Remove old Rasa language model (#325)

* Only test using the small models

* lightweight

* removed-old-rasa-links

* removed rasa tests

* update-tests

Author: koaning

.github/workflows/croncheck.yml

@@ -24,7 +24,6 @@ jobs:
         pip install -e ".[dev]"
         pip install flake8 pytest black
         python -m spacy download en_core_web_sm
-        python -m spacy download en_core_web_md
     - name: Style Check
       run: |
         # stop the build if there are Python syntax errors or undefined names

.github/workflows/macos-test.yml

@@ -27,7 +27,6 @@ jobs:
         python -m pip install --upgrade pip setuptools wheel
         pip install -e ".[dev]"
         python -m spacy download en_core_web_sm
-        python -m spacy download en_core_web_md
     - name: Test with pytest
       run: |
         python tests/scripts/prepare_fasttext_tests.py

.github/workflows/pythonpackage.yml

@@ -27,7 +27,6 @@ jobs:
         python -m pip install --upgrade pip setuptools wheel
         pip install -e ".[dev]"
         python -m spacy download en_core_web_sm
-        python -m spacy download en_core_web_md
     - name: Test with pytest
       run: |
         python tests/scripts/prepare_fasttext_tests.py

.github/workflows/rasatest.yml

@@ -12,8 +12,6 @@ develop:
 	python tests/scripts/prepare_fasttext_tests.py
 	python tests/scripts/prepare_gensim_kv.py
 	python tests/scripts/prepare_floret.py
-	mkdir tests/rasa-test-demo
-	rasa init --init-dir tests/rasa-test-demo --no-prompt
 
 flake:
 	flake8 setup.py --count --statistics --max-complexity=10 --max-line-length=127

Makefile

@@ -6,7 +6,7 @@ One of the goals of this package is to make it simple to explore embeddings.
 This includes embeddings that are Non-English. In this guide we'll demonstrate how you might be
 able to use this library to run simple Arabic classification benchmark
 using scikit-learn and this library. This benchmark was part of discussion
-on [github](https://github.com/RasaHQ/whatlies/issues/262).
+on [github](https://github.com/koaning/whatlies/issues/262).
 
 If you want to follow along, make sure that this tool is installed.
 

docs/examples/arabic/index.md

@@ -186,7 +186,7 @@ emb_debias = e1.merge(e2) | (lang['man'] - lang['woman'])
 ```
 
 Next, we'll use the fasttext language backend as a scikit-learn featurizer.
-You can read more on this feature [here](https://rasahq.github.io/whatlies/tutorial/scikit-learn/).
+You can read more on this feature [here](https://koaning.github.io/whatlies/tutorial/scikit-learn/).
 
 ```python
 from sklearn.svm import SVC

docs/examples/lipstick-pig/index.md

@@ -34,7 +34,7 @@ in using an [altair](https://altair-viz.github.io/) visualisation in a publicati
 details are listed on their [documentation page](https://altair-viz.github.io/user_guide/saving_charts.html?highlight=save%20svg#png-svg-and-pdf-format)
 in short you'll need to install the `altair_saver` package for this functionality.
 
-To get this code to work you [may](https://github.com/RasaHQ/whatlies/issues/58) need to install some node
+To get this code to work you [may](https://github.com/koaning/whatlies/issues/58) need to install some node
 dependencies though. To install them locally in your project run;
 
 ```

docs/faq.md

@@ -135,7 +135,7 @@ Please use the following citation when you found `whatlies` helpful for any of y
     url = "https://www.aclweb.org/anthology/2020.nlposs-1.8",
     doi = "10.18653/v1/2020.nlposs-1.8",
     pages = "52--60",
-    abstract = "We introduce whatlies, an open source toolkit for visually inspecting word and sentence embeddings. The project offers a unified and extensible API with current support for a range of popular embedding backends including spaCy, tfhub, huggingface transformers, gensim, fastText and BytePair embeddings. The package combines a domain specific language for vector arithmetic with visualisation tools that make exploring word embeddings more intuitive and concise. It offers support for many popular dimensionality reduction techniques as well as many interactive visualisations that can either be statically exported or shared via Jupyter notebooks. The project documentation is available from https://rasahq.github.io/whatlies/.",
+    abstract = "We introduce whatlies, an open source toolkit for visually inspecting word and sentence embeddings. The project offers a unified and extensible API with current support for a range of popular embedding backends including spaCy, tfhub, huggingface transformers, gensim, fastText and BytePair embeddings. The package combines a domain specific language for vector arithmetic with visualisation tools that make exploring word embeddings more intuitive and concise. It offers support for many popular dimensionality reduction techniques as well as many interactive visualisations that can either be statically exported or shared via Jupyter notebooks. The project documentation is available from https://koaning.github.io/whatlies/.",
 }
 ```
 

docs/index.md

@@ -83,4 +83,4 @@ a `Pipeline`. You should assume that you cannot use `GridSearchCV` and that you
 to disk.
 
 If you see a way to properly support this in general, let us know on github by
-creating an [issue](https://github.com/RasaHQ/whatlies/issues).
+creating an [issue](https://github.com/koaning/whatlies/issues).