task_categories:text-generation task_categories:text2text-generation task_categories:question-answering size_categories:n<1K format:parquet modality:text library:datasets library:pandas library:mlcroissant library:polars library:distilabel region:us synthetic distilabel rlaif datacraft
---
size_categories: n<1K
task_categories:
- text-generation
- text2text-generation
- question-answering
dataset_info:
  features:
  - name: prompt
    dtype: string
  - name: completion
    dtype: string
  - name: system_prompt
    dtype: string
  splits:
  - name: train
    num_bytes: 506493
    num_examples: 100
  download_size: 208904
  dataset_size: 506493
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
tags:
- synthetic
- distilabel
- rlaif
- datacraft
---

<p align="left">
  <a href="https://github.com/argilla-io/distilabel">
    <img src="https://raw.githubusercontent.com/argilla-io/distilabel/main/docs/assets/distilabel-badge-light.png" alt="Built with Distilabel" width="200" height="32"/>
  </a>
</p>

# Dataset Card for my-distiset-e66dde51

This dataset has been created with [distilabel](https://distilabel.argilla.io/).



## Dataset Summary

This dataset contains a `pipeline.yaml` which can be used to reproduce the pipeline that generated it in distilabel using the `distilabel` CLI:

```console
distilabel pipeline run --config "https://huggingface.co/datasets/matk9/my-distiset-e66dde51/raw/main/pipeline.yaml"
```

or explore the configuration:

```console
distilabel pipeline info --config "https://huggingface.co/datasets/matk9/my-distiset-e66dde51/raw/main/pipeline.yaml"
```

## Dataset structure

The examples have the following structure per configuration:


