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--- |
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extra_gated_heading: Access aimped/nlp-health-translation-base-de-en on Hugging Face |
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extra_gated_description: >- |
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This is a form to enable access to this model on Hugging Face after you have |
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been granted access from the Aimped. Please visit the [Aimped |
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website](https://aimped.ai/) to Sign Up and accept our Terms of Use and |
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Privacy Policy before submitting this form. Requests will be processed in 1-2 |
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days. |
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extra_gated_prompt: >- |
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**Your Hugging Face account email address MUST match the email you provide on |
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the Aimped website or your request will not be approved.** |
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extra_gated_button_content: Submit |
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extra_gated_fields: |
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I agree to share my name, email address, and username with Aimped and confirm that I have already been granted download access on the Aimped website: checkbox |
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license: cc-by-nc-4.0 |
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language: |
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- en |
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- de |
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metrics: |
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- bleu |
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pipeline_tag: translation |
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widget: |
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- text: >- |
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Neben der Impfung sind präventive Maßnahmen wie das Tragen von Masken, gute |
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Händehygiene und die Einhaltung physischer Distanzierung wichtig, um die |
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Übertragung des Virus zu kontrollieren. Wissenschaftliche Forschung und |
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öffentliche Gesundheitsmaßnahmen bleiben von entscheidender Bedeutung im |
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Kampf gegen die anhaltende Covid-19-Pandemie. |
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- text: >- |
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Allerdings sind weitere umfangreiche Studien und klinische Versuche |
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erforderlich, um die Wirksamkeit und Sicherheit dieser Ansätze zu validieren |
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und eine maßgeschneiderte und individualisierte Behandlung für Patienten mit |
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Hirntumoren zu entwickeln. |
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tags: |
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- medical |
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- translation |
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- medical translation |
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datasets: |
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- aimped/medical-translation-test-set |
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--- |
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<p align="center"> |
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<img src="https://raw.githubusercontent.com/ai-amplified/models/main/media/AimpedLogoDark.svg" alt="aimped logo" width="50%" height="50%"/> |
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</p> |
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# Description of the Model |
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<p> |
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Paper: <a href="https://arxiv.org/abs/2407.12126" style="text-decoration: underline; color: blue;">LLMs-in-the-loop Part-1: Expert Small AI Models for Bio-Medical Text Translation</a> |
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</p> |
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<p style="margin-bottom: 0in; text-align: justify; line-height: 1.3;"><span style="font-family: "IBM Plex Sans", sans-serif; font-size: 16px;">The Medical Translation AI model represents a specialized language model, trained for the accurate translations of medical documents from German to English. Its primary objective is to provide healthcare professionals, researchers, and individuals within the medical field with a reliable tool for the precise translation of a wide spectrum of medical documents. </span></p> |
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<p style="margin-bottom: 0in; text-align: justify; line-height: 1.3;"> |
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<span style="font-family: "IBM Plex Sans", sans-serif; font-size: 16px;">The development of this model entailed the utilization of the |
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<a href="https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/de-en/README.md" style="text-decoration: underline; color: blue;">Hensinki/MarianMT</a> neural translation architecture, which required 2+ days of intensive training using A100 (24G RAM) GPU. To create an exceptionally high-quality corpus for training the translation model, we combined both publicly available and proprietary datasets. These datasets were further enriched by meticulously curated text collected from online sources. In addition, the inclusion of clinical and discharge reports from diverse healthcare institutions enhanced the dataset's depth and diversity. This meticulous curation process plays a pivotal role in ensuring the model's ability to generate accurate translations tailored specifically to the medical domain, meeting the stringent standards expected by our users.<br><br>The versatility of the Medical Translation AI model extends to the translation of a wide array of healthcare-related documents, encompassing medical reports, patient records, medication instructions, research manuscripts, clinical trial documents, and more. By harnessing the capabilities of this model, users can efficiently and dependably obtain translations, thereby streamlining and expediting the often complex task of language translation within the medical field.</span> |
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</p> |
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<p style="margin-bottom: 0in; text-align: justify; line-height: 1.3;"><span style="font-family: "IBM Plex Sans", sans-serif; font-size: 16px;">The model we have developed outperforms leading translation companies like Google, Helsinki-Opus/MarianMT, and DeepL when compared against our meticulously curated proprietary test data set. </span></p> |
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<p style="line-height: 1.3; margin-bottom: 0in; text-align: justify;"><br></p> |
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<table style="border-collapse: collapse; width: 605px; height: 117px;"> |
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<tbody> |
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<tr> |
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<td style="width: 19.5041%;"><br></td> |
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<td style="width: 20.6612%; text-align: center; color: rgb(255, 255, 255);"><span style="font-size: 16px;"><strong>ROUGE</strong></span><br></td> |
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<td style="width: 20%; text-align: center; color: rgb(255, 255, 255);"><span style="font-size: 16px;"><strong>BLEU</strong></span><br></td> |
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<td style="width: 20%; text-align: center; color: rgb(255, 255, 255);"><span style="font-size: 16px;"><strong>METEOR</strong></span><br></td> |
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<td style="width: 20%; text-align: center; color: rgb(255, 255, 255);"><span style="font-size: 16px;"><strong>BERT</strong></span><br></td> |
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</tr> |
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<tr> |
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<td style="text-align: center;"><span style="font-size: 16px;">Aimped</span><br></td> |
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<td style="text-align: center;"><span style="font-size: 16px;">0.84</span></td> |
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<td style="width: 20%; text-align: center;"><span style="font-size: 16px;">0.65</span></td> |
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<td style="width: 20%; text-align: center;"><span style="font-size: 16px;">0.83</span></td> |
