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Merge pull request #53 from kmascar/master
Updated Textract Custom Queries code samples
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python/custom-queries/custom-queries-checks-blog.ipynb

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"id": "34faecaf-0568-428d-b18f-fa0ab51f3298",
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"metadata": {},
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"source": [
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"### Step 1.8: Evaluate the adapter (console)"
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"### Step 1.8: Evaluate the adapter (console)\n",
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"Once the training completes, click the Evaluate Adapter button on the Adapter Details page to review the adapter performance metrics. \n",
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"You can also test samples in the console by clicking on the Try Adapter button and uploading a sample document.\n",
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"<img src=\"./screenshots/checks-notebook-step8.png\"/>"
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"import boto3\n",
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"from textractcaller.t_call import call_textract, Textract_Features, Query, QueriesConfig, Adapter, AdaptersConfig\n",
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"import trp.trp2 as t2\n",
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"import pandas as pd\n",
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"\n",
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"textract_client = boto3.client('textract')\n",
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"\n",
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"id": "6eb56c5f-c3ea-4ce7-b920-29d3c49b7e3c",
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"metadata": {},
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"### Step2.4: List all the adapter versions in your AWS Account\n",
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"### Step 2.4: List all the adapter versions in your AWS Account\n",
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"You will see a new Adapter ID and Version in the list with the Status as \"CREATION_IN_PROGRESS\". \n",
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"Training can take 1 to 30 hours to complete, however, given our dataset is small, it should complete in an hour or so."
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"tags": []
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},
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"### Step2.5: View details of the adapter you just created using GetAdapterVersion\n",
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"### Step 2.5: View details of the adapter you just created using GetAdapterVersion\n",
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"1. This provides you with all the details for the adapter - from the dataset and output config to the evaluation metrics. \n",
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"2. As the adapter creation and training is still in progress, you will not see **\"EvaluationMetrics\"** yet. Come back once the training is complete"
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