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https://github.com/abdelrahman-amen/active_learning_in_nlp

I applied active learning to the IMDB dataset for sentiment analysis. Starting with a small labeled subset, I trained a model and used uncertainty sampling to select and label challenging reviews. This iterative process improved performance while reducing labeling effort.
https://github.com/abdelrahman-amen/active_learning_in_nlp

activelearning cuda entropy imdb-dataset margin nlp python sklearnex torch uncertainty

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I applied active learning to the IMDB dataset for sentiment analysis. Starting with a small labeled subset, I trained a model and used uncertainty sampling to select and label challenging reviews. This iterative process improved performance while reducing labeling effort.

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README 

        
# Active Learning in NLP Tasks 🪐



# Introduction 🔍



Active learning is a strategy in machine learning where the model selectively identifies the most informative, unlabeled data points for annotation. This approach is especially beneficial in scenarios where labeled data is scarce, expensive, or time-consuming to obtain. By focusing on the most critical data, active learning enables the model to achieve high performance with minimal labeled examples.



# Why Active Learning for NLP? 🎯



In natural language processing (NLP), active learning is particularly valuable because:



• Minimized Labeling Effort: Annotating large datasets, such as labeling text for sentiment analysis or entity recognition, can be tedious and costly. Active learning reduces the workload.



• Improved Accuracy: By selecting the most challenging or ambiguous text samples, the model gains better insights into the dataset.



• Efficient Use of Resources: Models trained with active learning achieve high performance even with limited labeled data.



# Common NLP Applications 🧠

Active learning can be applied across a wide range of NLP tasks, including:



• Text Classification: Automatically tagging emails as spam or categorizing news articles into topics.



• Sentiment Analysis: Identifying whether a review or social media post expresses positive, neutral, or negative sentiment.



• Named Entity Recognition (NER): Extracting specific entities like names, dates, and organizations from unstructured text.



• Question Answering: Enhancing models by prioritizing ambiguous or uncertain queries for annotation.



• Text Summarization: Refining models to better summarize content by focusing on complex or diverse examples.



# How to Apply Active Learning in NLP 📊



## Step 1: Prepare Your Dataset



• Begin with a small labeled dataset to train an initial model.



• Keep a large pool of unlabeled data from which informative samples will be selected.



## Step 2: Choose an Active Learning Strategy 💡



1. Uncertainty Sampling:



• Query data points where the model has the least confidence in its predictions.



• For example, in a text classification task, select samples with nearly equal probabilities across multiple classes.



2. Margin Sampling:



• Focus on samples near the decision boundary of the model.



• In NLP, this can mean choosing sentences with ambiguous sentiment or classification scores close to the threshold.



3. Entropy Sampling:



• Select samples with the highest prediction entropy, where the model is most uncertain about its prediction.



• For instance, a sentence where predicted probabilities for “positive,” “negative,” and “neutral” are evenly distributed.



## Step 3: Annotate and Update the Model



• Send the selected samples to an oracle (e.g., human annotator) for labeling.



• Use the newly labeled data to retrain or fine-tune the model.



## Step 4: Iterate



• Repeat the process until the model achieves satisfactory performance or labeling resources are exhausted.



# Example Workflow for NLP Tasks 🚀



1.Text Classification:



• Start with a model trained on a small dataset of labeled news articles.



• Use active learning to select samples with high uncertainty (e.g., articles classified with low confidence) for annotation.



2.Named Entity Recognition (NER):



• Annotate sentences where the model struggles to identify entities or predicts multiple entity types with similar probabilities.



• This ensures the model learns to identify complex entities efficiently.



3.Sentiment Analysis:



• Identify reviews or tweets where the sentiment score is ambiguous, such as near the threshold of positivity or negativity.



• Annotating such data improves the model's ability to handle nuanced text.



# Benefits for NLP Projects 🌟



• Cost Efficiency: Reduces the need for exhaustive annotation of all text data.



• Domain Adaptability: Helps train models for niche domains, like legal or medical texts, where labeled data is hard to obtain.



• Improved Accuracy: Enhances model generalization by prioritizing difficult or diverse samples.



# Conclusion 📝

Active learning in NLP optimizes data labeling by focusing on the most informative text samples. Whether it's sentiment analysis, NER, or text classification, this technique improves model performance while reducing the burden of large-scale annotation.