Sold by: Lelapa AI
Deployed on AWS
Use Lelapa AI's Vulavula Multilingual NER Model to identify and categorize named entities in text across multiple African languages
Overview
At Lelapa AI, we're committed to advancing language technology and broadening its reach with our specialized Vulavula Multilingual Named Entity Recognition (NER) Model, particularly designed for Africa's linguistic diversity. This innovative model efficiently identifies and categorizes named entities in text, such as names, places, and organizations, across several key African languages. By converting raw text into a structured format, it provides deeper insights and supports nuanced interactions, especially in chatbot applications. Currently operational in English, Afrikaans, isiZulu, and Sesotho, our efforts continue as we plan to extend support to additional South African and Sub-Saharan languages. This powerful tool enhances data extraction processes, enabling more sophisticated analysis and applications in multilingual environments.
Highlights
- Expansive Language Support: Lelapa AI introduces the Vulavula NER Model, engineered to comprehend and process text across a wide array of African languages, significantly expanding access to advanced NLP tools beyond dominant global languages.
- Precision and Speed: With its cutting-edge technology, the Vulavula NER Model excels in swiftly identifying and classifying named entities with a higher degree of accuracy than conventional models, enhancing both user experience and data utility.
- Adaptable Applications: Designed to support diverse needs, Lelapa AI's model is perfect for a range of applications including semantic search, precise text analysis, and dynamic content moderation in multilingual environments, making it a robust tool for developers and businesses targeting African markets.
Details
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Latest version
Deployed on AWS
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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.xlarge Inference (Batch) Recommended | Model inference on the ml.m5.xlarge instance type, batch mode | $18.00 |
ml.m5.xlarge Inference (Real-Time) Recommended | Model inference on the ml.m5.xlarge instance type, real-time mode | $10.00 |
Dimensions summary
You pay by the hour for model inference running on the ml.m5.xlarge instance type. Pricing is usage-based, billed per host hour the instance runs. Two options split by delivery mode. Batch mode processes uploaded content in bulk, useful for after-the-fact transcription or translation. Real-time mode handles live requests as they happen. Both run on the same instance size, so cost differs by how you deliver the work, not by capacity. You choose the mode that fits your workflow and pay only for the hours you use.
Top-of-mind questions for buyers
You pay for each hour the ml.m5.xlarge instance runs your inference workload. One host hour equals one hour of that instance being active, regardless of how many requests it processes during that hour. Billing meters running time, so idle instances that are shut down stop accruing software charges.
Both modes run on the same instance size and bill per host hour. Batch mode processes uploaded audio or text in bulk after the fact, so the instance runs during processing windows. Real-time mode keeps the instance running to handle live requests as they arrive. Cost differs by how long the instance stays active.
Yes. The two dimensions bill independently, each metering its own instance host hours. Running both means you pay for each active instance separately, and both charges appear on the same invoice. Choose one mode or both based on whether your work is bulk processing, live requests, or a mix.
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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
This is the first version
Additional details
Inputs
- Summary
The model accepts text/csv and application/json requests that specifies the input text .
text = "This is a test"
- Input MIME type
- text/csv
https://github.com/Lelapa-AI/lelapa-aws/blob/main/lelapa_aws/input.csv
https://github.com/Lelapa-AI/lelapa-aws/blob/main/lelapa_aws/input.csv
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
text | This can be a paragraph or a very large article with so many paragraphs . | Type: FreeText | Yes |
Support
Vendor support
Lelapa AI Multilingual Named Entity Recognition (NER) Model support@lelapa.ai
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.
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