adjusted installation instructions

docs/before-OLD.txt

@@ -0,0 +1,85 @@
+---
+title: Before You Start Coding
+has_children: false
+nav_order: 4
+---
+
+# Setting Up Your Conda Environment
+
+Before you start, please set up a new conda environment as follows.
+
+If your machine does not have a GPU:
+
+```
+conda create --name book
+conda activate book
+
+conda install pip
+conda install pytorch torchvision torchaudio torchtext cpuonly -c pytorch
+conda install jupyter pandas matplotlib scikit-learn gensim nltk
+pip install conllu transformers datasets
+pip install accelerate -U
+```
+
+If your machine machine has an Nvidia GPU:
+```
+conda create --name book
+conda activate book
+
+conda install pip
+```
+
+Then install PyTorch with GPU support using the instructions on this page: [https://pytorch.org/get-started/previous-versions/](https://pytorch.org/get-started/previous-versions/). For example, in Summer 2023, we used:
+```
+conda install pytorch torchvision torchaudio cudatoolkit=11.6 -c pytorch -c conda-forge
+```
+In Summer 2024, we used:
+```
+conda install pytorch==2.3.0 torchvision==0.18.0 torchaudio==2.3.0 pytorch-cuda=11.8 -c pytorch -c nvidia
+```
+
+Lastly, install a few more needed packages:
+```
+conda install jupyter pandas matplotlib scikit-learn gensim nltk
+pip install conllu transformers datasets
+pip install accelerate -U
+```
+(See https://pytorch.org/get-started/locally/ for PyTorch installation instructions on other platforms.)
+
+Note that as these libraries evolve you may run into versions that are no longer compatible with this code. To control for this situation, we list below the exact environments that were used to test this code when we wrote the book. You can install any of these using the command `conda env create -f <ENVIRONMENT-NAME>`, e.g., `conda env create -f environment_gpu.yml` to install the environment for a Linux machine with GPU.
+
+Environments in which this code was tested:
+- [Linux machine with a GPU](https://github.com/clulab/gentlenlp/blob/main/notebooks/environment_gpu.yml)
+- [Linux machine without a GPU](https://github.com/clulab/gentlenlp/blob/main/notebooks/environment_cpu.yml)
+- [M1 Mac](https://github.com/clulab/gentlenlp/blob/main/notebooks/environment_mac.yml)
+
+# External Datasets Used 
+
+## Binary Classification
+
+For binary classification we used the [Large Movie Review Dataset](https://ai.stanford.edu/~amaas/data/sentiment/) created by Andrew Maas. Because this dataset allows redistribution, we copied it in this repository at this location: [https://github.com/clulab/gentlenlp/tree/main/notebooks/data/aclImdb](https://github.com/clulab/gentlenlp/tree/main/notebooks/data/aclImdb), which is the location expected by the notebooks that implement binary classifiers. Please see the [dataset's README](https://github.com/clulab/gentlenlp/blob/main/notebooks/data/aclImdb/README) for more details, including the appropriate citation if you use this dataset in research publications. 
+
+## Multiclass Classification
+
+For multiclass classification we used a version of the [AG News dataset](http://groups.di.unipi.it/~gulli/AG_corpus_of_news_articles.html). In particular, we used the simplified form of the dataset from the paper [Character-level Convolutional Networks for Text Classification](https://proceedings.neurips.cc/paper/2015/file/250cf8b51c773f3f8dc8b4be867a9a02-Paper.pdf), which keeps only the four most frequent labels. The license for this dataset does not allow redistribution, so please download the archive `ag_news_csv.tar.gz` yourself from [here](https://drive.google.com/drive/u/0/folders/0Bz8a_Dbh9Qhbfll6bVpmNUtUcFdjYmF2SEpmZUZUcVNiMUw1TWN6RDV3a0JHT3kxLVhVR2M?resourcekey=0-TLwzfR2O-D2aPitmn5o9VQ). Uncompress the downloaded file, and copy the `ag_news_csv` folder under `notebooks/data/` in your repository.
+
+## Pre-trained English Word Embeddings
+
+Please download the [these GloVe embeddings](https://nlp.stanford.edu/data/glove.6B.zip) from the [Stanford GloVe website](https://nlp.stanford.edu/projects/glove/). Once the `glove.6B.zip` file is downloaded, uncompress it and place the extracted `glove.6B.300d.txt` file in the `notebooks/` folder.
+
+## Pre-trained Spanish Word Embeddings
+
+Please download the [these Spanish GloVe embeddings](http://dcc.uchile.cl/~jperez/word-embeddings/glove-sbwc.i25.vec.gz) from the [Spanish Word Embeddings GitHub repository](https://github.com/dccuchile/spanish-word-embeddings). Once the `glove-sbwc.i25.vec.gz` file is downloaded, uncompress it and place the extracted `glove-sbwc.i25.vec` file in the `notebooks/` folder.
+
+## Part-of-speech Tagging
+
+For part-of-speech tagging we used the Spanish AnCora dataset that is included in the 
+[Universal Dependencies version 2.8 dataset](https://lindat.mff.cuni.cz/repository/xmlui/bitstream/handle/11234/1-3720/deep-ud-2.8-data.tgz?sequence=1&isAllowed=y). Its license does not allow redistribution, so please download the UD version 2.8 dataset from [here](https://lindat.mff.cuni.cz/repository/xmlui/bitstream/handle/11234/1-3720/deep-ud-2.8-data.tgz?sequence=1&isAllowed=y). Uncompress the downloaded `deep-ud-2.8-data.tgz` file, and copy the `UD_Spanish-AnCora` folder under `notebooks/data/` in your repository.
+
+
+
+
+
+
+
+

