README.md

metadata

dataset_info:
  features:
    - name: id
      dtype: string
    - name: text
      dtype: string
    - name: entities
      struct:
        - name: start
          list: int32
        - name: end
          list: int32
        - name: label
          list: string
  splits:
    - name: train
      num_bytes: 433090
      num_examples: 457
  download_size: 285220
  dataset_size: 433090
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
license: mit
language:
  - en
pretty_name: WallStreetBets NER Dataset
size_categories:
  - n<1K
task_categories:
  - token-classification
tags:
  - ner
  - named-entity-recognition
  - wsb
  - reddit
  - financial-posts
  - finance
  - stocks
  - wallstreetbets
  - r/wallstreetbets

WallStreetBets NER Dataset

A high-quality, human-annotated Named Entity Recognition (NER) dataset composed of 457 gold-standard samples extracted from Reddit's r/wallstreetbets. This dataset is explicitly curated to train zero-shot and token-classification models (like GLiNER) to parse chaotic retail investor text, financial slang, and complex asset mentions.

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📊 Dataset Overview

• Total Samples: 457 curated posts and long-form "Due Diligence" (DD) manifestos.
• Target Domain: Retail finance, options trading, and market commentary.
• Format: Clean text paired with explicit absolute character offset annotations (start, end, label).

Entity Schema

Label	Description	Examples
ticker	A market ticker symbol representing a tradable financial asset/option underlying.	$AAPL, TSLA, AMD, GLD
company	The corporate body, business entity, brand, employer, or manufacturer.	Microsoft, Melvin Capital, NVIDIA

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🎯 Annotation Guidelines & Boundary Rules

To ensure high precision and eliminate labeling noise, annotations strictly adhere to the following linguistic and data engineering boundaries:

1. The Context Disambiguation Rule (e.g., AMD / Tesla)

Entities are classified based on how they function in the sentence context, not solely by the keyword itself:

• Labeled as ticker: "Long on AMD 180c options" or "Buying shares of TSLA" (Functions as a transactional asset/market symbol).
• Labeled as company: "AMD released their new AI chips" or "I want to work at Tesla" (Functions as a corporate entity, manufacturer, or employer).

2. Strict Currency Exclusions (Noise Mitigation)

• Fiat Currencies: Monetary values, bills, and account units (e.g., $2, $22, $10k, USD, EUR) are hard-excluded and never annotated.
• Cryptocurrencies: Digital protocol tokens (e.g., BTC, ETH, SOL) are excluded from this equity-centric release to prevent semantic drift.

3. Commodities Disambiguation

• Generic Commodities: Words like "gold", "silver", or "oil" are treated as common nouns and left unlabelled.
• Commodity Instruments: Asset-tracking ETFs (e.g., GLD, SLV) are strictly labeled as ticker.

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🛠️ Quick Start

You can load this dataset instantly using the Hugging Face datasets library:

from datasets import load_dataset

# Load the dataset
dataset = load_dataset("StephanAkkerman/wallstreetbets-ner")

# Preview a sample record
sample = dataset["train"][0]
print(f"Text:\n{sample['text']}\n")
print("Entities Found:")
for start, end, label in zip(sample["entities"]["start"], sample["entities"]["end"], sample["entities"]["label"]):
    entity_text = sample["text"][start:end]
    print(f" - [{label}] {entity_text} ({start} -> {end})")

🧬 Intended Use & Model Training

This dataset is designed for training small-footprint sequence models and fine-tuning adapters (like LoRA models for GLiNER2).

• Training Note: For long posts over 256 tokens, it is highly recommended to perform overlapping sliding-window chunking (e.g., 150-word chunks with a 40-word overlap) to fit attention windows and optimize VRAM allocation without chopping tokens on boundaries.