Merge HuggingFace individual and organization repos

README.md

@@ -1,81 +1,82 @@
 ---
 license: apache-2.0
 ---
-Cookiecutter-MLOps
-==============================
-
-A cookiecutter template employing MLOps best practices, so you can focus on building machine learning products while
-having MLOps best practices applied.
-
-Instructions
-------------
-1. Clone the repo.
-2. Run `make dirs` to create the missing parts of the directory structure described below.
-3. *Optional:* Run `make virtualenv` to create a python virtual environment. Skip if using conda or some other env manager.
-    1. Run `source env/bin/activate` to activate the virtualenv.
-4. Run `make requirements` to install required python packages.
-5. Put the raw data in `data/raw`.
-6. To save the raw data to the DVC cache, run `dvc add data/raw`
-7. Edit the code files to your heart's desire.
-8. Process your data, train and evaluate your model using `dvc repro` or `make reproduce`
-9. To run the pre-commit hooks, run `make pre-commit-install`
-10. For setting up data validation tests, run `make setup-setup-data-validation`
-11. For **running** the data validation tests, run `make run-data-validation`
-12. When you're happy with the result, commit files (including .dvc files) to git.
-
-Project Organization
-------------
-
-    ├── LICENSE
-    ├── Makefile           <- Makefile with commands like `make dirs` or `make clean`
-    ├── README.md          <- The top-level README for developers using this project.
-    ├── data
-    │   ├── processed      <- The final, canonical data sets for modeling.
-    │   └── raw            <- The original, immutable data dump
-    │
-    ├── models             <- Trained and serialized models, model predictions, or model summaries
-    │
-    ├── notebooks          <- Jupyter notebooks. Naming convention is a number (for ordering),
-    │                         the creator's initials, and a short `-` delimited description, e.g.
-    │                         `1.0-jqp-initial-data-exploration`.
-    ├── references         <- Data dictionaries, manuals, and all other explanatory materials.
-    ├── reports            <- Generated analysis as HTML, PDF, LaTeX, etc.
-    │   └── figures        <- Generated graphics and figures to be used in reporting
-    │   └── metrics.txt    <- Relevant metrics after evaluating the model.
-    │   └── training_metrics.txt    <- Relevant metrics from training the model.
-    │
-    ├── requirements.txt   <- The requirements file for reproducing the analysis environment, e.g.
-    │                         generated with `pip freeze > requirements.txt`
-    │
-    ├── setup.py           <- makes project pip installable (pip install -e .) so src can be imported
-    ├── src                <- Source code for use in this project.
-    │   ├── __init__.py    <- Makes src a Python module
-    │   │
-    │   ├── data           <- Scripts to download or generate data
-    │   │   ├── great_expectations  <- Folder containing data integrity check files
-    │   │   ├── make_dataset.py
-    │   │   └── data_validation.py  <- Script to run data integrity checks
-    │   │
-    │   ├── models         <- Scripts to train models and then use trained models to make
-    │   │   │                 predictions
-    │   │   ├── predict_model.py
-    │   │   └── train_model.py
-    │   │
-    │   └── visualization  <- Scripts to create exploratory and results oriented visualizations
-    │       └── visualize.py
-    │
-    ├── .pre-commit-config.yaml  <- pre-commit hooks file with selected hooks for the projects.
-    ├── dvc.lock           <- constructs the ML pipeline with defined stages.
-    └── dvc.yaml           <- Traing a model on the processed data.
-
-
---------
-
-<p><small>Project based on the <a target="_blank" href="https://drivendata.github.io/cookiecutter-data-science/">cookiecutter data science project template</a>. #cookiecutterdatascience</small></p>
-
-
----
-
-To create a project like this, just go to https://dagshub.com/repo/create and select the **Cookiecutter DVC** project template.
-
-Made with 🐶 by [DAGsHub](https://dagshub.com/).
+
+Cookiecutter-MLOps
+==============================
+
+A cookiecutter template employing MLOps best practices, so you can focus on building machine learning products while
+having MLOps best practices applied.
+
+Instructions
+------------
+1. Clone the repo.
+2. Run `make dirs` to create the missing parts of the directory structure described below.
+3. *Optional:* Run `make virtualenv` to create a python virtual environment. Skip if using conda or some other env manager.
+    1. Run `source env/bin/activate` to activate the virtualenv.
+4. Run `make requirements` to install required python packages.
+5. Put the raw data in `data/raw`.
+6. To save the raw data to the DVC cache, run `dvc add data/raw`
+7. Edit the code files to your heart's desire.
+8. Process your data, train and evaluate your model using `dvc repro` or `make reproduce`
+9. To run the pre-commit hooks, run `make pre-commit-install`
+10. For setting up data validation tests, run `make setup-setup-data-validation`
+11. For **running** the data validation tests, run `make run-data-validation`
+12. When you're happy with the result, commit files (including .dvc files) to git.
+
+Project Organization
+------------
+
+    ├── LICENSE
+    ├── Makefile           <- Makefile with commands like `make dirs` or `make clean`
+    ├── README.md          <- The top-level README for developers using this project.
+    ├── data
+    │   ├── processed      <- The final, canonical data sets for modeling.
+    │   └── raw            <- The original, immutable data dump
+    │
+    ├── models             <- Trained and serialized models, model predictions, or model summaries
+    │
+    ├── notebooks          <- Jupyter notebooks. Naming convention is a number (for ordering),
+    │                         the creator's initials, and a short `-` delimited description, e.g.
+    │                         `1.0-jqp-initial-data-exploration`.
+    ├── references         <- Data dictionaries, manuals, and all other explanatory materials.
+    ├── reports            <- Generated analysis as HTML, PDF, LaTeX, etc.
+    │   └── figures        <- Generated graphics and figures to be used in reporting
+    │   └── metrics.txt    <- Relevant metrics after evaluating the model.
+    │   └── training_metrics.txt    <- Relevant metrics from training the model.
+    │
+    ├── requirements.txt   <- The requirements file for reproducing the analysis environment, e.g.
+    │                         generated with `pip freeze > requirements.txt`
+    │
+    ├── setup.py           <- makes project pip installable (pip install -e .) so src can be imported
+    ├── src                <- Source code for use in this project.
+    │   ├── __init__.py    <- Makes src a Python module
+    │   │
+    │   ├── data           <- Scripts to download or generate data
+    │   │   ├── great_expectations  <- Folder containing data integrity check files
+    │   │   ├── make_dataset.py
+    │   │   └── data_validation.py  <- Script to run data integrity checks
+    │   │
+    │   ├── models         <- Scripts to train models and then use trained models to make
+    │   │   │                 predictions
+    │   │   ├── predict_model.py
+    │   │   └── train_model.py
+    │   │
+    │   └── visualization  <- Scripts to create exploratory and results oriented visualizations
+    │       └── visualize.py
+    │
+    ├── .pre-commit-config.yaml  <- pre-commit hooks file with selected hooks for the projects.
+    ├── dvc.lock           <- constructs the ML pipeline with defined stages.
+    └── dvc.yaml           <- Traing a model on the processed data.
+
+
+--------
+
+<p><small>Project based on the <a target="_blank" href="https://drivendata.github.io/cookiecutter-data-science/">cookiecutter data science project template</a>. #cookiecutterdatascience</small></p>
+
+
+---
+
+To create a project like this, just go to https://dagshub.com/repo/create and select the **Cookiecutter DVC** project template.
+
+Made with 🐶 by [DAGsHub](https://dagshub.com/).