Sentiment Analysis uses a state of art transformer based natural language processing model. The product provides accurate, real-time sentiment analysis, enabling businesses to understand customer emotions and opinions better. This enables you to gain valuable insights from vast amounts of textual...
Model Package - Fulfilled on Amazon SageMaker
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
Natural Language Sentence Generator helps ML practitioners to significantly increase the diversity of data available for training models, without actually collecting new data. The effectiveness of machine learning based classifiers largely depends on the size and quality of the training data. This ...
Model Package - Fulfilled on Amazon SageMaker
Starting from $0.10 to $0.10/hr for software + AWS usage fees
This is a repackaged open-source software product wherein additional charges apply for technical support and maintenance by Apps4Rent. Embark on an enriching experience with Optuna on Linux Operating Systems re-packaged by Apps4Rent for AI/ML usage on the AWS Marketplace. This powerful combination...
Linux/Unix, Ubuntu 20.04LTS - 64-bit Amazon Machine Image (AMI)
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/Korean/Japanese 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
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
Unfortunately, traditional Contact Center knowledge management can function like a sort of guessing game. It relies on an agent’s ability to discern customer context, put together the right set of search terms, and skim the articles each search returns to find a relevant answer. Artificial...
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