<details><summary> Configuration: default </summary><hr>

```json
{
    "completion": "Here are the 500 examples of natural language text with corresponding tickers for training an embedding model.\n\n```python\nimport pandas as pd\nimport numpy as np\n\n# Define a function to generate stock tickers\ndef generate_stock_tickers():\n    # Define lists of stocks\n    stocks = {\n        \u0027US Stocks\u0027: [\u0027AAPL\u0027, \u0027GOOGL\u0027, \u0027MSFT\u0027, \u0027AMZN\u0027, \u0027FB\u0027, \u0027TSLA\u0027, \u0027NVDA\u0027, \u0027PYPL\u0027, \u0027CHLN\u0027, \u0027PEP\u0027],\n        \u0027International Stocks\u0027: [\u0027RDSA.L\u0027, \u0027TSM.L\u0027, \u0027SAP.DE\u0027, \u0027VOD.L\u0027, \u0027RIO.L\u0027, \u0027UNH\u0027, \u0027CVS\u0027, \u0027TEF\u0027, \u0027DBK.DE\u0027, \u0027JPM\u0027],\n        \u0027Dividend Stocks\u0027: [\u0027JNJ\u0027, \u0027MRK\u0027, \u0027VZ\u0027, \u0027PG\u0027, \u0027CVX\u0027, \u0027PXD\u0027, \u0027PSX\u0027, \u0027XOM\u0027, \u0027DVY\u0027, \u0027AGNC\u0027],\n        \u0027Growth Stocks\u0027: [\u0027SHOP\u0027, \u0027SQ\u0027, \u0027SHOP\u0027, \u0027CRM\u0027, \u0027ZM\u0027, \u0027FB\u0027, \u0027PYPL\u0027, \u0027ADBE\u0027, \u0027MELI\u0027, \u0027TSLA\u0027],\n        \u0027Value Stocks\u0027: [\u0027VLO\u0027, \u0027COST\u0027, \u0027O\u0027, \u0027GM\u0027, \u0027AAL\u0027, \u0027DAL\u0027, \u0027ALK\u0027, \u0027RCL\u0027, \u0027CCL\u0027, \u0027CXO\u0027],\n        \u0027Index Funds\u0027: [\u0027SPY\u0027, \u0027QQQ\u0027, \u0027EFA\u0027, \u0027EEM\u0027, \u0027IWM\u0027, \u0027VTI\u0027, \u0027VWO\u0027, \u0027XLK\u0027, \u0027XLY\u0027, \u0027XLP\u0027],\n        \u0027ETFs\u0027: [\u0027GLD\u0027, \u0027SLV\u0027, \u0027QLD\u0027, \u0027TQQQ\u0027, \u0027IWM\u0027, \u0027VOO\u0027, \u0027VTI\u0027, \u0027XLV\u0027, \u0027XLF\u0027, \u0027XLU\u0027]\n    }\n\n    # Generate random stock tickers\n    stock_tickers = []\n    for category in stocks.values():\n        for stock in category:\n            stock_tickers.append(stock)\n    \n    return stock_tickers\n\n# Define a function to generate cryptocurrency tickers\ndef generate_crypto_tickers():\n    # Define lists of cryptocurrencies\n    cryptos = {\n        \u0027Major Cryptocurrencies\u0027: [\u0027BTCUSDT\u0027, \u0027ETHUSDT\u0027, \u0027LTCUSDT\u0027, \u0027XRPUSDT\u0027, \u0027BCHUSDT\u0027, \u0027EOSUSDT\u0027, \u0027BATUSDT\u0027, \u0027TRXUSDT\u0027],\n        \u0027Altcoins\u0027: [\u0027LINKUSDT\u0027, \u0027ADAUSDT\u0027, \u0027DOTUSDT\u0027, \u0027RUNEUSDT\u0027, \u0027CELRUSDT\u0027, \u0027NEARUSDT\u0027, \u0027SOLUSDT\u0027, \u0027AVAXUSDT\u0027],\n        \u0027Meme Coins\u0027: [\u0027DOGEUSDT\u0027, \u0027SHIBUSDT\u0027, \u0027XRPUSDT\u0027, \u0027FLOUSDT\u0027, \u0027CELOUSDT\u0027, \u0027MATICUSDT\u0027, \u0027AXSUSDT\u0027, \u0027HNTUSDT\u0027],\n        \u0027Stablecoins\u0027: [\u0027USDTUSDT\u0027, \u0027USDCUSDT\u0027, \u0027USDCUSDT\u0027, \u0027DAIUSDT\u0027, \u0027TUSDUSDT\u0027, \u0027USDSUSDT\u0027, \u0027BUSDUSDT\u0027, \u0027PAXUSDT\u0027]\n    }\n\n    # Generate random cryptocurrency tickers\n    crypto_tickers = []\n    for category in cryptos.values():\n        for crypto in category:\n            crypto_tickers.append(crypto)\n    \n    return crypto_tickers\n\n# Define a function to generate foreign exchange tickers\ndef generate_forex_tickers():\n    # Define lists of foreign exchange pairs\n    forex = {\n        \u0027Major Pairs\u0027: [\u0027EURUSD\u0027, \u0027USDJPY\u0027, \u0027USDCHF\u0027, \u0027GBPNZD\u0027, \u0027USDCAD\u0027, \u0027AUDUSD\u0027, \u0027GBPUSD\u0027, \u0027NZDUSD\u0027, \u0027USDCNH\u0027, \u0027EURGBP\u0027],\n        \u0027Emerging Market Pairs\u0027: [\u0027BRLUSD\u0027, \u0027MXNUSD\u0027, \u0027ZARUSD\u0027, \u0027INRUSD\u0027, \u0027PHPUSD\u0027, \u0027TRYUSD\u0027, \u0027RUBUSD\u0027, \u0027MXNPB\u0027, \u0027ARGUSD\u0027, \u0027BRLCHF\u0027],\n        \u0027Cross Rates\u0027: [\u0027EURGBP\u0027, \u0027GBPCHF\u0027, \u0027CHFJPY\u0027, \u0027EURJPY\u0027, \u0027GBPJPY\u0027, \u0027AUDJPY\u0027, \u0027NZDJPY\u0027, \u0027EURAUD\u0027, \u0027EURCAD\u0027, \u0027GBPUSD\u0027]\n    }\n\n    # Generate random foreign exchange tickers\n    forex_tickers = []\n    for category in forex.values():\n        for pair in category:\n            forex_tickers.append(pair)\n    \n    return forex_tickers\n\n# Define a function to generate commodity tickers\ndef generate_commodity_tickers():\n    # Define lists of commodities\n    commodities = {\n        \u0027Metals\u0027: [\u0027GC=F\u0027, \u0027SI=F\u0027, \u0027HG=F\u0027, \u0027PL=F\u0027, \u0027PA=F\u0027, \u0027CU=F\u0027, \u0027SI=F\u0027, \u0027GC=F\u0027, \u0027HG=F\u0027, \u0027PA=F\u0027],\n        \u0027Energy Commodities\u0027: [\u0027CL=F\u0027, \u0027BZ=F",
    "prompt": "**Data Generation**\n\nBelow are 500 examples of natural language text with corresponding tickers for training an embedding model.\n\n### STOCKS\n```markdown\n### Stocks\n#### US Stocks\n#### International Stocks\n#### Dividend Stocks\n#### Growth Stocks\n#### Value Stocks\n#### Index Funds\n#### ETFs\n\n### Stocks with Market Cap\n#### Large-Cap Stocks\n#### Mid-Cap Stocks\n#### Small-Cap Stocks\n\n### Sector Stocks\n#### Technology Stocks\n#### Healthcare Stocks\n#### Finance Stocks\n#### Energy Stocks\n#### Consumer Discretionary Stocks\n\n### Stocks with Market Movement\n#### Stocks with High Movement\n#### Stocks with Low Movement\n\n### Stocks with Industry\n#### Retail Stocks\n#### Industrial Stocks\n#### Consumer Staples Stocks\n#### Utilities Stocks\n\n#### Other Stocks\n```\n\n### CRYPTO\n\n```markdown\n### Cryptocurrencies\n#### Major Cryptocurrencies\n#### Altcoins\n#### Meme Coins\n#### Stablecoins\n\n### Cryptocurrencies with Market Cap\n#### Large-Cap Cryptocurrencies\n#### Mid-Cap Cryptocurrencies\n#### Small-Cap Cryptocurrencies\n\n### Cryptocurrencies with Market Movement\n#### Cryptocurrencies with High Movement\n#### Cryptocurrencies with Low Movement\n\n### Cryptocurrencies with Industry\n#### DeFi Cryptocurrencies\n#### Gaming Cryptocurrencies\n#### NFT Cryptocurrencies\n\n#### Other Cryptocurrencies\n```\n\n### FOREX\n\n```markdown\n### Major Pairs\n#### EURUSD\n#### USDJPY\n#### USDCHF\n#### GBPNZD\n#### USDCAD\n\n### Emerging Market Pairs\n#### BRLUSD\n#### MXNUSD\n#### ZARUSD\n#### INRUSD\n#### PHPUSD\n\n### Cross Rates\n#### EURGBP\n#### GBPCHF\n#### CHFJPY\n#### EURJPY\n#### GBPJPY\n\n### Other Pairs\n```\n\n### COMMODITY\n\n```markdown\n### Metals\n#### Gold\n#### Silver\n#### Copper\n#### Platinum\n#### Palladium\n\n### Energy Commodities\n#### WTI Oil\n#### Brent Oil\n#### Natural Gas\n#### Heating Oil\n#### RBOB Gasoline\n\n### Agricultural Commodities\n#### Wheat\n#### Corn\n#### Soybeans\n#### Soybean Oil\n#### Coffee\n\n### Other Commodities\n```\n\n### CODE\n\n```python\nimport pandas as pd\n\n# Define a function to generate stock tickers\ndef generate_stock_tickers():\n    # Define lists of stocks\n    stocks = {\n        \u0027US Stocks\u0027:",
    "system_prompt": "Generate 500 examples of natural language text and corresponding tickers for training an embedding model, including STOCK, CRYPTO, FOREX, and COMMODITY types, ensuring examples are realistic and varied. "
}
```

This subset can be loaded as:

```python
from datasets import load_dataset

ds = load_dataset("matk9/my-distiset-e66dde51", "default")
```

Or simply as it follows, since there's only one configuration and is named `default`: 

```python
from datasets import load_dataset

ds = load_dataset("matk9/my-distiset-e66dde51")
```


</details>




