Sold by: Mphasis
Deployed on AWS
This solution helps in processing textual data to identify named entities present in the corpus of text.
Overview
Mphasis DeepInsights Named Entity Recognizer is an efficient way of identifying named entities present in the corpus of text. This solution applies NLP techniques to extract the named entities which can be used for further text analytics and for providing useful insights about the text. The model takes a corpus of text as input file, processes it and provides a CSV file containing all the named entities present in the text as output.
Highlights
- Mphasis DeepInsights Named Entity Recognizer is a Natural Language Processing based solution for extracting named entities such as person, organization, geopolitical entities and various others present in the text data. This solution enables business users to analyse large volume of unstructured text and identifying named entities to build better search solutions, provide content recommendations, provide better customer support, SEO, virtual assistant etc.
- The model can take a maximum of 350kb ( ~ 2500 rows of text) as input and extract named entities present in the text.
- Mphasis DeepInsights is a cloud-based cognitive computing platform that offers data extraction & predictive analytics capabilities. Need Customized Deep learning and Machine Learning Solutions? Get in Touch!
Details
Sold by
Delivery method
Latest version
Deployed on AWS
New
Introducing multi-product solutions
You can now purchase comprehensive solutions tailored to use cases and industries.
Features and programs
Financing for AWS Marketplace purchases
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
Pricing
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/host/hour |
|---|---|---|
ml.m5.large Inference (Batch) Recommended | Model inference on the ml.m5.large instance type, batch mode | $10.00 |
ml.m5.large Inference (Real-Time) Recommended | Model inference on the ml.m5.large instance type, real-time mode | $5.00 |
ml.m4.4xlarge Inference (Batch) | Model inference on the ml.m4.4xlarge instance type, batch mode | $10.00 |
ml.m5.4xlarge Inference (Batch) | Model inference on the ml.m5.4xlarge instance type, batch mode | $10.00 |
ml.m5.12xlarge Inference (Batch) | Model inference on the ml.m5.12xlarge instance type, batch mode | $10.00 |
ml.m4.16xlarge Inference (Batch) | Model inference on the ml.m4.16xlarge instance type, batch mode | $10.00 |
ml.m5.2xlarge Inference (Batch) | Model inference on the ml.m5.2xlarge instance type, batch mode | $10.00 |
ml.c4.4xlarge Inference (Batch) | Model inference on the ml.c4.4xlarge instance type, batch mode | $10.00 |
ml.m5.xlarge Inference (Batch) | Model inference on the ml.m5.xlarge instance type, batch mode | $10.00 |
ml.c5.9xlarge Inference (Batch) | Model inference on the ml.c5.9xlarge instance type, batch mode | $10.00 |
Dimensions summary
You pay by the hour for the compute instance that runs the named entity recognition model. Pricing splits into two processing modes. Batch mode handles inference on grouped data, while real-time mode processes requests as they arrive. Within each mode, you choose an AWS SageMaker instance type. Options range from smaller general-purpose and compute-optimized instances to large multi-core sizes. Larger or more powerful instances carry higher hourly rates. You are billed per host hour based on the instance you select and how long it runs. No upfront commitment applies.
Top-of-mind questions for buyers
One host hour is one clock hour that a single chosen SageMaker instance runs the model. You are billed for the running time of that instance. Rates differ by instance type. Stopping the instance stops the software host-hour charges, though AWS may still bill related storage or infrastructure separately.
Batch mode processes grouped data in scheduled runs, so you pay host hours only while a batch job runs. Real-time mode keeps an instance running to answer requests as they arrive, so charges accrue continuously while that endpoint stays active. Each mode meters host hours independently.
The model reads text extracted from documents and identifies named entities within it. The platform ingests data from many file types, including images, PDFs, emails, and HTML, then applies natural language processing. Your host-hour charges cover the compute time this inference work consumes on your selected instance.
www.mphasis.com
Helpful?
Vendor refund policy
Currently we do not support refunds, but you can cancel your subscription to the service at any time.
Legal
Vendor terms and conditions
Upon subscribing to this product, you must acknowledge and agree to the terms and conditions outlined in the vendor's End User License Agreement (EULA) .
Content disclaimer
Vendors are responsible for their product descriptions and other product content. AWS does not warrant that vendors' product descriptions or other product content are accurate, complete, reliable, current, or error-free.
Delivery details
Amazon SageMaker model
An Amazon SageMaker model package is a pre-trained machine learning model ready to use without additional training. Use the model package to create a model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.
Deploy the model on Amazon SageMaker AI using the following options:
Version release notes
Bug Fixes and Performance Improvement
Additional details
Inputs
- Summary
- Supported content types: text/csv
- The input has to be a '.txt' file with 'utf-8' encoding.
- Limitations for input type
- Maximum input file size: 350kb ( ~ 2500 rows of text)
- Input MIME type
- text/csv, text/plain
https://github.com/Mphasis-ML-Marketplace/DeepInsights-Named-Entity-Recognizer/tree/main/Input
https://github.com/Mphasis-ML-Marketplace/DeepInsights-Named-Entity-Recognizer/tree/main/Input
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
Input.txt | Input will be text file which will contain the textual data from which the named entities will be extracted. | Type: FreeText | Yes |
Resources
Vendor resources
Support
Vendor support
For any assistance reach out to us at:
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
Similar products
Mphasis DeepInsights Text Summarizer helps in summarizing text documents.
The solution paraphrases textual data using Natural Language Processing techniques.
Machine Learning based solution which extracts important Key phrases/words from a corpus of text.
Mphasis DeepInsights Table Detection helps in detecting and locating the tabulated information from scanned images.
This NLP based solution finds meaningful and frequent n-grams/phrases of words in the provided text.
Customer reviews
No customer reviews yet
Be the first to review this product . We've partnered with PeerSpot to gather customer feedback. You can share your experience by writing or recording a review, or scheduling a call with a PeerSpot analyst.