Stocks NER 2000 Sample Test Dataset for Named Entity Recognition

This dataset has been automatically processed by AutoTrain for the project stocks-ner-2000-sample-test, and is perfect for training models for Named Entity Recognition (NER) in the stock market domain.

Dataset Description

The dataset includes 2000 samples of stock market related text, with each sample consisting of a sequence of tokens and their corresponding named entity tags. The language of the dataset is English (BCP-47 code: 'en').

Dataset Structure

The dataset is structured as a list of data instances, where each instance includes the following fields:

tokens: a sequence of strings representing the text in the sample.
tags: a sequence of integers representing the named entity tags for each token in the sample. There are a total of 12 named entities in the dataset, including 'NANA', 'btst', 'delivery', 'enter', 'entry_momentum', 'exit', 'exit2', 'exit3', 'intraday', 'sl', 'symbol', and 'touched'.

Each sample in the dataset looks like this:

[
  {
    "tokens": [
      "MAXVIL",
      " : CONVERGENCE OF AVERAGES HAPPENING,  VOLUMES ABOVE AVERAGE  RSI FULLY BREAK OUT "
    ],
    "tags": [
      10,
      0
    ]
  },
  {
    "tokens": [
      "INTRADAY",
      " : BUY ",
      "CAMS",
      " ABOVE ",
      "2625",
      " SL ",
      "2595",
      " TARGET ",
      "2650",
      " - ",
      "2675",
      " - ",
      "2700",
      " "
    ],
    "tags": [
      8,
      0,
      10,
      0,
      3,
      0,
      9,
      0,
      5,
      0,
      6,
      0,
      7,
      0
    ]
  }
]

Dataset Splits

The dataset is split into a train and validation split, with 1261 samples in the train split and 480 samples in the validation split.

This dataset is designed to train models for Named Entity Recognition in the stock market domain and can be used for natural language processing (NLP) research and development. Download this dataset now and take the first step towards building your own state-of-the-art NER model for stock market text.