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BERT Language Translation

It seems like there might be a typo in your question, and you're referring to BERT. BERT (Bidirectional Encoder Representations from Transformers) is primarily designed for tasks related to natural language understanding, such as classification and named entity recognition. On the other hand, language translation tasks are usually handled by models like transformers from the Hugging Face library.

If you're interested in an example for language translation using a transformer model, here's an example using MarianMT, a multilingual translation model available in the transformers library:

pythonCopy codefrom transformers import MarianMTModel, MarianTokenizer

# Step 1: Load the MarianMT model and tokenizer
model_name = "Helsinki-NLP/opus-mt-en-de"  # English to German translation
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)

# Step 2: Define a function for translation
def translate(text, model, tokenizer):
    inputs = tokenizer(text, return_tensors="pt")
    outputs = model.generate(**inputs)
    translation = tokenizer.batch_decode(outputs, skip_special_tokens=True)
    return translation[0]

# Step 3: Perform translation
text_to_translate = "Hello, how are you?"
translated_text = translate(text_to_translate, model, tokenizer)

# Step 4: Print the results
print(f"Input: {text_to_translate}")
print(f"Translation: {translated_text}")

Make sure to replace model_name with the appropriate model for the language pair you are interested in. You can find available MarianMT models on the Hugging Face model hub: https://huggingface.co/models?pipeline_tag=translation.

If you were indeed referring to BERT or have any other specific requirements, please clarify, and I'd be happy to help!

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