Opus Mt En Ine

🚀 eng-ine

This is a translation model that translates English into various Indo - European languages, providing a wide - range of language translation capabilities.

🚀 Quick Start

This eng-ine model is designed for translating English to a multitude of Indo - European languages. To get started, you need to ensure you have the necessary pre - processing steps in place. A sentence initial language token in the form of >>id<< (where id is a valid target language ID) is required.

✨ Features

• Broad Language Support: Supports a vast array of Indo - European languages, including but not limited to Afrikaans, Arabic, Armenian, Basque, Belarusian, Bulgarian, Catalan, Czech, Danish, Dutch, English, Estonian, Finnish, French, Galician, German, Greek, Gujarati, Hebrew, Hindi, Hungarian, Icelandic, Irish, Italian, Japanese, Kannada, Korean, Latvian, Lithuanian, Macedonian, Malayalam, Marathi, Norwegian, Persian, Polish, Portuguese, Romanian, Russian, Serbian, Slovak, Slovenian, Spanish, Swedish, Tamil, Telugu, Thai, Turkish, Ukrainian, Urdu, and Welsh.
• Transformer Model: Utilizes a transformer architecture, which is known for its effectiveness in handling sequence - to - sequence tasks such as machine translation.
• Pre - processing: Incorporates normalization and SentencePiece (spm32k, spm32k) for pre - processing, which helps in better handling of text data.

📦 Installation

There is no specific installation steps provided in the original document. If you want to use this model, you may need to download the original weights from the provided link: [opus2m - 2020 - 08 - 01.zip](https://object.pouta.csc.fi/Tatoeba - MT - models/eng - ine/opus2m - 2020 - 08 - 01.zip)

💻 Usage Examples

There are no code examples provided in the original document. However, in a general machine - translation scenario, you might use a framework like Hugging Face's transformers library. Here is a simple pseudo - code example:

from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

# Load the model and tokenizer
model = AutoModelForSeq2SeqLM.from_pretrained('path/to/downloaded/model')
tokenizer = AutoTokenizer.from_pretrained('path/to/downloaded/tokenizer')

# Input English text
input_text = "This is a test sentence."
# Add the target language token (e.g., for Spanish)
input_text_with_token = ">>spa<< " + input_text

# Tokenize the input
input_ids = tokenizer.encode(input_text_with_token, return_tensors='pt')

# Generate translation
outputs = model.generate(input_ids)

# Decode the output
translation = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(translation)

📚 Documentation

Model Information

Benchmarks

System Info

📄 License

This project is licensed under the Apache - 2.0 license.