Sold by: Lelapa AI
Deployed on AWS
Use Lelapa AI's Vulavula Multilingual Sentiment Analysis Model to analyze and categorize sentiments in text across isiZulu.
Overview
At Lelapa AI, we are dedicated to enhancing language technology and expanding its accessibility with our Vulavula Multilingual Sentiment Analysis Model, tailored for Africa's rich linguistic landscape. This cutting-edge model accurately assesses and classifies sentiments in text, capturing emotions and opinions specifically in isiZulu. By transforming raw text into an organized format, it facilitates deeper understanding and supports more refined interactions, particularly in chatbot systems. This robust tool improves data analysis, enabling more advanced applications in multilingual contexts.
Highlights
- Comprehensive Emotion Detection: Lelapa AI presents the Vulavula Sentiment Analysis Model, meticulously crafted to interpret and analyze text for sentiment in isiZulu, providing a nuanced understanding of emotions and opinions within African languages
- Enhanced Accuracy and Efficiency: Leveraging state-of-the-art technology, the Vulavula Sentiment Analysis Model delivers precise sentiment categorization with exceptional speed and accuracy, surpassing traditional models and significantly improving the quality of user interactions and data insights.
- Versatile Use Cases: Tailored to meet diverse requirements, Lelapa AI's sentiment analysis model is ideal for a variety of applications, including sentiment monitoring, customer feedback analysis, and personalized content recommendations in isiZulu, making it an invaluable asset for developers and businesses focused on 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. Two options set how the model processes your data. Batch mode handles uploaded audio in bulk. Real-time mode processes conversations as they happen. Both bill per host hour, so your cost scales with how long the instance runs. You choose the mode that fits your workflow. Neither is a fixed subscription; charges depend entirely on your usage time.
Top-of-mind questions for buyers
You get model inference running on a single ml.m5.xlarge instance. That is a general-purpose compute instance sized for running the sentiment model. Billing counts each hour the instance stays running, per host hour. You pay for one instance's active runtime, not per API call or per data volume.
Both modes bill per host hour while the instance runs. Batch mode processes uploaded audio in bulk, so charges accrue for the time the instance runs your batch jobs. Real-time mode processes live conversations, so charges accrue while the instance stays active. Stopping the instance stops the software host-hour charges.
Both meter host hours the same way, but suit different workloads. Batch mode fits uploaded audio processed after calls end, so you run the instance during scheduled processing. Real-time mode fits live transcription and translation during calls, so the instance runs continuously through active conversations. You choose the mode matching your workflow.
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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
This NER model was built brilliant researchers at Lelapa AI,
Le modeli ye-NER yakhiwe abacwaningi abahlakaniphile e-Lelapa AI
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 | Raw text in isiZulu for sentiment analysis. | Type: FreeText | Yes |
Support
Vendor support
Vulavula Multilingual Sentiment Analysis 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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