Input

Usage Methodology for the algorithm:

• The input has to be a '.txt' file with 'utf-8' encoding. PLEASE NOTE: If your input .txt file is not 'utf-8' encoded, model will not perform as expected
• To make sure that your input file is 'UTF-8' encoded please 'Save As' using Encoding as 'UTF-8'
• The input can have a maximum of 512 words (Sagemaker restriction)
• Input should have atleast 3 sentences (Model limitation)
• Supported content types: text/plain

Output

Content type: text/plain

Invoking endpoint

AWS CLI Command

If you are using real time inferencing, please create the endpoint first and then use the following command to invoke it:

aws sagemaker-runtime invoke-endpoint --endpoint-name "endpoint-name" --body fileb://input.txt --content-type text/plain --accept text/plain result.txt

Substitute the following parameters:

• "endpoint-name" - name of the inference endpoint where the model is deployed
• input.txt - input file
• text/plain - MIME type of the given input file (above)
• result.txt - filename where the inference results are written to.

Python

Real-time inference snippet (more detailed example can be found in sample notebook): sample_txt = 'location of input text file' transformer = model.transformer(1, 'ml.m5.xlarge') transformer.transform(sample_txt, content_type="text/plain") transformer.wait() print("Batch Transform output saved to " + transformer.output_path)

Sample Notebook :https://tinyurl.com/yyu32g32 Sample Input : https://tinyurl.com/tx94grp Sample Output: https://tinyurl.com/wnzfy9c