Sold by: AUM Labs
Deployed on AWS
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Sentence Embedding based on the RoBERTa Model https://huggingface.co/FacebookAI/roberta-base for running on GPUs
Overview
RoBERTa base model: Pretrained model on the English language using a masked language modeling (MLM) objective. It was introduced in this paper (https://arxiv.org/abs/1907.11692 ) and first released in this repository (https://github.com/facebookresearch/fairseq/tree/main/examples/roberta ). This Service sets up an endpoint on AWS Sagemaker that returns text embeddings based on the RoBERTa model. Use this service if you plan to run it on GPU.
For running it on CPU use https://aws.amazon.com/marketplace/pp/prodview-nvrw726ubwj26
Keywords: Embedding, RoBERTa, BERT, Retrieval augmented generation, facebook, classifier, text, NLP
Highlights
- RoBERTa Model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks. If you have a dataset of labeled sentences for instance, you can train a standard classifier using the embedding produced by the RoBERTa model as inputs.
- RoBERTa (Robustly Optimized BERT Approach) is a transformer-based language model developed by Facebook AI as an improvement over the BERT (Bidirectional Encoder Representations from Transformers) model. Like BERT, it is designed for natural language processing (NLP) tasks such as text classification, question answering, sentiment analysis, building RAG solutions, and more. However, RoBERTa incorporates several optimizations to enhance its performance and scalability.
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Pricing
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Try this product free for 5 days according to the free trial terms set by the vendor.
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.g5.2xlarge Inference (Batch) Recommended | Model inference on the ml.g5.2xlarge instance type, batch mode | $0.30 |
ml.g5.2xlarge Inference (Real-Time) Recommended | Model inference on the ml.g5.2xlarge instance type, real-time mode | $0.30 |
ml.g5.xlarge Inference (Batch) | Model inference on the ml.g5.xlarge instance type, batch mode | $0.30 |
ml.g5.8xlarge Inference (Batch) | Model inference on the ml.g5.8xlarge instance type, batch mode | $0.30 |
ml.g5.12xlarge Inference (Batch) | Model inference on the ml.g5.12xlarge instance type, batch mode | $0.30 |
ml.g5.4xlarge Inference (Batch) | Model inference on the ml.g5.4xlarge instance type, batch mode | $0.30 |
ml.g5.48xlarge Inference (Batch) | Model inference on the ml.g5.48xlarge instance type, batch mode | $0.30 |
ml.g5.16xlarge Inference (Batch) | Model inference on the ml.g5.16xlarge instance type, batch mode | $0.30 |
ml.g5.24xlarge Inference (Batch) | Model inference on the ml.g5.24xlarge instance type, batch mode | $0.30 |
ml.g6.16xlarge Inference (Real-Time) | Model inference on the ml.g6.16xlarge instance type, real-time mode | $0.30 |
Dimensions summary
You pay by the host hour for running this English-language embedding model on your chosen GPU instance. Pricing splits into two inference modes. Batch mode runs on ml.g5 instances in eight sizes, from xlarge up to 48xlarge. Real-time mode runs on ml.g5 and ml.g6 instances, plus larger ml.p4d.24xlarge and ml.p5.48xlarge options. Within each mode, your rate scales with the instance size you select — larger instances carry more GPU capacity. You choose the mode and instance size that fit your workload, and billing follows actual usage hours.
Top-of-mind questions for buyers
One HostHrs unit is one hour that one instance runs the embedding model. Billing counts each active host-hour. If you run one instance for ten hours, you accrue ten host-hours. Running several instances at once adds their hours together on the same invoice.
Software charges meter running host-hours only. A fully stopped instance does not accrue host-hour charges. Stopped or paused instances may still incur separate AWS storage or infrastructure fees, but those are billed by AWS, not by this software listing.
Both meter per host-hour on your chosen instance. Batch mode runs on ml.g5 instances and processes grouped requests. Real-time mode runs on ml.g5, ml.g6, ml.p4d, and ml.p5 instances for on-demand responses. You pick the mode that matches your workload, and charges follow the hours that mode runs.
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Thank you for purchasing RoBERTa Embedding API on AWS Marketplace. We strive to ensure customer satisfaction with our services. If you have issues accessing the service contact us at support@aumlabs.ai
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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
Additional details
Inputs
- Summary
This model can analyze text passed as json or json text files stored in Amazon S3 bucket.
- Limitations for input type
- Maximum payload size for endpoint invocation is 5MB while the maximum payload size for batch inference is 100 MB.
{
"sentences": [
"Hello, how are you doing?",
"I am doing great, how about you?"
]
}
https://github.com/aumlabs/aws-marketplace-listing/blob/main/sagemaker/roberta/data/input/batch/input.jsonl
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
sentences | An array of English text | Type: FreeText | Yes |
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