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<td style="width: 20%; text-align: center;"><span style="font-size: 16px;">0.97</span></td> |
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</tr> |
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<tr> |
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<td style="text-align: center;"><span style="font-size: 16px;">Google</span></td> |
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<td style="text-align: center;"><span style="font-size: 16px;">0.82</span></td> |
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<td style="width: 20%; text-align: center;"><span style="font-size: 16px;">0.60</span></td> |
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<td style="width: 20%; text-align: center;"><span style="font-size: 16px;">0.81</span></td> |
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<td style="width: 20%; text-align: center;"><span style="font-size: 16px;">0.94</span></td> |
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</tr> |
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<tr> |
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<td style="text-align: center;"><span style="font-size: 16px;">DeepL</span></td> |
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<td style="text-align: center;"><span style="font-size: 16px;">0.82</span></td> |
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<td style="width: 20%; text-align: center;"><span style="font-size: 16px;">0.60</span></td> |
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<td style="width: 20%; text-align: center;"><span style="font-size: 16px;">0.81</span></td> |
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<td style="width: 20%; text-align: center;"><span style="font-size: 16px;">0.93</span></td> |
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</tr> |
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<tr> |
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<td style="text-align: center;"><span style="font-size: 16px;">Opus/MarianMT</span></td> |
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<td style="text-align: center;"><span style="font-size: 16px;">0.72</span></td> |
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<td style="width: 20%; text-align: center;"><span style="font-size: 16px;">0.46</span></td> |
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<td style="width: 20%; text-align: center;"><span style="font-size: 16px;">0.72</span></td> |
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<td style="width: 20%; text-align: center;"><span style="font-size: 16px;">0.90</span></td> |
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</tr> |
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</tbody> |
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</table> |
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## Why should you use Aimped API? |
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To get started, you can easily use our open-source version of the models for research purposes. However, the models provided through the Aimped API are trained on new data every three months. This ensures that the models understand ongoing healthcare developments in the world and can identify the most relevant medical terminology without a knowledge cutoff. In addition, we implement post/pre processing steps to improve the translation quality. Naturally, our quality control ensures that the models' performance always remains at least similar to previous versions. |
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<p style="line-height: 1.3;"><strong style="font-family: "IBM Plex Sans", sans-serif; background-color: transparent; text-align: justify; font-size: 16px;">Text Format Requirements: </strong><span style="font-family: "IBM Plex Sans", sans-serif; background-color: transparent; text-align: justify; font-size: 16px;">The text to be translated must adhere to a structured and grammatically correct format, including proper paragraph and sentence structures. Spelling errors or formatting issues, such as line breaks occurring before the completion of a sentence, will not be automatically corrected.</span><br></p> |
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<p><br></p> |
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p { line-height: 115%; margin-bottom: 0.1in; background: transparent } |
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</style> |
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@page { size: 8.27in 11.69in; margin: 0.79in } |
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p { line-height: 115%; margin-bottom: 0.1in; background: transparent } |
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</style> |
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<style type="text/css"> |
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@page { size: 8.27in 11.69in; margin: 0.79in } |
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p { line-height: 115%; margin-bottom: 0.1in; background: transparent } |
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a:link { color: #000080; text-decoration: underline } |
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a:visited { color: #800000; text-decoration: underline } |
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</style> |
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## How to Use: |
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To get the right results, use this function. |
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|
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- Install requirements |
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```python |
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!pip install transformers |
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!pip install sentencepiece |
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!pip install aimped |
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import nltk |
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nltk.download('punkt') |
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``` |
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- import libraries |
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```python |
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline |
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from aimped.nlp.translation import text_translate |
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import torch |
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device = "cuda" if torch.cuda.is_available() else "cpu" |
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``` |
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- load model |
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```python |
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model_path = "aimped/nlp-health-translation-base-de-en" |
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tokenizer = AutoTokenizer.from_pretrained(model_path) |
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model = AutoModelForSeq2SeqLM.from_pretrained(model_path) |
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``` |
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```python |
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translater = pipeline( |
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task="translation_de_to_en", |
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model=model, |
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tokenizer=tokenizer, |
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device= device, |
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max_length=512, |
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num_beams=7, |
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early_stopping=False, |
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num_return_sequences=1, |
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do_sample=False, |
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) |
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``` |
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- Use Model: |
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```python |
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sentence = "Allerdings sind weitere umfangreiche Studien und klinische Versuche erforderlich, um die Wirksamkeit und Sicherheit dieser Ansätze zu validieren und eine maßgeschneiderte und individualisierte Behandlung für Patienten mit Hirntumoren zu entwickeln." |
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translated_text = text_translate([sentence],source_lang="de", pipeline=translater) |
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``` |
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## Test Set |
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<p><span style="font-family: "IBM Plex Sans", sans-serif; font-size: 16px;">Trainin data: Public and in-house datasets.</span></p> |
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<p><span style="font-family: "IBM Plex Sans", sans-serif; font-size: 16px;">Test data: Public and in-house datasets which is available <a href="https://github.com/ai-amplified/models/tree/main/medical_translation/test_data/en-de pairs">here</a>.</span></p> |
