Best NLP Datasets For Natural Language Processing

In the rapidly evolving world of artificial intelligence, natural language processing (NLP) has emerged as one of the most impactful domains—powering everything from chatbots and sentiment analysis to machine translation and summarization engines. But at the heart of every effective NLP model lies one key element: data.

Whether you’re fine-tuning a sentiment classifier or building a multilingual question-answering system, your model is only as good as the dataset it’s trained on. That’s why choosing the right NLP dataset—or building one yourself—is one of the most important decisions you’ll make in any NLP project.

In this guide, we’ll explore the landscape of natural language processing datasets, covering the best available open-source options, where to find them, how to build your own, and how to structure and scale your data pipeline. Along the way, we’ll show you how NetNut’s residential proxies and mobile proxy network plays a vital role in helping teams access hard-to-reach data sources safely, ethically, and at scale.

What Are NLP Datasets?

An NLP dataset is a collection of text-based data used to train, test, or fine-tune models in natural language processing. These datasets are typically annotated with labels or structured in a way that allows machines to learn specific language-related tasks—such as identifying the sentiment of a sentence, recognizing named entities (like people or locations), or predicting the next word in a sentence.

NLP datasets vary widely in structure and content depending on the specific task. They might contain:

• Raw or cleaned text (e.g., articles, tweets, reviews)
• Tokens and part-of-speech tags
• Sentence-level sentiment labels
• Paragraphs with summaries
• Question-answer pairs
• Translated sentence pairs for multilingual tasks

Some of the most well-known NLP datasets, like SQuAD, IMDB, or CoNLL-2003, are publicly available and widely used in academic research and industry.

However, off-the-shelf datasets don’t always match your specific needs—especially if you’re working in niche industries, low-resource languages, or real-time use cases. In those scenarios, web scraping becomes essential—and accessing that data efficiently and securely is where NetNut proxies can give you a critical edge.

What Makes a Good NLP Dataset?

Not all data is created equal. When evaluating or building an NLP dataset, several factors determine whether the data will actually help your model perform well in real-world applications.

Relevance to Your Task

A dataset should closely reflect the use case you’re building for. For example, if you’re training a chatbot for healthcare, Reddit comments won’t be nearly as useful as transcripts from medical forums or patient feedback.

Size and Diversity

The larger and more diverse the dataset, the better the model can generalize. This means including a variety of sentence structures, topics, and tones—across multiple demographics or regions if applicable.

Annotation Quality

Labeled datasets are often used in supervised learning. Whether it’s sentiment scores or named entities, your labels must be consistent and accurate to avoid teaching your model bad habits.

Recency and Real-World Language

Language evolves rapidly. A great NLP dataset should reflect modern usage—including slang, emojis, and domain-specific jargon—especially for applications like social listening or customer service automation.

Ethical and Legal Considerations

Ensure that your dataset complies with privacy laws, includes proper attribution where required, and avoids scraping from gated or sensitive sources. This is especially important when gathering your own dataset via web scraping.

Top NLP Datasets for Core NLP Tasks

Different NLP tasks require different types of datasets. Here’s a curated list of some of the most trusted, widely used, and high-quality NLP datasets, categorized by task. These are great starting points for training or benchmarking your models.

A. Text Classification

• AG News: A large collection of news articles classified into four categories—World, Sports, Business, and Science/Technology.
• Yelp Reviews: Contains thousands of customer reviews labeled by star rating or sentiment polarity.
• Amazon Reviews: Product reviews labeled by star ratings, helpful for training sentiment and product analysis models.

B. Named Entity Recognition (NER)

• CoNLL-2003: Classic NER dataset with labeled entities (PER, ORG, LOC, MISC) based on Reuters newswire data.
• OntoNotes 5: More extensive NER dataset including entities, coreference, and syntax layers.
• WikiANN: Multilingual NER dataset built from Wikipedia with consistent annotation across many languages.

C. Question Answering (QA)

• SQuAD (Stanford Question Answering Dataset): Human-annotated questions based on Wikipedia passages. A gold standard for QA tasks.
• Natural Questions: From Google, this dataset features real search queries paired with answers from Wikipedia.
• HotpotQA: Multihop QA dataset where answers require reasoning across multiple Wikipedia paragraphs.

