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Names Entity Recognition - NER (21 results) showing 1 - 10

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Jurassic-2 Light is the quickest large language model (LLM) by AI21 Labs and is available for you to deploy in your private environment. Small but mighty, Jurassic-2 Light is ideal for simple language tasks that require maximum affordability and minimal latency in your private environment. Common...

Model Package - Fulfilled on Amazon SageMaker is an independent provider of innovative Natural Language Processing (NLP) solutions in multiple languages combined with AI and Machine Learning (ML). With hyScore analyze you use a NLP solution that provides valuable insights from any text or website in real-time as a structured JSON...

Starting from $1.00 to $2.00/hr for software + AWS usage fees

Scan text for locations, extract addresses, street intersections, postal codes, zip codes and place names. Geocode the extracted locations into Latitude,Longitude,Elevation and export the results as GeoJson, Json, XML or CSV. Reverse geocode coordinates to addresses, place names, postal codes, zip...

Linux/Unix, Amazon Linux Any - 64-bit Amazon Machine Image (AMI)

Named Entity Extraction API can identify and extract individuals, places, animals, plants, historical figures, monuments, organizations, and other various types of entities from a given body of text. As an output, the API lists different named entities across different categories like name, place,...

Mphasis DeepInsights Geopolitical Entity Recognizer is an efficient way of identifying geopolitical entities present in the corpus of text. This solution applies NLP techniques to extract the geopolitical entities which can be used for providing useful insights about the text and further text...

Model Package - Fulfilled on Amazon SageMaker

This solution identifies and anonymizes Personally Identifiable Information like Name, SSN, Email, Phone numbers from tabular data. The Solution is designed to work on structured data source.

Model Package - Fulfilled on Amazon SageMaker

This solution helps users automate the coherent summary generation from documents. It identifies frequently used entity clusters in the document to capture salient and most important candidate sentences. An abstractive summary is generated from these sentences using reinforcement learning to...

Model Package - Fulfilled on Amazon SageMaker

Legal entity name extraction is an optimal way to identify and classify legal organization name and their aliases in an unstructured text. It can consume the texts such as legal documents and process it to identify all the legal entities/aliases in the document.

Model Package - Fulfilled on Amazon SageMaker

This solution creates a knowledge graph based on entity-name pairs from data collected from multiple sources of information such as Wikipedia, company's website, CrunchBase etc. This solution creates a graph model of a company's profile based on unstructured data.

Model Package - Fulfilled on Amazon SageMaker