Docs fixes.

docs/source/blog/release-050.md

@@ -127,7 +127,7 @@ Previously changing a config after importing or labeling tasks could be dangerou
 
 ### Exporting with automatic converters
 
-When finishing your project - go to the [export page](http://localhost:8080/export) and choose in between the [common export formats](/guide/completions.html#Export-formats) valid for your current project configuration.
+When finishing your project - go to the [export page](http://localhost:8080/export) and choose in between the [common export formats](/guide/export.html#Export-formats) valid for your current project configuration.
 
 ### Connection to running Machine Learning backend
 

docs/source/guide/export.md

@@ -14,7 +14,7 @@ You can optionally convert and export raw completions to a more common format by
 
 ## Basic format
 
-The output data is stored in _completions_ - JSON formatted files, one per each completed task saved in project directory in `completions` folder or in the [`"output_dir"` option](config.html#output_dir) The example structure of _completion_ is the following:
+The output data is stored in _completions_ - JSON formatted files, one per each completed task saved in project directory in `completions` folder or in the [`"output_dir"` option](setup.html#Structure) The example structure of _completion_ is the following:
 
 ```json
 {
@@ -136,15 +136,15 @@ Tag specific value that includes the labeling result details. The exact structur
 
 ### data
 
-Data copied from [input task](config.html#JSON-file)
+Data copied from [input task](tasks.html#Basic-format)
 
 ### id
 
 Task identifier
 
 ### predictions
 
-Machine learning predictions (aka _pre-labeling results_). Follows the [same format](completions.html#completions) as completion, with some additional fields related to machine learning inference:
+Machine learning predictions (aka _pre-labeling results_). Follows the [same format](export.html#completions) as completion, with some additional fields related to machine learning inference:
 
 - **score** - the overall result score (probabilistic output, confidence level, etc.)
 

docs/source/guide/index.md

@@ -13,8 +13,8 @@ Here are the main concepts behind Label Studio's workflow:
 <div style="margin:auto; text-align:center; width:100%"><img src="/images/label-studio-ov.jpg" style="opacity: 0.7"/></div>
 
 - **Tasks** represent an individual dataset items. Label Studio is a multi-type labeling tool - you can [import](tasks.html) either text, image, audio URL, HTML text or any number and combination of these data resources.
-- **Completions** are the labeling results in [JSON format](completions.html#Completion-fields). They could be [exported](completions.html) in various common formats, ready to use in machine learning pipelines.
-- **Predictions** are the optional labeling results in [the same format](completions.html#Completion-fields), but unlike completions they are used for generating pre-labeling during the annotation process, or validating the model predictions.
+- **Completions** are the labeling results in [JSON format](export.html#Completion-fields). They could be [exported](completions.html) in various common formats, ready to use in machine learning pipelines.
+- **Predictions** are the optional labeling results in [the same format](export.html#Completion-fields), but unlike completions they are used for generating pre-labeling during the annotation process, or validating the model predictions.
 - [**Machine learning backend** connects](ml.html) popular machine learning frameworks to Label Studio for active learning & generating model predictions on-the-fly.
 - **Labeling config** is a simple [XML tree with **tags**](setup.html#Labeling-config) used to configure UI elements, connect input data to output labeling scheme.
 - **Project** encompasses tasks, config, predictions and completions all-in-one in an isolated directory

docs/source/guide/tasks.md

@@ -17,8 +17,8 @@ Label Studio expects the JSON-formatted list of _tasks_ as input. Each _task_ is
     - `<AudioPlus value="$key">`: `value` is taken as a valid URL to an audio file with CORS policy enabled on the server side
     - `<Image value="$key">`: `value` is a valid URL to an image file
 * (optional) **id** - integer task ID
-* (optional) **completions** - list of output annotation results, where each result is saved using [Label Studio's completion format](/guide/completions.html#completions). You can import annotation results in order to use them in consequent labeling task.
-* (optional) **predictions** - list of model prediction results, where each result is saved using [Label Studio's prediction format](/guide/completions.html#predictions). Importing predictions is useful for automatic task prelabeling & active learning & exploration.
+* (optional) **completions** - list of output annotation results, where each result is saved using [Label Studio's completion format](/guide/export.html#completions). You can import annotation results in order to use them in consequent labeling task.
