Instructions to use BenguerineMohammed/nmt-seq2seq-translator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BenguerineMohammed/nmt-seq2seq-translator with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("BenguerineMohammed/nmt-seq2seq-translator") model = AutoModelForSeq2SeqLM.from_pretrained("BenguerineMohammed/nmt-seq2seq-translator") - Notebooks
- Google Colab
- Kaggle
| """ | |
| Language mapping utilities for translation and speech recognition. | |
| Components: | |
| - LANGUAGE_CODES: Maps language names to FLORES-200 codes (used by Meta's NLLB model). | |
| - SPEECH_LANG_CODES: Maps language names to BCP-47 codes for Google Speech Recognition. | |
| - SUPPORTED_LANGUAGES: List of all supported language names (keys from LANGUAGE_CODES). | |
| - get_flores_code: Returns the FLORES-200 code for a given language name. | |
| - get_speech_code: Returns the Google SR BCP-47 code for a given language name. | |
| FLORES-200 code format: <language>_<Script> | |
| e.g. eng_Latn = English in Latin script | |
| arb_Arab = Arabic in Arabic script | |
| zho_Hans = Chinese in Simplified Han script | |
| """ | |
| LANGUAGE_CODES: dict[str, str] = { | |
| "English": "eng_Latn", | |
| "French": "fra_Latn", | |
| "Arabic": "arb_Arab", | |
| "Spanish": "spa_Latn", | |
| "German": "deu_Latn", | |
| "Chinese (Simplified)": "zho_Hans", | |
| "Chinese (Traditional)": "zho_Hant", | |
| "Japanese": "jpn_Jpan", | |
| "Korean": "kor_Hang", | |
| "Russian": "rus_Cyrl", | |
| "Portuguese": "por_Latn", | |
| "Italian": "ita_Latn", | |
| "Dutch": "nld_Latn", | |
| "Turkish": "tur_Latn", | |
| "Polish": "pol_Latn", | |
| "Hindi": "hin_Deva", | |
| "Bengali": "ben_Beng", | |
| "Urdu": "urd_Arab", | |
| "Vietnamese": "vie_Latn", | |
| "Thai": "tha_Thai", | |
| "Indonesian": "ind_Latn", | |
| "Malay": "zsm_Latn", | |
| "Swahili": "swh_Latn", | |
| "Greek": "ell_Grek", | |
| "Hebrew": "heb_Hebr", | |
| "Persian": "pes_Arab", | |
| "Ukrainian": "ukr_Cyrl", | |
| "Czech": "ces_Latn", | |
| "Swedish": "swe_Latn", | |
| "Danish": "dan_Latn", | |
| "Finnish": "fin_Latn", | |
| "Norwegian": "nob_Latn", | |
| "Hungarian": "hun_Latn", | |
| "Romanian": "ron_Latn", | |
| "Bulgarian": "bul_Cyrl", | |
| "Croatian": "hrv_Latn", | |
| "Serbian": "srp_Cyrl", | |
| "Slovak": "slk_Latn", | |
| "Lithuanian": "lit_Latn", | |
| "Latvian": "lvs_Latn", | |
| "Estonian": "est_Latn", | |
| "Slovenian": "slv_Latn", | |
| "Catalan": "cat_Latn", | |
| "Tagalog": "tgl_Latn", | |
| "Tamil": "tam_Taml", | |
| "Telugu": "tel_Telu", | |
| "Kannada": "kan_Knda", | |
| "Malayalam": "mal_Mlym", | |
| "Marathi": "mar_Deva", | |
| "Gujarati": "guj_Gujr", | |
| } | |
| # ISO 639-1 codes for Google Speech Recognition (subset of supported languages) | |
| SPEECH_LANG_CODES: dict[str, str] = { | |
| "English": "en-US", | |
| "French": "fr-FR", | |
| "Arabic": "ar-SA", | |
| "Spanish": "es-ES", | |
| "German": "de-DE", | |
| "Chinese (Simplified)": "zh-CN", | |
| "Japanese": "ja-JP", | |
| "Korean": "ko-KR", | |
| "Russian": "ru-RU", | |
| "Portuguese": "pt-PT", | |
| "Italian": "it-IT", | |
| } | |
| SUPPORTED_LANGUAGES: list[str] = list(LANGUAGE_CODES.keys()) | |
| def get_flores_code(language: str, fallback: str = "eng_Latn") -> str: | |
| """Return the floress-200 code for a language name.""" | |
| return LANGUAGE_CODES.get(language, fallback) | |
| def get_speech_code(language: str, fallback: str = "en-US") -> str: | |
| """Return the GOOGLE SR ISO code for a language name.""" | |
| return SPEECH_LANG_CODES.get(language, fallback) | |
| if __name__ == "__main__": | |
| print("Supported Languages:") | |
| for lang in SUPPORTED_LANGUAGES: | |
| print(f" {lang} and flores code: {get_flores_code(lang)} and speech code: {get_speech_code(lang)}") |