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---
language:
- en
---
# 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:
```json
[
{
"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"):
```json
{
"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](https://github.com/hemangjoshi37a/TelegramTradeMsgBacktestML)
# 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](https://hjlabs.in/product/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.
 convert to 
2. Convert LabelStudio CSV or JSON to HuggingFace-autoTrain dataset conversion script
3. Train NER model on Hugginface-autoTrain.
4. Use Hugginface-autoTrain model to predict labels on new data in LabelStudio using LabelStudio-ML-Backend.
5. Define python function to predict labels using Hugginface-autoTrain model.
6. Only label new data from newly predicted-labels-dataset that has falsified labels.
7. Backtest Truely labelled dataset against real historical data of the stock using zerodha kiteconnect and jugaad_trader.
8. Evaluate total gained percentage since inception summation-wise and compounded and plot.
9. Listen to telegram channel for new LIVE messages using telegram API for algotrading.
10. Serve the app as flask web API for web request and respond to it as labelled tokens.
11. Outperforming or underperforming results of the telegram channel tips against exchange index by percentage.
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----------------------------------------------------------------------
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Place a custom order on hjLabs.in : [https://hjLabs.in](https://hjlabs.in/)
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- [hemangjoshi37a/autotrain-ratnakar_1000_sample_curated-1474454086 : Stock tip message NER(Named Entity Recognition or Token Classification) using HUggingFace-AutoTrain and LabelStudio and Ratnakar Securities Pvt. Ltd.](https://huggingface.co/hemangjoshi37a/autotrain-ratnakar_1000_sample_curated-1474454086)
## Our HuggingFace Datasets :
- [hemangjoshi37a/autotrain-data-ratnakar_1000_sample_curated : Stock tip message NER(Named Entity Recognition or Token Classification) using HUggingFace-AutoTrain and LabelStudio and Ratnakar Securities Pvt. Ltd.](https://huggingface.co/datasets/hemangjoshi37a/autotrain-data-ratnakar_1000_sample_curated)
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