# 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.

# 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.
 ![Screenshot from 2022-09-30 12-28-50](https://user-images.githubusercontent.com/12392345/193394190-3ad215d1-3205-4af3-949e-6d95cf866c6c.png) convert to  ![Screenshot from 2022-09-30 18-59-14](https://user-images.githubusercontent.com/12392345/193394213-9bb936e7-34ea-4cbc-9132-80c7e5a006d7.png)

2. Convert LabelStudio CSV or JSON to HuggingFace-autoTrain dataset conversion script
![Screenshot from 2022-10-01 10-36-03](https://user-images.githubusercontent.com/12392345/193394227-32e293d4-6736-4e71-b687-b0c2fcad732c.png)

3. Train NER model on Hugginface-autoTrain.
 ![Screenshot from 2022-10-01 10-38-24](https://user-images.githubusercontent.com/12392345/193394247-bf51da86-45bb-41b4-b4da-3de86014e6a5.png)

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](https://user-images.githubusercontent.com/12392345/193394251-bfba07d4-c56b-4fe8-ba7f-08a1c69f0e2c.png)
 ![Screenshot from 2022-10-01 10-42-36](https://user-images.githubusercontent.com/12392345/193394261-df4bc8f8-9ffd-4819-ba26-04fddbba8e7b.png)
 ![Screenshot from 2022-10-01 10-44-56](https://user-images.githubusercontent.com/12392345/193394267-c5a111c3-8d00-4d6f-b3c6-0ea82e4ac474.png)

5. Define python function to predict labels using Hugginface-autoTrain model.
 ![Screenshot from 2022-10-01 10-47-08](https://user-images.githubusercontent.com/12392345/193394278-81389606-f690-454a-bb2b-ef3f1db39571.png)
![Screenshot from 2022-10-01 10-47-25](https://user-images.githubusercontent.com/12392345/193394288-27a0c250-41af-48b1-9c57-c146dc51da1d.png)

6. Only label new data from newly predicted-labels-dataset that has falsified labels.
 ![Screenshot from 2022-09-30 22-47-23](https://user-images.githubusercontent.com/12392345/193394294-fdfaf40a-c9cd-4c2d-836e-1878b503a668.png)

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](https://user-images.githubusercontent.com/12392345/193394303-137c2a2a-3341-4be3-8ece-5191669ec53a.png)

8. Evaluate total gained percentage since inception summation-wise and compounded and plot.
 ![Screenshot from 2022-10-01 00-06-59](https://user-images.githubusercontent.com/12392345/193394308-446eddd9-c5d1-47e3-a231-9edc620284bb.png)

9. Listen to telegram channel for new LIVE messages using telegram API for algotrading.
 ![Screenshot from 2022-10-01 00-09-29](https://user-images.githubusercontent.com/12392345/193394319-8cc915b7-216e-4e05-a7bf-28360b17de99.png)

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](https://user-images.githubusercontent.com/12392345/193394323-822c2a59-ca72-45b1-abca-a6e5df3364b0.png)

11. Outperforming or underperforming results of the telegram channel tips against exchange index by percentage.
 ![Screenshot from 2022-10-01 11-16-27](https://user-images.githubusercontent.com/12392345/193394685-53235198-04f8-4d3c-a341-535dd9093252.png)



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## Some Cool Arduino and ESP8266 (or NodeMCU) IoT projects:
- [IoT_LED_over_ESP8266_NodeMCU : Turn LED on and off using web server hosted on a nodemcu or esp8266](https://github.com/hemangjoshi37a/my_Arduino/tree/master/IoT_LED_over_ESP8266_NodeMCU)
- [ESP8266_NodeMCU_BasicOTA : Simple OTA (Over The Air) upload code from Arduino IDE using WiFi to NodeMCU or ESP8266](https://github.com/hemangjoshi37a/my_Arduino/tree/master/ESP8266_NodeMCU_BasicOTA)  
- [IoT_CSV_SD : Read analog value of Voltage and Current and write it to SD Card in CSV format for Arduino, ESP8266, NodeMCU etc](https://github.com/hemangjoshi37a/my_Arduino/tree/master/IoT_CSV_SD)  
- [Honeywell_I2C_Datalogger : Log data in A SD Card from a Honeywell I2C HIH8000 or HIH6000 series sensor having external I2C RTC clock](https://github.com/hemangjoshi37a/my_Arduino/tree/master/Honeywell_I2C_Datalogger)
- [IoT_Load_Cell_using_ESP8266_NodeMC : Read ADC value from High Precision 12bit ADS1015 ADC Sensor and Display on SSD1306 SPI Display as progress bar for Arduino or ESP8266 or NodeMCU](https://github.com/hemangjoshi37a/my_Arduino/tree/master/IoT_Load_Cell_using_ESP8266_NodeMC)
- [IoT_SSD1306_ESP8266_NodeMCU : Read from High Precision 12bit ADC seonsor ADS1015 and display to SSD1306 SPI as progress bar in ESP8266 or NodeMCU or Arduino](https://github.com/hemangjoshi37a/my_Arduino/tree/master/IoT_SSD1306_ESP8266_NodeMCU)  


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## 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)

## We sell Gigs on Fiverr : 
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region:us
{}
TAGS
#region-us 

# 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:


## 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.

# GitHub Link to this project : Telegram Trade Msg Backtest ML

# Need custom model for your application? : Place a order on URL : 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 URL : URL


