--- license: cc-by-nc-4.0 language: - en - zh metrics: - bleu base_model: facebook/nllb-200-distilled-600M pipeline_tag: translation library_name: transformers --- ## Overview The **medical-nllb-200-zh2en** project designed specifically for translating medical texts from Chinese to English. Built on the No Language Left Behind (NLLB) framework, this model aims to enhance the accuracy and fluency of translations in the medical domain, facilitating better communication and understanding in healthcare settings. ## Features - **High Accuracy**: Utilizes advanced NLLB technology for precise translations. - **Medical Focus**: Specific for translating medical literature, patient records, and related documents. - **User-Friendly**: Easy integration into existing workflows and applications. ## Installation To use the model, you need to have the `transformers` library installed. You can install it via pip: ```bash pip install transformers ``` ## Usage Here’s a simple example demonstrating how to use the **medical-nllb-200-zh2en** model for translation: ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM # Load the tokenizer and model model_name = "6yuru99/medical-nllb-200-en2zh_hant" model = AutoModelForSeq2SeqLM.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name, src_lang="zho_Hant") # Example Chinese medical text input_text = "患者有高血壓病史,需定期檢查血壓。" # Tokenize the input text inputs = tokenizer(input_text, return_tensors="pt") # Generate translation translated_tokens = model.generate(**inputs, forced_bos_token_id=tokenizer.convert_tokens_to_ids("eng_Latn")) # Decode the translated text outputs = tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0] print("Translated Text:", outputs) ``` ## Contributing Welcome contributions to improve this project! If you have suggestions or improvements, please feel free to submit a pull request or open an issue. ## Contact For any inquiries or feedback, please contact the project maintainer at [6yuru99@gmail.com].