[GitHub] [beam] tvalentyn commented on a diff in pull request #22069: Reviewing the RunInference ReadMe file for clarity.

[GitBox <gi...@apache.org>]

tvalentyn commented on code in PR #22069:
URL: https://github.com/apache/beam/pull/22069#discussion_r922416093


##########
sdks/python/apache_beam/examples/inference/README.md:
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@@ -218,16 +228,19 @@ is the word that the model predicts for the mask.
 The pipeline reads rows of pixels corresponding to a digit, performs basic preprocessing, passes the pixels to the Scikit-learn implementation of RunInference, and then writes the predictions to a text file.
 
 ### Dataset and model for language modeling
-- **Required**: A path to a file called `INPUT` that contains label and pixels to feed into the model. Each row should have elements that are comma-separated. The first element is the label. All subsuequent elements would be pixel values. It should look something like this:
+
+To use this transform, you need a dataset and model for language modeling.
+
+1. Create a file named `INPUT` that contains labels and pixels to feed into the model. Each row should have comma-separated elements. The first element is the label. All other elements are pixel values. The content of the file should be similar to the following example:
 ```
 1,0,0,0...
 0,0,0,0...
 1,0,0,0...
 4,0,0,0...
 ...
 ```
-- **Required**: A path to a file called `OUTPUT`, to which the pipeline will write the predictions.
-- **Required**: A path to a file called `MODEL_PATH` that contains the pickled file of a scikit-learn model trained on MNIST data. Please refer to this scikit-learn [documentation](https://scikit-learn.org/stable/model_persistence.html) on how to serialize models.
+2. Create a file named `OUTPUT`. This file is used by the pipeline to write the predictions.

Review Comment:
   Doesn't pipeline create output files?



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