A pretrained translation Transformers model can be used for long - text translation as follows: ### Model Selection First, select a suitable pre - trained translation model. For instance, models like `transformers.MarianMTModel` are well - known for translation tasks. These models have already been trained on large - scale multilingual datasets, providing a good starting point for translation. You can choose a model that is pre - trained for the specific language pair of your long text, e.g., if translating from English to German, pick a model trained on this language combination [^1]. ### Handling Long Text Long texts often exceed the maximum sequence length that a Transformer model can handle. To address this, the long text needs to be segmented. One common approach is to split the text into smaller paragraphs or sentences. The model then translates these segments separately. After translation, the translated segments are combined back into the final long - text translation. ### Decoding Strategy When using the `generate()` method to get the translation output, customize the decoding strategy. This can help reduce text repetition and make the translated text more coherent. For example, beam search or sampling - based strategies can be adjusted according to the characteristics of the long text. Modifying the decoding strategy will not change the model's trainable parameters but can significantly impact the quality of the translated output [^4]. ### Code Example ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM # Load the pre - trained translation model and tokenizer model_name = "Helsinki - NLP/opus - mt - en - de" # Example for English to German tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForSeq2SeqLM.from_pretrained(model_name) long_text = "Your long text to be translated here..." # Split the long text text_segments = long_text.split('. ') translated_segments = [] for segment in text_segments: input_ids = tokenizer(segment, return_tensors='pt').input_ids output = model.generate(input_ids) translated_text = tokenizer.decode(output[0], skip_special_tokens=True) translated_segments.append(translated_text) final_translation = '. '.join(translated_segments) print(final_translation) ```