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158	158	**The second step is the hierarchical annotation** [[custom recipe>>https://gitlab.com/aisop/aisop-nlp/-/tree/main/hierarchical_annotation?ref_type=heads]] (link to become public soon): The same fragments are now annotated with the top-level annotation and all their children. E.g. using the command ##python -m prodigy subcat_annotate_with_top2 the-course-name-l2 \
159	159	the-course-name-dbout.jsonl labels-all-depths.txt  -F ./subcat_annotate_with_top2.py##.
160	160
161		-The resulting data-set can be extracted out of prodigy using the db-out recipe, e.g. ##prodigy db-out the-course-name-l2 the-course-name-l2-dbout##.
	161	+The resulting data-set can be extracted out of prodigy using the db-out recipe, e.g. prodigy db-out the-course-name-l2 the-course-name-l2-dbout or can be converted to a spaCy dataset for training e.g. using the command xxxxx (see [[here>>https://gitlab.com/aisop/aisop-nlp/-/tree/main/it3/fundamental-principles]])
162	162
163	163
164	164	----
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165	165
166	166	== 2) Deployment ==
167	167
168		-Most of the steps below contribute to creating an _[[AISOP-domain>>the-AISOP-domains.WebHome]]_, a directory with all of the subject-specific information. This directory contains runtime information; it can be first composed as a source directory with all the annotated and source documents. The AISOP-domain should be possible to share.
	168	+=== 2.1 Train a Recognition Model ===
169	169
170		-=== 2.1 Train a Series of Recognition Models ===
	170	+See [[here>>https://gitlab.com/aisop/aisop-nlp/-/tree/main/it3/fundamental-principles]].
171	171
172		-The annotation process above will collect all annotations in a single JSONL file. It needs to be split for the different trainings.
173		-
174		-For the level 1, one creates a recognition model by training on the annotated dataset:
175		-
176		-For differentiating among topics of the level 1:
177		-
178		-##prodigy train the-course-name-l1 ~-~-base-model de_core_news_sm ~-~-lang de ~-~-textcat-multilabel the-course-name --label-stats##
179		-
180		-This outputs a directory called ##the-course-name## with the latest and the best models. Choose one as the chosen model to be copied inside `l1-model` of the domain.
181		-
182		-Then, for each of the L1 topic, you need to separate the annotations (??? Pierre ???) to individual files which you then involve in a training. E.g. here for Error-Correction:
183		-
184		-##prodigy train the-course-name-l2-Error-Correction ~-~-base-model de_core_news_sm ~-~-lang de ~-~-textcat-multilabel the-course-name-Error-Correction --label-stats##
185		-
186		-Inspecting the output statistics is an effective way to prevent low-quality results for some of the contents.
187		-
188		-Copy each of the produced best models into the domain's `l2-models` directory.
189		-
190		-The two models directories should be put inside `domains` at the root of the AISOP-webapp.
191		-
192	192	=== 2.2 Create a Pipeline ===
193	193
194	194	... write down the configuration JSON of the pipeline, get inspired [[pipeline-medieninderlehre.json>>https://gitlab.com/aisop/aisop-webapp/-/blob/main/config/couchdb/pipeline-medieninderlehre.json?ref_type=heads]]
195	195
196		-The pipeline is the central configuration information referencing the models and the scripts (found in the `scripts/python` directory). After changing the pipeline and copying it inside the domain, the web-application should be restarted.
	176	+=== 2.3 Create a Seminar and Import Content ===
197	197
198		-=== 2.3 Test ===
	178	+...
199	199
200		-A simple tool to analyze pasted sentences is available using the following schema:
201		-##https://app-url/debug-classifier/model-l1/model-l2/?text=theText##. This allows to verify that the analysis of expected classical sentences performs correctly. The formulation a URLs containing all information allows creators of domains to collect a series of tests that they can perform again after each adjustments (e.g. after a change of hyperparameter of the training or a change of the annotations).
	180	+Create a seminar with the web-interface, associate the appropriate pipeline.
202	202
203		-=== 2.4 Create a Seminar and Import Content ===
204		-
205		-Now that the pipeline is effective, we can create, within a group, a new __seminar__ which defines the pipeline and the associated views from within all views accessible to the group administrators.
206		-
207		-The button analyze should the be visible to you. Consider that analyzing all portfolios within a seminar can take time. We have experienced several hours for some cases.
208		-
209	209	=== 2.4 Interface with the composition platform ===
210	210
211		-The app installation is concluded with the configuration of the service for the Mahara platform: the instructions of the [[aisop-oauth>>https://gitlab.com/aisop/aisop-oauth]] apply here.
	184	+See the Mahara authorization configuration.
212	212
213	213	----
214	214
215	215	== 3) Usage ==
216	216
217		-Make sure that the users who are going to be revising others' e-portfolios are group-administrator within Mahara.
218		-
219	219	=== 3.1 Invite Users ===
220	220
221		-Within a learning management, announce to the expected users of the AISOP-webapp, the availability of the web-app URL. They will login by authorising the app to download from Mahara. The invitation should contain a little description of the expected function.
	192	+...
222	222
223		-The AISOP-webapp is likely able to support the students' writing process.
224		-
225	225	=== 3.2 Verify Imports and Analyses ===
226	226
227		-Once a sufficient amount of portfolios is available among the views of the seminars. It is possible launch global analyses. These make output synthetic graphs expressing the covergage of each topic. These graphs can be used in classroom to reflect on the course.
	196	+...
228	228
229	229	=== 3.3 Observe Usage and Reflect on Quality ===
230	230
231		-The AI-based observation can be used and, for each Mahara view, a portfolio-explorer and global-dashboard is available. They both can be the opportunity to observe the e-portfolios quality. They can also reveal classifications' flaws and, most probably, the appearance of topics that don't fit well any of the classification.
	200	+...
232	232
233		-
234		-
235	235	=== 3.4 Gather Enhancements ===
236	236
237		-As a first feedback after the use of the web-app. Multiple refinements of the e-portfolios could be suggested. Several such can be discussed in class.
238		-
239		-Further enhancements can be obtained from the web-app usage: if the proportion of allocated content matches the expectation from the course, if the students' understanding has been suffering for particular contents, or if some content parts are particularly popular.
240		-
241		-Finally, enhancements can be reflected upon for the subsequent issues of the course: Enhancements to the classification, to the contents, to the annotation sources or to the web-app and processes.
	204	+... on the web-app, on the creation process, and on the course