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.

Here is an example on how to query the service from the AWS CLI

```

aws sagemaker-runtime invoke-endpoint \

--endpoint-name "roberta-embedding" \

--body '{"sentences": ["Hello, how are you doing?", "I am doing great, how about you?"]}' \

--content-type "application/json" \

--region "us-east-1" \

--cli-binary-format raw-in-base64-out \

output.json

```

This will return a list of embeddings for each text in the input:

```

{"embeddings":[[-0.06, 0.07, ..], [-0.01, -0.03, ..]]}

```