+* (optional) **predictions** - list of model prediction results, where each result is saved using [Label Studio's prediction format](/guide/export.html#predictions). Importing predictions is useful for automatic task prelabeling & active learning & exploration.
 
 > Note: in case `"data"` field is missing in imported task object, the whole task body is interpreted as `task["data"]`, i.e. `[{"my_key": "my_value"}]` will be internally converted to `[{"data": {"my_key": "my_value"}}]`
 
@@ -92,7 +92,7 @@ There are a few possible ways to import data files to your labeling project:
 
  - Initialize Label Studio project and directly specify the paths, e.g. `label-studio init --input-path my_tasks.json --input-format json`
 
-The `--input-path` argument points to a file or a directory where your labeling tasks reside. By default it expects [JSON-formatted tasks](config.html#JSON-file), but you can also specify all other formats listed bellow by using `--input-format` option.
+The `--input-path` argument points to a file or a directory where your labeling tasks reside. By default it expects [JSON-formatted tasks](tasks.html#JSON), but you can also specify all other formats listed bellow by using `--input-format` option.
 
 ### JSON
 

docs/source/tutorials/dummy_model.md

@@ -10,8 +10,8 @@ If you create ML backend by using Label Studio's ML SDK, you have to follow the
 
 - created model class should be inherited from `label_studio.ml.LabelStudioMLBase`
 - 2 methods should be overrided:
-    - `predict()` takes [input tasks](/guide/tasks.html#Basic-format) and outputs [predictions](/guide/completions.html#predictions) in a Label Studio format
-    - `train()` receives [completions](/guide/completions.html#Basic-format) iterable and returns dictionary with created links and resources. This dictionary will be later used for model loading via `self.train_output` field.
+    - `predict()` takes [input tasks](/guide/tasks.html#Basic-format) and outputs [predictions](/guide/export.html#predictions) in a Label Studio format
+    - `train()` receives [completions](/guide/export.html#Basic-format) iterable and returns dictionary with created links and resources. This dictionary will be later used for model loading via `self.train_output` field.
 
 Create a file `model.py` with the following content:
 

docs/source/tutorials/pytorch-image-transfer-learning.md

@@ -22,8 +22,8 @@ If you create ML backend by using Label Studio's ML SDK, you have to follow the
 
 - created model class should be inherited from `label_studio.ml.LabelStudioMLBase`
 - 2 methods should be overrided:
-    - `predict()` takes [input tasks](/guide/tasks.html#Basic-format) and outputs [predictions](/guide/completions.html#predictions) in a Label Studio format
-    - `train()` receives [completions](/guide/completions.html#Basic-format) iterable and returns dictionary with created links and resources. This dictionary will be later used for model loading via `self.train_output` field.
+    - `predict()` takes [input tasks](/guide/tasks.html#Basic-format) and outputs [predictions](/guide/export.html#predictions) in a Label Studio format
+    - `train()` receives [completions](/guide/export.html#Basic-format) iterable and returns dictionary with created links and resources. This dictionary will be later used for model loading via `self.train_output` field.
 
 Create a file `model.py` with the PyTorch model for ready for training and inference.
 

docs/source/tutorials/sklearn-text-classifier.md

@@ -23,8 +23,8 @@ If you create ML backend by using Label Studio's ML SDK, you have to follow the
 
 - created model class should be inherited from `label_studio.ml.LabelStudioMLBase`
 - 2 methods should be overrided:
-    - `predict()` takes [input tasks](/guide/tasks.html#Basic-format) and outputs [predictions](/guide/completions.html#predictions) in a Label Studio format
-    - `train()` receives [completions](/guide/completions.html#Basic-format) iterable and returns dictionary with created links and resources. This dictionary will be later used for model loading via `self.train_output` field.
+    - `predict()` takes [input tasks](/guide/tasks.html#Basic-format) and outputs [predictions](/guide/export.html#predictions) in a Label Studio format
+    - `train()` receives [completions](/guide/export.html#Basic-format) iterable and returns dictionary with created links and resources. This dictionary will be later used for model loading via `self.train_output` field.
 
 Create a file `model.py` with the following content:
 

label_studio/static/js/lsb.js

@@ -140,7 +140,7 @@ const loadNext = function(ls) {
 };
 
 const loadTask = function(ls, taskID) {
-  var url = `${API_URL.MAIN}${API_URL.PROJECTS}/1${API_URL.TASKS}/${taskID}`;
+  var url = `${API_URL.MAIN}${API_URL.TASKS}/${taskID}`;
     return _loadTask(ls, url);
 };