docs/before.md

@@ -6,49 +6,39 @@ nav_order: 4
 
 # Setting Up Your Conda Environment
 
-Before you start, please set up a new conda environment as follows.
 
-If your machine does not have a GPU:
+Install miniconda by following the instructions under "Quick command line install" for your operating system here: https://docs.anaconda.com/miniconda/. 
 
+Create and activate the conda environment for the book:
 ```
 conda create --name book
 conda activate book
-
-conda install pip
-conda install pytorch torchvision torchaudio torchtext cpuonly -c pytorch
-conda install jupyter pandas matplotlib scikit-learn gensim nltk
-pip install conllu transformers datasets
-pip install accelerate -U
 ```
 
-If your machine machine has an Nvidia GPU:
-```
-conda create --name book
-conda activate book
+Install PyTorch by selecting the appropriate parameters on this page: https://pytorch.org. Note that if your computer has a Nvidia GPU, you should select one of the CUDA version for Compute Platform (ideally, the most recent one). 
 
-conda install pip
-```
+For example, for Mac OS, the configuration should look like this:
+
+[[images/pytorch-macos.png]]
+
+Then, run the indicated command. For example, for Macs the command generated is:
 
-Then install PyTorch with GPU support using the instructions on this page: [https://pytorch.org/get-started/previous-versions/](https://pytorch.org/get-started/previous-versions/). For example, in Summer 2023, we used:
-```
-conda install pytorch torchvision torchaudio cudatoolkit=11.6 -c pytorch -c conda-forge
-```
-In Summer 2024, we used:
 ```
-conda install pytorch==2.3.0 torchvision==0.18.0 torchaudio==2.3.0 pytorch-cuda=11.8 -c pytorch -c nvidia
+conda install pytorch::pytorch torchvision torchaudio -c pytorch
 ```
 
 Lastly, install a few more needed packages:
 ```
+conda install pip
 conda install jupyter pandas matplotlib scikit-learn gensim nltk
 pip install conllu transformers datasets
 pip install accelerate -U
 ```
-(See https://pytorch.org/get-started/locally/ for PyTorch installation instructions on other platforms.)
+
+# Environments in which this Code was Tested
 
 Note that as these libraries evolve you may run into versions that are no longer compatible with this code. To control for this situation, we list below the exact environments that were used to test this code when we wrote the book. You can install any of these using the command `conda env create -f <ENVIRONMENT-NAME>`, e.g., `conda env create -f environment_gpu.yml` to install the environment for a Linux machine with GPU.
 
-Environments in which this code was tested:
 - [Linux machine with a GPU](https://github.com/clulab/gentlenlp/blob/main/notebooks/environment_gpu.yml)
 - [Linux machine without a GPU](https://github.com/clulab/gentlenlp/blob/main/notebooks/environment_cpu.yml)
 - [M1 Mac](https://github.com/clulab/gentlenlp/blob/main/notebooks/environment_mac.yml)