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import streamlit as st
st.markdown("""
<style>
/* Set a soft background color */
body {
background-color: #eef2f7;
}
/* Style for main title */
h1 {
color: black;
font-family: 'Roboto', sans-serif;
font-weight: 700;
text-align: center;
margin-bottom: 25px;
}
/* Style for headers */
h2 {
color: red;
font-family: 'Roboto', sans-serif;
font-weight: 600;
margin-top: 30px;
}
/* Style for subheaders */
h3 {
color: violet;
font-family: 'Roboto', sans-serif;
font-weight: 500;
margin-top: 20px;
}
.custom-subheader {
color: violet;
font-family: 'Roboto', sans-serif;
font-weight: 600;
margin-bottom: 15px;
}
/* Paragraph styling */
p {
font-family: 'Georgia', serif;
line-height: 1.8;
color: black;
margin-bottom: 20px;
}
/* List styling with checkmark bullets */
.icon-bullet {
list-style-type: none;
padding-left: 20px;
}
.icon-bullet li {
font-family: 'Georgia', serif;
font-size: 1.1em;
margin-bottom: 10px;
color: black;
}
.icon-bullet li::before {
content: "◆";
padding-right: 10px;
color: black;
}
/* Sidebar styling */
.sidebar .sidebar-content {
background-color: #ffffff;
border-radius: 10px;
padding: 15px;
}
.sidebar h2 {
color: #495057;
}
/* Custom button style */
.streamlit-button {
background-color: #00FFFF;
color: #000000;
font-weight: bold;
}
</style>
""", unsafe_allow_html=True)
st.markdown("<h1 class='title'>📖 NLP Terminology</h1>", unsafe_allow_html=True)
st.markdown("<p class='caption'>✨ Explore essential terms in Natural Language Processing and their meanings!...</p>", unsafe_allow_html=True)
st.header("📝 Corpus")
st.markdown("- **A corpus** is a collection of documents.")
st.header("📄 Document")
st.markdown("- **A document** is a collection of sentences, paragraphs, single words, or even single characters.")
st.header("📝 Paragraph")
st.markdown("- **A paragraph** consists of multiple sentences.")
st.header("📢 Sentence")
st.markdown("- **A sentence** is a collection of words.")
st.header("🔤 Word")
st.markdown("- **Words** are made up of characters.")
st.header("🔠 Character")
st.markdown("- **A character** can be a number, alphabet, or special symbol.")
st.header("✂️ Tokenization")
st.markdown("- **Tokenization** is a technique by using which we can convert a huge chunk into small entity where those small entities are known as tokens.")
st.subheader("🛠️ Types of Tokenization")
st.markdown("""
- 🔹 **Sentence Tokenization** – Splits text into sentences.
- 🔹 **Word Tokenization** – Splits sentences into words.
- 🔹 **Character Tokenization** – Splits words into individual characters.
""")
st.subheader("📝 Sentence Tokenization")
st.markdown("- **Breaks a large text into meaningful sentence units.**")
st.subheader("📖 Word Tokenization")
st.markdown("- **Splits a sentence into individual words.**")
st.subheader("🔡 Character Tokenization")
st.markdown("- **Breaks words into separate characters.**")
st.header("🚫 Stop Words")
st.markdown("- **Common words** (e.g., 'the', 'is', 'and') that do not add meaning to the text but maintain grammatical structure.")
st.header("📊 Vectorization")
st.markdown("- **Transforms text into numerical representation** for machine learning models.")
st.subheader("🔢 Different Types of Vectorization Techniques")
st.markdown("""
- 🎯 **One-Hot Encoding**
- 🏷️ **Bag of Words (BoW)**
- 📊 **TF-IDF (Term Frequency-Inverse Document Frequency)**
- 🧠 **Word2Vec**
- 🌍 **GloVe**
- ⚡ **FastText**
""")
st.success("🚀 Mastering these **NLP terminologies** will help you build powerful text-processing applications!") |