Sold by: AUM Labs
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Sentence Embedding based on the RoBERTa Model https://huggingface.co/FacebookAI/roberta-base
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.
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.
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.
Details
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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.m5.xlarge Inference (Batch) Recommended | Model inference on the ml.m5.xlarge instance type, batch mode | $0.10 |
ml.m5.xlarge Inference (Real-Time) Recommended | Model inference on the ml.m5.xlarge instance type, real-time mode | $0.10 |
ml.m4.4xlarge Inference (Batch) | Model inference on the ml.m4.4xlarge instance type, batch mode | $0.10 |
ml.m5.4xlarge Inference (Batch) | Model inference on the ml.m5.4xlarge instance type, batch mode | $0.10 |
ml.m5.12xlarge Inference (Batch) | Model inference on the ml.m5.12xlarge instance type, batch mode | $0.10 |
ml.m5.large Inference (Batch) | Model inference on the ml.m5.large instance type, batch mode | $0.10 |
ml.m4.16xlarge Inference (Batch) | Model inference on the ml.m4.16xlarge instance type, batch mode | $0.10 |
ml.m5.2xlarge Inference (Batch) | Model inference on the ml.m5.2xlarge instance type, batch mode | $0.10 |
ml.m4.10xlarge Inference (Batch) | Model inference on the ml.m4.10xlarge instance type, batch mode | $0.10 |
ml.m5.24xlarge Inference (Batch) | Model inference on the ml.m5.24xlarge instance type, batch mode | $0.10 |
Dimensions summary
You pay by the hour for each host running RoBERTa Embedding on your chosen compute instance. Pricing falls into two inference modes. Batch mode covers a smaller set of ml.m4 and ml.m5 instances for processing text in groups. Real-time mode covers a wider range, including ml.m4, ml.m5, ml.m5d, ml.m6g, ml.m6gd, ml.m7i, and ml.t2 families, for live requests. Within each mode, hourly rates scale with instance size, from large through 48xlarge. You pick the mode and instance that fit your workload, then pay only for hours used.
Top-of-mind questions for buyers
One host-hour means one running inference instance billed for each hour it is active. Each instance you launch meters separately. If you run two instances, you pay two host-hours per clock hour. Charges track actual running time on the compute instance you selected.
Software charges accrue per running host-hour. A fully stopped instance stops accruing the software fee. Underlying AWS infrastructure charges, such as storage, may still apply while an instance is stopped. The software meter counts only hours the instance actively runs.
Both meter per host-hour on the instance you choose. Batch mode processes text in grouped jobs and covers select ml.m4 and ml.m5 instances. Real-time mode serves live requests continuously and covers more instance families. You pay for hours the chosen instance runs in either mode.
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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 |
Resources
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Vendor support
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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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