Mt 3

README.md

@@ -1,68 +1,70 @@
-# MT Exercise 3: Layer Normalization for Transformer Models
-
-This repo is a collection of scripts showing how to install [JoeyNMT](https://github.com/joeynmt/joeynmt), download
-data and train & evaluate models, as well as the necessary data for training your own model
-
-# Requirements
-
-- This only works on a Unix-like system, with bash.
-- Python 3.10 must be installed on your system, i.e. the command `python3` must be available
-- Make sure virtualenv is installed on your system. To install, e.g.
-
-    `pip install virtualenv`
-
-# Steps for macOS & Linux users
-
-Clone this repository or your fork thereof in the desired place:
-
-    git clone https://github.com/marpng/mt-exercise-03
-
-Create a new virtualenv that uses Python 3. Please make sure to run this command outside of any virtual Python environment:
-
-    ./scripts/make_virtualenv.sh
-
-**Important**: Then activate the env by executing the `source` command that is output by the shell script above.
-
-Make sure to install the exact software versions specified in the the exercise sheet before continuing.
-
-Download Moses for post-processing:
-
-    ./scripts/download_install_packages.sh
-
-
-Train a model:
-
-    ./scripts/train.sh
-
-The training process can be interrupted at any time, and the best checkpoint will always be saved. It is also possible to continue training from there later on.
-
-# Steps for Windows users
-
-This repo relies on Bash scripts (.sh files), which do not run natively on Windows (CMD or PowerShell).  
-Here are two ways to make it work:
-
-Option 1: Use WSL (Windows Subsystem for Linux)
-Enable WSL and install Ubuntu: `wsl --install`
-
-Open Ubuntu from your Start menu.
-
-Inside the Ubuntu terminal, follow the exact same steps as shown above for macOS/Linux:
-```
-git clone https://github.com/marpng/mt-exercise-4
-cd mt-exercise-4
-./scripts/make_virtualenv.sh
-./scripts/download_install_packages.sh
-./scripts/train.sh
-```     
-
-Option 2: Manually run steps without shell scripts
-If you can't use WSL, you can recreate the process manually using PowerShell or CMD
-Create and activate a virtual environment:
-```
-python -m venv venv
-.\venv\Scripts\activate
-pip install -r requirements.txt
-```
-Manually download and install Moses and other dependencies (you'll need to look inside scripts/download_install_packages.sh to replicate its steps).
-
-Run the training logic by manually executing the code inside train.sh, or porting it to a Python script or notebook.
+# MT Exercise 3: Layer Normalization for Transformer Models
+
+This repo is a collection of scripts showing how to install [JoeyNMT](https://github.com/joeynmt/joeynmt), download
+data and train & evaluate models, as well as the necessary data for training your own model
+
+# Requirements
+
+- This only works on a Unix-like system, with bash.
+- Python 3.10 must be installed on your system, i.e. the command `python3` must be available
+- Make sure virtualenv is installed on your system. To install, e.g.
+
+    `pip install virtualenv`
+
+# Steps for macOS & Linux users
+
+Clone this repository or your fork thereof in the desired place:
+
+    git clone https://github.com/marpng/mt-exercise-03
+
+Create a new virtualenv that uses Python 3. Please make sure to run this command outside of any virtual Python environment:
+
+    ./scripts/make_virtualenv.sh
+
+**Important**: Then activate the env by executing the `source` command that is output by the shell script above.
+
+Make sure to install the exact software versions specified in the the exercise sheet before continuing.
+
+Download Moses for post-processing:
+
+    ./scripts/download_install_packages.sh
+
+
+Train a model:
+
+    ./scripts/train.sh
+
+The training process can be interrupted at any time, and the best checkpoint will always be saved. It is also possible to continue training from there later on.
+
+# Steps for Windows users
+
+This repo relies on Bash scripts (.sh files), which do not run natively on Windows (CMD or PowerShell).  
+Here are two ways to make it work:
+
+Option 1: Use WSL (Windows Subsystem for Linux)
+Enable WSL and install Ubuntu: `wsl --install`
+
+Open Ubuntu from your Start menu.
+
+Inside the Ubuntu terminal, follow the exact same steps as shown above for macOS/Linux:
+```
+git clone https://github.com/marpng/mt-exercise-4
+cd mt-exercise-4
+./scripts/make_virtualenv.sh
+./scripts/download_install_packages.sh
+./scripts/train.sh
+```     
+
+Option 2: Manually run steps without shell scripts
+If you can't use WSL, you can recreate the process manually using PowerShell or CMD
+Create and activate a virtual environment:
+```
+python -m venv venv
+.\venv\Scripts\activate
+pip install -r requirements.txt
+```
+Manually download and install Moses and other dependencies (you'll need to look inside scripts/download_install_packages.sh to replicate its steps).
+
+Run the training logic by manually executing the code inside train.sh, or porting it to a Python script or notebook.
+
+#about post_norm, I add " model before: False in the yaml, Changed the variable name on the documents and number of cores about my computer.