D. Sentiment Analysis

• IMDB: 50,000 movie reviews labeled as positive or negative.
• SST (Stanford Sentiment Treebank): Offers fine-grained sentiment labels at both phrase and sentence levels.
• Sentiment140: Uses tweets and emoticons to classify sentiment in short-form, informal text.

E. Text Summarization

• CNN/DailyMail: Large collection of news articles with corresponding human-written summaries.
• XSum: BBC news articles and concise single-sentence summaries—useful for abstractive summarization models.
• Multi-News: Summaries of multiple news articles on the same event/topic, ideal for multi-document summarization.

F. Translation & Multilingual NLP

• Europarl: Transcripts of the European Parliament, aligned across multiple languages.
• WMT (Workshop on Machine Translation): Benchmark datasets used for translation competitions.
• Tatoeba: Open, crowd-sourced collection of sentence translations in over 350 languages.

Sources to Access NLP Datasets

There are many platforms that host public natural language processing data sets—from research institutions to open-source communities. Here are some of the most popular and reliable sources:

Hugging Face Datasets Hub

A massive and growing repository of curated datasets for all NLP tasks. Offers plug-and-play support for Python via the datasets library.

Kaggle

Well-known for its competitions, Kaggle also hosts hundreds of NLP datasets shared by its user community—often already cleaned and annotated.

Google Dataset Search

A powerful meta-search engine for public datasets, useful for discovering hidden gems across government and academic portals.

LDC (Linguistic Data Consortium)

Offers high-quality, curated language datasets. While many are paid, they’re great for high-stakes, commercial NLP applications.

Web Scraping

For tasks not covered by public data—like real-time sentiment, market trends, or user-generated content—scraping the web is often the only option.

This is where NetNut shines. Using rotating residential proxies, you can:

• Access data behind soft paywalls or geo-blocks
• Rotate IPs to avoid bans when scraping at scale
• Target mobile-optimized versions of content using mobile proxies
• Build datasets from blogs, forums, product listings, and more

Using Web Scraping to Build Your Own NLP Dataset

Prebuilt datasets are helpful, but what if your use case doesn’t fit the mold? For niche industries, regional languages, or specific user segments, building your own NLP dataset is the best path forward.

Why Build Your Own NLP Dataset?

• No existing dataset meets your domain-specific needs
• You want up-to-date content (e.g., news, social media, product listings)
• You’re targeting underrepresented dialects or languages
• You need training data for custom chatbots, intent models, or voice assistants

What Can You Scrape?

• News websites for summarization and sentiment analysis
• Reddit and forums for question answering and topic modeling
• E-commerce sites for product classification and review sentiment
• Legal and medical sites for domain-specific language understanding

Scraping Tools to Use:

• Scrapy: Framework for building structured crawlers
• BeautifulSoup: Lightweight HTML parser
• Playwright/Selenium: Browser automation for JavaScript-heavy pages

How NetNut Makes Scraping Smarter:

• Rotating Residential Proxies: Avoid bans and mimic real users
• Mobile Proxies: Access mobile-only or app-like sites
• Geo-Targeting: Collect content from specific countries or cities
• Sticky Sessions: Maintain state across multi-step data extractions

Structuring and Cleaning NLP Data

Once you’ve sourced your data—whether from a public repository or through web scraping—the next critical step is cleaning and structuring it for your NLP task. Raw text data is often noisy and inconsistent, making preprocessing a non-negotiable part of your pipeline.

Key Preprocessing Steps for NLP Datasets:

• Text Normalization
  • Lowercasing
  • Removing special characters, HTML tags, and emojis (if irrelevant)
  • Dealing with contractions (e.g., “don’t” → “do not”)
• Tokenization
  • Breaking text into words, subwords, or characters
  • Libraries: spaCy, NLTK, Hugging Face Tokenizers
• Stopword Removal
  • Filtering out common words like “and,” “the,” or “is” unless contextually important
• Stemming and Lemmatization
  • Reducing words to their root forms (e.g., “running” → “run”)
• Annotation
  • Adding labels for classification, tagging entities for NER, or segmenting text for summarization
• Data Format
  • Use NLP-friendly formats such as:
    • JSONL: for line-by-line samples (great for transformers)
    • CSV/TSV: for structured tabular data
    • CoNLL: for NER/sequence tagging tasks

Clean data leads to better models, fewer hallucinations, and more actionable outputs. Whether your data comes from scraped web pages or existing datasets, investing time in cleaning pays off in downstream performance.

