README.md

---
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.

---

## 📊 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` |

---

## 🎯 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`.

---

## 🛠️ Quick Start

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

```python
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.