Add baseline example, fix Git commands

README.md

@@ -10,16 +10,25 @@ This repository can be used to generate a template so you can submit your predic
 
 ## Quickstart
 
-### 1. Create an account and repository on the Hugging Face Hub
+### 1. Create an account on the Hugging Face Hub
 
 First create an account on the Hugging Face Hub and you can sign up [here](https://huggingface.co/join) if you haven't already!
 
 ### 2. Create a template repository on your machine
 
-The next step is to create a template repository on your local machine that contains various files and a CLI to help you validate and submit your predictions. You can run the following commands to create the repository:
+The next step is to create a template repository on your local machine that contains various files and a CLI to help you validate and submit your predictions. The Hugging Face Hub uses [Git Large File Storage (LFS)](https://git-lfs.github.com) to manage large files, so first install it if you don't have it already. For example, on macOS you can run:
 
 ```bash
-# Install the following libraries if you don't have them
+brew install git-lfs
+git lfs install
+```
+
+Next, run the following commands to create the repository. We recommend creating a Python virtual environment for the project, e.g. with Anaconda:
+
+```bash
+# Create and activate a virtual environment
+conda create -n raft python=3.8 && conda activate raft
+# Install the following libraries
 pip install cookiecutter huggingface-hub==0.0.16
 # Create the template repository
 cookiecutter git+https://huggingface.co/datasets/ought/raft-submission
@@ -86,13 +95,11 @@ my-raft-submission
 
 ### 3. Install the dependencies
 
-The final step is to install the project's dependencies. We recommend creating a Python virtual environment for the project, e.g. with Anaconda:
+The final step is to install the project's dependencies:
 
 ```bash
 # Navigate to the template repository
 cd my-raft-submissions
-# Create and activate a virtual environment
-conda create -n raft python=3.8 && conda activate raft
 # Install dependencies
 python -m pip install -r requirements.txt
 ```
@@ -124,7 +131,31 @@ For each task in RAFT, you should create a CSV file called `predictions.csv` wit
 * ID (int)
 * Label (string)
 
-See the dummy predictions in the `data` folder for examples with the expected format. Each `predictions.csv` file should be stored in the task's subfolder in `data` and at the end you should have something like the following:
+See the dummy predictions in the `data` folder for examples with the expected format. Here is a simple example that creates a majority-class baseline:
+
+```python
+from pathlib import Path
+import pandas as pd
+from collections import Counter
+from datasets import load_dataset, get_dataset_config_names
+
+tasks = get_dataset_config_names("ought/raft")
+
+for task in tasks:
+    # Load dataset
+    raft_subset = load_dataset("ought/raft", task)
+    # Compute majority class over training set
+    counter = Counter(raft_subset["train"]["Label"])
+    majority_class = counter.most_common(1)[0][0]
+    # Load predictions file
+    preds = pd.read_csv(f"data/{task}/predictions.csv")
+    # Convert label IDs to label names
+    preds["Label"] = raft_subset["train"].features["Label"].int2str(majority_class)
+    # Save predictions
+    preds.to_csv(f"data/{task}/predictions.csv", index=False)
+```
+
+As you can see in the example, each `predictions.csv` file should be stored in the task's subfolder in `data` and at the end you should have something like the following:
 
 ```
 data

{{cookiecutter.repo_name}}/README.md

@@ -29,7 +29,31 @@ For each task in RAFT, you should create a CSV file called `predictions.csv` wit
 * ID (int)
 * Label (string)
 
-See the dummy predictions in the `data` folder for examples with the expected format. Each `predictions.csv` file should be stored in the task's subfolder in `data` and at the end you should have something like the following:
+See the dummy predictions in the `data` folder for examples with the expected format. Here is a simple example that creates a majority-class baseline:
+
+```python
+from pathlib import Path
+import pandas as pd
+from collections import Counter
+from datasets import load_dataset, get_dataset_config_names
+
+tasks = get_dataset_config_names("ought/raft")
+
+for task in tasks:
+    # Load dataset
+    raft_subset = load_dataset("ought/raft", task)
+    # Compute majority class over training set
+    counter = Counter(raft_subset["train"]["Label"])
+    majority_class = counter.most_common(1)[0][0]
+    # Load predictions file
+    preds = pd.read_csv(f"data/{task}/predictions.csv")
+    # Convert label IDs to label names
+    preds["Label"] = raft_subset["train"].features["Label"].int2str(majority_class)
+    # Save predictions
+    preds.to_csv(f"data/{task}/predictions.csv", index=False)
+```
+
+As you can see in the example, each `predictions.csv` file should be stored in the task's subfolder in `data` and at the end you should have something like the following:
 
 ```
 data

{{cookiecutter.repo_name}}/cli.py

@@ -70,9 +70,8 @@ def validate():
 
 @app.command()
 def submit():
-    # TODO(lewtun): Replace with subprocess.run and only add the exact files we need for evaluation
     subprocess.call("git pull origin main".split())
-    subprocess.call(["git", "add", "data"])
+    subprocess.call(["git", "add", "'*predictions.csv'"])
     subprocess.call(["git", "commit", "-m", "Submission"])
     subprocess.call(["git", "push"])