Create Transformers and Pretrained models.py

pages/Transformers and Pretrained models.py

@@ -0,0 +1,82 @@
+import streamlit as st
+from transformers import pipeline
+import torch
+
+# Page Config
+st.set_page_config(page_title='Transformers in NLP', layout='wide')
+
+# Page Title
+st.markdown('<h1 style="color:#4CAF50; text-align:center;">🤖 Transformers & Pretrained Models in NLP 🚀</h1>', unsafe_allow_html=True)
+
+# Transformer Architecture
+st.markdown('<h2 style="color:#FF5733">📌 1. Transformer Architecture</h2>', unsafe_allow_html=True)
+
+st.subheader('🔎 Definition:')
+st.write("""
+The **Transformer architecture** revolutionized NLP by using **self-attention** to process sequences in parallel. 
+- **Self-attention** enables words to focus on others dynamically.
+- The **encoder-decoder** structure is used in tasks like translation.
+
+📜 Introduced in "**Attention is All You Need**" (Vaswani et al., 2017).
+""")
+
+st.subheader('🛠️ Key Components:')
+st.write("""
+- **Encoder**: Processes input tokens into internal representations.
+- **Decoder**: Uses encoder outputs to generate predictions.
+- **Multi-head Attention**: Allows diverse focus across sequences.
+- **Positional Encoding**: Injects sequence order into embeddings.
+""")
+
+# Pretrained Models
+st.markdown('<h2 style="color:#3E7FCB">📌 2. Pretrained Models</h2>', unsafe_allow_html=True)
+
+st.subheader('🔍 Definition:')
+st.write("""
+Pretrained models leverage vast corpora to understand language patterns.
+- **BERT**: Bi-directional context learning for diverse NLP tasks.
+- **GPT**: Text generation with autoregressive modeling.
+- **RoBERTa**: Optimized BERT variant.
+- **T5**: Universal text-to-text learning.
+- **XLNet**: Captures dependencies in all positions.
+""")
+
+# Sentiment Analysis Example
+st.subheader('🎭 Pretrained Model Example: Sentiment Analysis')
+nlp = pipeline("sentiment-analysis", model="bert-base-uncased")
+text = st.text_area("📝 Enter text to analyze", "Transformers are amazing!")
+if st.button('📊 Analyze Sentiment'):
+    result = nlp(text)
+    st.write(f"**🧐 Result:** {result}")
+
+# Fine-tuning Pretrained Models
+st.markdown('<h2 style="color:#E67E22">📌 3. Fine-tuning Pretrained Models</h2>', unsafe_allow_html=True)
+
+st.subheader('⚙️ Definition:')
+st.write("""
+Fine-tuning tailors pretrained models to specific NLP tasks:
+- **Sentiment Analysis**: Classifies text sentiments.
+- **Named Entity Recognition (NER)**: Detects names, locations, organizations.
+- **Question Answering**: Extracts answers from given contexts.
+""")
+
+# NER Example
+st.subheader('🔎 Named Entity Recognition (NER)')
+nlp_ner = pipeline("ner", model="dbmdz/bert-large-cased-finetuned-conll03-english")
+text_ner = st.text_area("🔤 Enter text for NER", "Barack Obama was born in Hawaii.")
+if st.button('🔬 Perform NER'):
+    ner_results = nlp_ner(text_ner)
+    st.write("**🔍 NER Results:**")
+    for entity in ner_results:
+        st.write(f"📝 {entity['word']} - {entity['entity']} - Confidence: {entity['score']:.2f}")
+
+# Question Answering Example
+st.subheader('🧠 Question Answering with BERT')
+nlp_qa = pipeline("question-answering", model="bert-large-uncased-whole-word-masking-finetuned-squad")
+context = st.text_area("📚 Enter context", "Transformers revolutionized NLP with parallel processing.")
+question = st.text_input("❓ Ask a question", "What did transformers revolutionize?")
+if st.button('🎤 Get Answer'):
+    answer = nlp_qa(question=question, context=context)
+    st.write(f"**📝 Answer:** {answer['answer']}")
+
+st.markdown('<h3 style="color:#4CAF50; text-align:center;">✨ Thanks for Exploring NLP! ✨</h3>', unsafe_allow_html=True)