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""
"AutoTrain generated dataset"
{ "tokens": { "feature": { "dtype": "string", "id": null, "_type": "Value" }, "length": -1, "id": null, "_type": "Sequence" }, "tags": { "feature": { "num_classes": 12, "names": [ "NANA", "btst", "delivery", "enter", "entry_momentum", "exit", "exit2", "exit3", "intraday", "sl", "symbol", "touched" ], "id": null, "_type": "ClassLabel" }, "length": -1, "id": null, "_type": "Sequence" } }
""
""
{ "train": { "name": "train", "num_bytes": 98510, "num_examples": 726, "dataset_name": null } }
[ { "filename": "dataset.arrow" } ]
"aa6a064e24659c49"
[ "tags", "tokens" ]
{}
{}
false

AutoTrain Dataset for project: ratnakar_1000_sample_curated

Dataset Description

This dataset has been automatically processed by AutoTrain for project ratnakar_1000_sample_curated.

Languages

The BCP-47 code for the dataset's language is en.

Dataset Structure

Data Instances

A sample from this dataset looks as follows:

[
  {
    "tokens": [
      "INTRADAY",
      "NAHARINDUS",
      " ABOVE ",
      "128",
      " - 129 SL ",
      "126",
      " TARGET ",
      "140",
      " "
    ],
    "tags": [
      8,
      10,
      0,
      3,
      0,
      9,
      0,
      5,
      0
    ]
  },
  {
    "tokens": [
      "INTRADAY",
      "ASTRON",
      " ABV ",
      "39",
      " SL ",
      "37.50",
      " TARGET ",
      "45",
      " "
    ],
    "tags": [
      8,
      10,
      0,
      3,
      0,
      9,
      0,
      5,
      0
    ]
  }
]

Dataset Fields

The dataset has the following fields (also called "features"):

{
  "tokens": "Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)",
  "tags": "Sequence(feature=ClassLabel(num_classes=12, names=['NANA', 'btst', 'delivery', 'enter', 'entry_momentum', 'exit', 'exit2', 'exit3', 'intraday', 'sl', 'symbol', 'touched'], id=None), length=-1, id=None)"
}

Dataset Splits

This dataset is split into a train and validation split. The split sizes are as follow:

Split name Num samples
train 726
valid 259

GitHub Link to this project : Telegram Trade Msg Backtest ML

Need custom model for your application? : Place a order on hjLabs.in : Custom Token Classification or Named Entity Recognition (NER) model as in Natural Language Processing (NLP) Machine Learning

What this repository contains? :

  1. Label data using LabelStudio NER(Named Entity Recognition or Token Classification) tool. Screenshot from 2022-09-30 12-28-50 convert to Screenshot from 2022-09-30 18-59-14

  2. Convert LabelStudio CSV or JSON to HuggingFace-autoTrain dataset conversion script Screenshot from 2022-10-01 10-36-03

  3. Train NER model on Hugginface-autoTrain. Screenshot from 2022-10-01 10-38-24

  4. Use Hugginface-autoTrain model to predict labels on new data in LabelStudio using LabelStudio-ML-Backend. Screenshot from 2022-10-01 10-41-07 Screenshot from 2022-10-01 10-42-36 Screenshot from 2022-10-01 10-44-56

  5. Define python function to predict labels using Hugginface-autoTrain model. Screenshot from 2022-10-01 10-47-08 Screenshot from 2022-10-01 10-47-25

  6. Only label new data from newly predicted-labels-dataset that has falsified labels. Screenshot from 2022-09-30 22-47-23

  7. Backtest Truely labelled dataset against real historical data of the stock using zerodha kiteconnect and jugaad_trader. Screenshot from 2022-10-01 00-05-55

  8. Evaluate total gained percentage since inception summation-wise and compounded and plot. Screenshot from 2022-10-01 00-06-59

  9. Listen to telegram channel for new LIVE messages using telegram API for algotrading. Screenshot from 2022-10-01 00-09-29

  10. Serve the app as flask web API for web request and respond to it as labelled tokens. Screenshot from 2022-10-01 00-12-12

  11. Outperforming or underperforming results of the telegram channel tips against exchange index by percentage. Screenshot from 2022-10-01 11-16-27

Place a custom order on hjLabs.in : https://hjLabs.in


Contact us

Mobile : +917016525813 Whatsapp & Telegram : +919409077371

Email : hemangjoshi37a@gmail.com

Place a custom order on hjLabs.in : https://hjLabs.in

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