Building an NLP Dataset Pipeline (End-to-End)

A scalable NLP system isn’t just about a great model—it’s about building a repeatable, automated pipeline for data ingestion, processing, and storage.

Here’s what an ideal NLP dataset pipeline looks like:

Step 1: Data Collection

• Pull data from static files, APIs, or scraped websites
• Use NetNut proxies to reach geo-restricted or protected sources

Step 2: Preprocessing

• Clean, normalize, and tokenize the data
• Structure into fields (e.g., text, label, source)

Step 3: Annotation (Optional)

• Manually label data or use pre-trained models for semi-supervised annotation
• Tools: Prodigy, Label Studio, Doccano

Step 4: Storage and Versioning

• Store datasets in versioned repositories (e.g., DVC, Hugging Face Hub, Git LFS)
• Use cloud storage for larger datasets

Step 5: Automation

• Use schedulers like Airflow, Luigi, or simple cron jobs to automate scraping and cleaning tasks
• Combine with NetNut rotating proxies to ensure reliable access over time

Step 6: Continuous Updates

• Re-run the pipeline weekly or monthly to keep the data fresh—especially useful for models requiring real-time awareness

Challenges in NLP Data Collection (and How NetNut Solves Them)

Collecting high-quality NLP data isn’t as easy as downloading a CSV. Here are the top challenges teams face—and how NetNut solves each one:

Challenge 1: IP Bans and Anti-Bot Systems

Sites with valuable language content often block repeated requests from the same IP.

NetNut Solution:
Use rotating residential proxies to simulate natural user behavior and avoid detection.

Challenge 2: Geo-Restricted or Region-Specific Content

Some content is only visible from certain countries (e.g., localized news, product listings, or legal documents).

NetNut Solution:
Geo-targeted proxies let you route requests from specific countries or cities.

Challenge 3: Mobile-Only Content

Some forums and platforms serve different content to mobile devices or are only accessible via mobile IPs.

NetNut Solution:
Use mobile proxies with real 3G/4G IPs to collect data just like a smartphone would.

Challenge 4: Session Instability During Multi-Step Scraping

When your scraper needs to log in, navigate multiple pages, or maintain a session, changing IPs can break the process.

NetNut Solution:
Sticky sessions allow for IP persistence across multiple requests—ideal for multi-step crawls or authenticated flows.

Challenge 5: Speed vs. Reliability

Public proxies often throttle speed or drop connections under load.

NetNut Solution:
With ISP-direct, high-speed proxy infrastructure, NetNut delivers faster, more reliable scraping—critical for building large-scale NLP datasets without delays.

​​FAQs

Where can I find high-quality NLP datasets?

You can explore platforms like Hugging Face, Kaggle, and Google Dataset Search, or access academic corpora from organizations like the Linguistic Data Consortium (LDC). If you need custom or real-time content, web scraping using proxies like NetNut is a powerful alternative.

Is it legal to scrape data for NLP projects?

It depends. Many sites allow scraping of public data as long as it’s not behind a login or paywall. However, always check the site’s terms of service and ensure your scraping adheres to ethical and legal standards. NetNut supports compliant data access with infrastructure that avoids violating rate limits or access policies.

How large should my NLP dataset be?

There’s no one-size-fits-all answer. A few thousand samples might be enough for fine-tuning a sentiment classifier, while training a language model from scratch could require billions of tokens. Diversity and quality often matter more than raw size.

What’s the difference between an NLP dataset and an NLP database?

An NLP dataset refers to a structured collection of text data used for training or testing models. An NLP database could be a larger, often dynamic system that stores and serves this data—especially in production environments.

Why do I need proxies when building NLP datasets?

If you’re collecting real-world or geo-restricted content, proxies:

• Prevent IP bans
• Allow regional data collection
• Maintain access over time
• Scale your data collection reliably

NetNut’s rotating residential and mobile proxies are ideal for this, offering enterprise-grade speed, reach, and control.