Sold by: MphasisÂ
Overview
Document Similarity solution helps a user find pairwise similarity between documents. This will help in identifying whether two documents have similar verbatim and contextual information. Higher similarity value means documents have very similar contextual information and are written in similar verbatim. This helps in removing duplicate documents from a set.
Highlights
- An unsupervised text mining solution to find similarity between documents based on content and context. An intuitive similarity score that ranges from 0 (low similarity) to 1 (high similarity) is computed that can be used to segregate documents belonging to distinct subject groups.
- Some of the practical applications for this solution include: 1) Optimization of time taken in searching for relevant information such as de-duplication of search engine results. 2) Grouping documents with similar content. 3) Recommending personalised learning paths for effective knowledge interventions.
- 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
Unlock automation with AI agent solutions
Fast-track AI initiatives with agents, tools, and solutions from AWS Partners.
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 | $20.00 |
ml.t2.medium Inference (Real-Time) Recommended | Model inference on the ml.t2.medium instance type, real-time mode | $10.00 |
ml.m4.4xlarge Inference (Batch) | Model inference on the ml.m4.4xlarge instance type, batch mode | $20.00 |
ml.m5.4xlarge Inference (Batch) | Model inference on the ml.m5.4xlarge instance type, batch mode | $20.00 |
ml.m4.16xlarge Inference (Batch) | Model inference on the ml.m4.16xlarge instance type, batch mode | $20.00 |
ml.m5.2xlarge Inference (Batch) | Model inference on the ml.m5.2xlarge instance type, batch mode | $20.00 |
ml.p3.16xlarge Inference (Batch) | Model inference on the ml.p3.16xlarge instance type, batch mode | $20.00 |
ml.m4.2xlarge Inference (Batch) | Model inference on the ml.m4.2xlarge instance type, batch mode | $20.00 |
ml.c5.2xlarge Inference (Batch) | Model inference on the ml.c5.2xlarge instance type, batch mode | $20.00 |
ml.p3.2xlarge Inference (Batch) | Model inference on the ml.p3.2xlarge instance type, batch mode | $20.00 |
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)Â .
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
Input
- The input file should be a zip of text files (.txt) in utf-8 encoding
- The zipped file can have a maximum of 20 documents
- The maximum size of the each file should be <= 10KB (1000 lines)
- Supported Content type: application/zip
Output
- The output from the model is a csv file, supported content type: text/csv
- Output file contains a document to document matrix with scores of similarity indices between 0 to 1 interpreted as:
- 0 being least similar
- 1 being most similar
- Sample output file: |----------------| Document-1 | Document-2 | Document-3 | | Document-1 | 1 | 0.167 | 0.648 | | Document-2 | 0.167 | 1 | 0.070 | | Document-3 | 0.648 | 0.070 | 1 |
Invoking endpoint
AWS CLI Command
If you are using real time inferencing, please create the endpoint first and then use the following command to invoke it:
aws sagemaker-runtime invoke-endpoint --endpoint-name "endpoint-name" --body fileb://input.zip --content-type application/zip --accept text/csv result.csvSubstitute the following parameters:
- endpoint-name - name of the inference endpoint where the model is deployed
- input.zip - input file
- application/zip - MIME type of the given input file (above)
- result.csv - filename where the inference results are written to.
Resources
- Input MIME type
- application/zip
See Input Summary
See Input Summary
Resources
Vendor resources
Support
Vendor support
For any assistance, please reach out 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.
Mphasis DeepInsights Table Detection helps in detecting and locating the tabulated information from scanned images.
The solution paraphrases textual data using Natural Language Processing techniques.
This solution helps in processing textual data to identify named entities present in the corpus of text.
An NLP based approach to identify relationship among entities in a corpus of text and present them as triplets of Subject-Predicate-Object.
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.