fix typo in gpl tutorial (deepset-ai#28)

* fix typo in gpl tutorial * fix typo in md * fix file path Co-authored-by: Massimiliano Pippi <[email protected]>
blancadesal · Sep 26, 2022 · 47acabd · 47acabd
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diff --git a/.github/workflows/markdowns.yml b/.github/workflows/markdowns.yml
@@ -19,10 +19,10 @@ jobs:
  with:
  python-version: '3.9'
  cache: 'pip' # caching pip dependencies
- 
+
  - name: Install Dependencies and Generate Markdown
  run: |
- pip install -r scripts/requirements.txt
+ pip install -r requirements.txt
  python scripts/generate_markdowns.py
 
  - name: Status

diff --git a/markdowns/18.md b/markdowns/18.md
@@ -11,7 +11,7 @@ id: "tutorial18md"
 
 [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/18_GPL.ipynb)
 
-*Note: Adapted to Haystack from Nils Riemers' original [notebook](https://colab.research.google.com/gist/jamescalam/d2c888775c87f9882bb7c379a96adbc8/gpl-domain-adaptation.ipynb#scrollTo=183ff7ab)
+*Note: Adapted to Haystack from Nils Reimers' original [notebook](https://colab.research.google.com/gist/jamescalam/d2c888775c87f9882bb7c379a96adbc8/gpl-domain-adaptation.ipynb#scrollTo=183ff7ab)
 
 The NLP models we use every day were trained on a corpus of data that reflects the world from the past. In the meantime, we've experienced world-changing events, like the COVID pandemics, and we'd like our models to know about them. Training a model from scratch is tedious work but what if we could just update the models with new data? Generative Pseudo Labeling comes to the rescue.
 

diff --git a/tutorials/18_GPL.ipynb b/tutorials/18_GPL.ipynb
@@ -13,7 +13,7 @@
  "\n",
  "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/18_GPL.ipynb)\n",
  "\n",
- "*Note: Adapted to Haystack from Nils Riemers' original [notebook](https://colab.research.google.com/gist/jamescalam/d2c888775c87f9882bb7c379a96adbc8/gpl-domain-adaptation.ipynb#scrollTo=183ff7ab)\n",
+ "*Note: Adapted to Haystack from Nils Reimers' original [notebook](https://colab.research.google.com/gist/jamescalam/d2c888775c87f9882bb7c379a96adbc8/gpl-domain-adaptation.ipynb#scrollTo=183ff7ab)\n",
  "\n",
  "The NLP models we use every day were trained on a corpus of data that reflects the world from the past. In the meantime, we've experienced world-changing events, like the COVID pandemics, and we'd like our models to know about them. Training a model from scratch is tedious work but what if we could just update the models with new data? Generative Pseudo Labeling comes to the rescue.\n",
  "\n",