diff --git a/README.md b/README.md
index 258d00b8153..7316895c6b2 100644
--- a/README.md
+++ b/README.md
@@ -2,7 +2,7 @@
-*Declarative deep learning framework built for scale and efficiency.*
+_Declarative deep learning framework built for scale and efficiency._
[![PyPI version](https://badge.fury.io/py/ludwig.svg)](https://badge.fury.io/py/ludwig)
[![Slack](https://img.shields.io/badge/slack-chat-green.svg?logo=slack)](https://join.slack.com/t/ludwig-ai/shared_invite/zt-mrxo87w6-DlX5~73T2B4v_g6jj0pJcQ)
@@ -44,11 +44,6 @@ Or install with all optional dependencies:
pip install ludwig[full]
```
-# 🚂 Getting Started
-
-For a full tutorial, check out the official [getting started guide](https://ludwig-ai.github.io/ludwig-docs/latest/getting_started/),
-or take a look at end-to-end [Examples](https://ludwig-ai.github.io/ludwig-docs/latest/examples).
-
## Large Language Model Fine-Tuning
[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1c3AO8l_H6V_x37RwQ8V7M6A-RmcBf2tG?usp=sharing)
@@ -121,7 +116,7 @@ And now let's train the model:
ludwig train --config model.yaml --dataset "ludwig://alpaca"
```
-## Supervied ML
+## Supervised ML
Let's build a neural network that predicts whether a given movie critic's review on [Rotten Tomatoes](https://www.kaggle.com/stefanoleone992/rotten-tomatoes-movies-and-critic-reviews-dataset) was positive or negative.
@@ -144,23 +139,23 @@ Next create a YAML config file named `model.yaml` with the following:
```yaml
input_features:
- - name: genres
- type: set
- preprocessing:
- tokenizer: comma
- - name: content_rating
- type: category
- - name: top_critic
- type: binary
- - name: runtime
- type: number
- - name: review_content
- type: text
- encoder:
- type: embed
+ - name: genres
+ type: set
+ preprocessing:
+ tokenizer: comma
+ - name: content_rating
+ type: category
+ - name: top_critic
+ type: binary
+ - name: runtime
+ type: number
+ - name: review_content
+ type: text
+ encoder:
+ type: embed
output_features:
- - name: recommended
- type: binary
+ - name: recommended
+ type: binary
```
That's it! Now let's train the model: