkish", label="Target Language"),
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]
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output = gr.outputs.Textbox(label="Output Text")
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examples = [
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[
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"Hello world!",
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"facebook/m2m100_418M",
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"Turkish",
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],
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[
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"Omar ve Merve çok iyi arkadaşlar.",
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"facebook/m2m100_418M",
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"Spanish",
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],
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[
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"Hugging Face is a great company.",
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"facebook/m2m100_418M",
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"French",
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]
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]
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title = "Beyond English-Centric Multilingual Machine Translation"
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description = "M2M100 is a multilingual encoder-decoder (seq-to-seq) model trained for Many-to-Many multilingual translation. It was introduced in this [paper](https://arxiv.org/abs/2010.11125) and first released in [this](https://github.com/pytorch/fairseq/tree/master/examples/m2m_100) repository."
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app = gr.Interface(
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fn=multilingual_translate,
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inputs=inputs,
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outputs=output,
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examples=examples,
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title=title,
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description=description,
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cache_examples=True
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)
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app.launch(debug=True, enable_queue=True)
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requirements.txt
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torch
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beautifulsoup4==4.11.2
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multilingual_translation==0.0.5
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requests==2.28.1
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utils.py
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from bs4 import BeautifulSoup
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import requests
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lang_ids = {
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"Afrikaans": "af",
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"Amharic": "am",
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"Arabic": "ar",
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"Asturian": "ast",
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"Azerbaijani": "az",
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"Bashkir": "ba",
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"Belarusian": "be",
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"Bulgarian": "bg",
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"Bengali": "bn",
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"Breton": "br",
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"Bosnian": "bs",
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"Catalan": "ca",
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"Cebuano": "ceb",
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"Czech": "cs",
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"Welsh": "cy",
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"Danish": "da",
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"German": "de",
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"Greeek": "el",
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"English": "en",
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"Spanish": "es",
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"Estonian": "et",
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"Persian": "fa",
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"Fulah": "ff",
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"Finnish": "fi",
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"French": "fr",
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"Western Frisian": "fy",
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"Irish": "ga",
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"Gaelic": "gd",
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"Galician": "gl",
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"Gujarati": "gu",
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"Hausa": "ha",
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"Hebrew": "he",
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"Hindi": "hi",
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"Croatian": "hr",
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"Haitian": "ht",
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"Hungarian": "hu",
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"Armenian": "hy",
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"Indonesian": "id",
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"Igbo": "ig",
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"Iloko": "ilo",
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"Icelandic": "is",
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"Italian": "it",
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"Japanese": "ja",
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"Javanese": "jv",
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"Georgian": "ka",
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"Kazakh": "kk",
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"Central Khmer": "km",
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"Kannada": "kn",
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"Korean": "ko",
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"Luxembourgish": "lb",
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"Ganda": "lg",
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"Lingala": "ln",
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"Lao": "lo",
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"Lithuanian": "lt",
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"Latvian": "lv",
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"Malagasy": "mg",
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"Macedonian": "mk",
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"Malayalam": "ml",
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"Mongolian": "mn",
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"Marathi": "mr",
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"Malay": "ms",
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"Burmese": "my",
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"Nepali": "ne",
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"Dutch": "nl",
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"Norwegian": "no",
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"Northern Sotho": "ns",
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"Occitan": "oc",
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"Oriya": "or",
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"Panjabi": "pa",
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"Polish": "pl",
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"Pushto": "ps",
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"Portuguese": "pt",
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"Romanian": "ro",
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"Russian": "ru",
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"Sindhi": "sd",
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"Sinhala": "si",
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"Slovak": "sk",
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"Slovenian": "sl",
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"Somali": "so",
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"Albanian": "sq",
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"Serbian": "sr",
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"Swati": "ss",
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"Sundanese": "su",
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"Swedish": "sv",
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"Swahili": "sw",
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"Tamil": "ta",
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"Thai": "th",
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"Tagalog": "tl",
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"Tswana": "tn",
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"Turkish": "tr",
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"Ukrainian": "uk",
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"Urdu": "ur",
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"Uzbek": "uz",
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"Vietnamese": "vi",
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"Wolof": "wo",
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"Xhosa": "xh",
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"Yiddish": "yi",
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"Yoruba": "yo",
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"Chinese": "zh",
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"Zulu": "zu",
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}
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def model_url_list():
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url_list = []
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for i in range(0, 5):
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url_list.append(f"https://huggingface.co/models?other=m2m_100&p={i}&sort=downloads")
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return url_list
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def data_scraping():
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url_list = model_url_list()
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model_list = []
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for url in url_list:
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response = requests.get(url)
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soup = BeautifulSoup(response.text, "html.parser")
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div_class = 'grid grid-cols-1 gap-5 2xl:grid-cols-2'
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div = soup.find('div', {'class': div_class})
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for a in div.find_all('a', href=True):
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model_list.append(a['href'])
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for i in range(len(model_list)):
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model_list[i] = model_list[i][1:]
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return model_list
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