NLP Sentiment Analysis Demo

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Model Architecture & Features

Transformer Architecture

Utilizes BERT's multi-layer bidirectional Transformer encoder to understand context from both directions.

High Accuracy

Achieves 95% accuracy on validation set through careful fine-tuning of pre-trained weights.

Contextual Understanding

Captures nuanced sentiment by analyzing word relationships in full sentence context.

Implementation Highlights

sentiment_analysis.py

from transformers import BertTokenizer, TFBertForSequenceClassification
from transformers import InputExample, InputFeatures
import tensorflow as tf

# Load pre-trained model and tokenizer
model = TFBertForSequenceClassification.from_pretrained('bert-base-uncased')
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')

# Prepare dataset (IMDb reviews)
train_examples = [
    InputExample(guid=None, text_a="This movie was fantastic!", label=1),
    # ... more examples ...
]

# Tokenize inputs
train_encodings = tokenizer([x.text_a for x in train_examples], 
                        truncation=True, padding=True, 
                        max_length=128)

# Fine-tune model
optimizer = tf.keras.optimizers.Adam(learning_rate=5e-5)
loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
model.compile(optimizer=optimizer, loss=loss, metrics=['accuracy'])

# Train and evaluate
model.fit(train_dataset.shuffle(1000).batch(16), 
          epochs=3, 
          batch_size=16)

# Evaluation showed 95% accuracy on validation set

Sentiment Analysis with BERT