Document grouping is a solution based on unsupervised machine learning that takes textual information and identifies topics across the given text corpus. Documents are grouped based on similarity of syntactic and contextual information present in them. This model takes a maximum of 30 documents...
Model Package - Fulfilled on Amazon SageMaker
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Trained on a diverse range of medical texts, this model excels in summarizing complex clinical notes, patient encounters, critical care annotations, discharge summaries, and various medical reports into concise and easily digestible summaries. For medical doctors, this tool provides a rapid...
Model Package - Fulfilled on Amazon SageMaker
HARMAN's LLM Professional Service empowers organizations with deep domain insights tailored to their specific industries. For example, our HealthGPT solution is a powerful, private LLM fine-tuned on healthcare-specific datasets, such as clinical trial data and medical records. Our professional...
This is a Text Classification model built upon a Text Embedding model from [TensorFlow Hub](https://tfhub.dev/tensorflow/bert_en_cased_L-24_H-1024_A-16/2). It takes a text string as input and classifies the input text as either a positive or negative movie review. The Text Embedding model which is...
Model Package - Fulfilled on Amazon SageMaker
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This is the quantized version of Solar Mini Chat ja, which is the Japanese specialized version of Solar Mini Chat. Solar Mini Chat ja is a compact yet powerful advanced large English/Japanese/Korean language model specialized in Japanese. It is specifically fine-tuned for multi-turn chat purposes,...
Model Package - Fulfilled on Amazon SageMaker
Building Your Summarizer After moving your document data into AWS via an ETL pipeline, we build a custom solution to process and summarize your data. Using Amazon Textract, Amazon Comprehend, and other techniques and services, we extract the data and add sufficient structure to allow for...
This is a Extractive Question Answering model built upon a Text Embedding model from [PyTorch Hub](https://pytorch.org/hub/huggingface_pytorch-transformers/). It takes as input a pair of question-context strings, and returns a sub-string from the context as a answer to the question. The Text...
Model Package - Fulfilled on Amazon SageMaker
Small medical practices and healthcare organizations can leverage the power of AWS to enhance their operations and gain valuable insights into patient care and practice management. Within a 3-week proof-of-concept timeframe, and with an estimated annual budget of $25,000 to $50,000, practices can...
This is a Extractive Question Answering model built upon a Text Embedding model from [PyTorch Hub](https://pytorch.org/hub/huggingface_pytorch-transformers/). It takes as input a pair of question-context strings, and returns a sub-string from the context as a answer to the question. The Text...
Model Package - Fulfilled on Amazon SageMaker
Revolutionize legal document interpretation with our automated solution. Bid farewell to painstaking hours – even days – spent manually extracting and formatting information from documents for input into internal systems. Our cutting-edge AI and RPA-powered approach transform this process into an...
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