IMDb Sentiment Classifier

Thu, 07 Nov 2024 00:00:00 +0000

A sentiment analysis model trained on the IMDb Movie Reviews dataset to classify reviews as positive or negative.

Approach

Preprocessing: tokenization, vocabulary building, and sequence padding of raw review text.
Embeddings: pre-trained GloVe word vectors give the network a head start on word meaning.
Model: a bidirectional LSTM reads each review in both directions, with batch normalization and dropout-style regularization to improve accuracy and generalization.
Training and evaluation: scripted training loop with held-out evaluation and metrics reporting.

What it demonstrates

End-to-end NLP with TensorFlow and Keras: text preprocessing, transfer learning with embeddings, recurrent architectures, and disciplined model evaluation.

Word2Vec Skip-Gram in TensorFlow 2

Tue, 11 Apr 2023 00:00:00 +0000

An implementation of the Word2Vec skip-gram algorithm in TensorFlow 2.0 that learns vector representations of words from a slice of Wikipedia (the text8 dataset).

How it works

The text8 corpus is downloaded (a helper script fetches it on demand) and processed into a vocabulary, dropping rare words below a frequency threshold.
A skip-gram model is trained with stochastic gradient descent and noise-contrastive estimation (NCE) loss.
During training the model is evaluated by finding the nearest neighbors of a set of test words using cosine similarity between their embeddings.

The implementation follows the approach described in Mikolov et al., Efficient Estimation of Word Representations in Vector Space (2013), and builds on Aymeric Damien’s TensorFlow examples.

What it demonstrates

Working knowledge of word embeddings, negative-sampling style objectives, and writing training and evaluation loops directly against the TensorFlow 2 API.

NLP | Scott Miner

IMDb Sentiment Classifier

Approach

What it demonstrates

Word2Vec Skip-Gram in TensorFlow 2

How it works

What it demonstrates