From 4df09bc161b603ce0478816b2a063870e6c71118 Mon Sep 17 00:00:00 2001 From: Hironsan Date: Mon, 6 Dec 2021 12:51:30 +0900 Subject: [PATCH] Add document for auto labeling --- docs/advanced/auto-labelling-config.md | 85 ++++++++++++++++++ .../auto-labeling/add_label_mapping.png | Bin 0 -> 18367 bytes docs/images/auto-labeling/enable.png | Bin 0 -> 192028 bytes docs/images/auto-labeling/select_template.png | Bin 0 -> 37609 bytes docs/images/auto-labeling/set_parameters.png | Bin 0 -> 28517 bytes .../auto-labeling/test_label_mapping.png | Bin 0 -> 23267 bytes .../auto-labeling/test_mapping_template.png | Bin 0 -> 21153 bytes docs/images/auto-labeling/test_parameters.png | Bin 0 -> 89476 bytes 8 files changed, 85 insertions(+) create mode 100644 docs/advanced/auto-labelling-config.md create mode 100644 docs/images/auto-labeling/add_label_mapping.png create mode 100644 docs/images/auto-labeling/enable.png create mode 100644 docs/images/auto-labeling/select_template.png create mode 100644 docs/images/auto-labeling/set_parameters.png create mode 100644 docs/images/auto-labeling/test_label_mapping.png create mode 100644 docs/images/auto-labeling/test_mapping_template.png create mode 100644 docs/images/auto-labeling/test_parameters.png diff --git a/docs/advanced/auto-labelling-config.md b/docs/advanced/auto-labelling-config.md new file mode 100644 index 00000000..ffe8eeca --- /dev/null +++ b/docs/advanced/auto-labelling-config.md @@ -0,0 +1,85 @@ +# Setup Auto Labeling + +In this tutorial, you will learn how to set up and use the auto-labeling feature. Auto-labeling is a feature that automates labeling using the Web API. This is not required to use doccano, but if you set it up, you will be able to label data more efficiently. + +The tutorial is divided into several sections: + +- Select a Template will give you a starting point to follow the tutorial. +- Set Request Parameters will teach you how to set parameters required to send a request. +- Specify Response Mapping will teach you the way to extract the label information from the response. +- Specify Label Mapping will give you the way to map the extracted labels to the internal labels of doccano. +- Enable the Feature will show you how to enable the auto-labeling. + +In this tutorial, we will show you how to set up auto-labeling using Amazon Comprehend Sentiment Analysis as an example. Therefore, we assume that you have a text classification project in doccano, an AWS account and be able to generate access keys. + +## Select a Template + +First, move to the "settings" page and open "Auto Labeling" tab. The new tab should display a "Create" button and an empty table. Click the button and select "Amazon Comprehend Sentiment Analysis" from the dropdown menu: + +![](../images/auto-labeling/select_template.png) + +## Set Request Parameters + +Next, you need to set parameters to send an API request. In the case of Amazon Comprehend Sentiment Analysis, the following parameters are required: + +- aws_access_key +- aws_secret_access_key +- region_name +- language_code + +In the following example, we set `us-west-2` as a `region_name` and `en` as a `language_code`: + +![](../images/auto-labeling/set_parameters.png) + +Then, we will test them using the sample text to make sure whether the parameters are set correctly or not. In this case, we set "I like you" as a sample text and be able to get the response from Amazon Comprehend Sentiment Analysis. If you look at the Sentiment field in the response, you will see that its value is POSITIVE: + +![](../images/auto-labeling/test_parameters.png) + +## Specify Response Mapping + +Now, you can successfully fetch the API response. Next, you need to convert it to doccano format(below) with the mapping template([Jinja2](https://jinja.palletsprojects.com/en/2.11.x/) format). + +```plain +Text Classification +[{ "label": "Cat" }, ...] + +Sequence Labeling +[{ "label": "Cat", "start_offset": 0, "end_offset": 5 }, ...] + +Sequence to sequence +[{ "text": "Cat" }, ...] +``` + +In the case of Amazon Comprehend Sentiment Analysis, we want to get `Sentiment` value from the response. As we can access the entire response by the `input` variable, the mapping template looks like the following: + +```json +[ + { + "label": "{{ input.Sentiment }}" + } +] +``` + +After setting the template, we will test them using the sample response. This response is the same one we fetched in the `Set Request Parameters` section. + +![](../images/auto-labeling/test_mapping_template.png) + +## Specify Label Mapping + +Once you specify the mapping template, you need to convert the label in the response into the one you defined at the label page. + +Click the `Add` button and fill in the `From` and `To` fields. `From` means the response label string. In this case, we can specify `POSITIVE`. `To` means the label of this project. In this case, we specify `positive`: + +![](../images/auto-labeling/add_label_mapping.png) + +After adding the label mapping, we will test them using the sample response: + +![](../images/auto-labeling/test_label_mapping.png) + +## Enable the Feature + +Finally, move to the "annotation" page and click "Auto Labeling" button. It should display a "Slide" button for switching enable/disable auto-labeling feature. 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