----------------------------------------------------------------------

### Social Media :
* WhatsApp/917016525813
* telegram/hjlabs 
* Gmail/hemangjoshi37a@URL
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* Tumblr/hemangjoshi37a-blog
* Pinterest/hemangjoshi37a
* Blogger/hemangjoshi
* Instagram/hemangjoshi37

----------------------------------------------------------------------

### Checkout Our Other Repositories

- pyPortMan
- transformers_stock_prediction
- TrendMaster
- hjAlgos_notebooks
- AutoCut
- My_Projects
- Cool Arduino and ESP8266 or NodeMCU Projects
- Telegram Trade Msg Backtest ML

### Checkout Our Other Products

- WiFi IoT LED Matrix Display
- SWiBoard WiFi Switch Board IoT Device
- Electric Bicycle
- Product 3D Design Service with Solidworks
- AutoCut : Automatic Wire Cutter Machine
- Custom AlgoTrading Software Coding Services
- SWiBoard :Tasmota MQTT Control App
- Custom Token Classification or Named Entity Recognition (NER) model as in Natural Language Processing (NLP) Machine Learning

## Some Cool Arduino and ESP8266 (or NodeMCU) IoT projects:
- IoT_LED_over_ESP8266_NodeMCU : Turn LED on and off using web server hosted on a nodemcu or esp8266
- ESP8266_NodeMCU_BasicOTA : Simple OTA (Over The Air) upload code from Arduino IDE using WiFi to NodeMCU or ESP8266  
- IoT_CSV_SD : Read analog value of Voltage and Current and write it to SD Card in CSV format for Arduino, ESP8266, NodeMCU etc  
- Honeywell_I2C_Datalogger : Log data in A SD Card from a Honeywell I2C HIH8000 or HIH6000 series sensor having external I2C RTC clock
- IoT_Load_Cell_using_ESP8266_NodeMC : Read ADC value from High Precision 12bit ADS1015 ADC Sensor and Display on SSD1306 SPI Display as progress bar for Arduino or ESP8266 or NodeMCU
- IoT_SSD1306_ESP8266_NodeMCU : Read from High Precision 12bit ADC seonsor ADS1015 and display to SSD1306 SPI as progress bar in ESP8266 or NodeMCU or Arduino  


## Checkout Our Awesome 3D GrabCAD Models:
- AutoCut : Automatic Wire Cutter Machine
- ESP Matrix Display 5mm Acrylic Box
- Arcylic Bending Machine w/ Hot Air Gun
- Automatic Wire Cutter/Stripper

## Our HuggingFace Models :
- 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.

## 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.

## We sell Gigs on Fiverr : 
- code android and ios app for you using flutter firebase software stack
- code custom algotrading software for zerodha or angel broking

# 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: ## 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. # GitHub Link to this project : Telegram Trade Msg Backtest ML # Need custom model for your application? : Place a order on URL : 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 URL : URL


---------------------------------------------------------------------- ### Social Media :
* WhatsApp/917016525813
* telegram/hjlabs 
* Gmail/hemangjoshi37a@URL
* Facebook/hemangjoshi37
* Twitter/HemangJ81509525
* LinkedIn/hemang-joshi-046746aa
* Tumblr/hemangjoshi37a-blog
* Pinterest/hemangjoshi37a
* Blogger/hemangjoshi
* Instagram/hemangjoshi37

---------------------------------------------------------------------- ### Checkout Our Other Repositories

- pyPortMan
- transformers_stock_prediction
- TrendMaster
- hjAlgos_notebooks
- AutoCut
- My_Projects
- Cool Arduino and ESP8266 or NodeMCU Projects
- Telegram Trade Msg Backtest ML ### Checkout Our Other Products

- WiFi IoT LED Matrix Display
- SWiBoard WiFi Switch Board IoT Device
- Electric Bicycle
- Product 3D Design Service with Solidworks
- AutoCut : Automatic Wire Cutter Machine
- Custom AlgoTrading Software Coding Services
- SWiBoard :Tasmota MQTT Control App
- Custom Token Classification or Named Entity Recognition (NER) model as in Natural Language Processing (NLP) Machine Learning ## Some Cool Arduino and ESP8266 (or NodeMCU) IoT projects:
- IoT_LED_over_ESP8266_NodeMCU : Turn LED on and off using web server hosted on a nodemcu or esp8266
- ESP8266_NodeMCU_BasicOTA : Simple OTA (Over The Air) upload code from Arduino IDE using WiFi to NodeMCU or ESP8266  
- IoT_CSV_SD : Read analog value of Voltage and Current and write it to SD Card in CSV format for Arduino, ESP8266, NodeMCU etc  
- Honeywell_I2C_Datalogger : Log data in A SD Card from a Honeywell I2C HIH8000 or HIH6000 series sensor having external I2C RTC clock
- IoT_Load_Cell_using_ESP8266_NodeMC : Read ADC value from High Precision 12bit ADS1015 ADC Sensor and Display on SSD1306 SPI Display as progress bar for Arduino or ESP8266 or NodeMCU
- IoT_SSD1306_ESP8266_NodeMCU : Read from High Precision 12bit ADC seonsor ADS1015 and display to SSD1306 SPI as progress bar in ESP8266 or NodeMCU or Arduino ## Checkout Our Awesome 3D GrabCAD Models:
- AutoCut : Automatic Wire Cutter Machine
- ESP Matrix Display 5mm Acrylic Box
- Arcylic Bending Machine w/ Hot Air Gun
- Automatic Wire Cutter/Stripper ## Our HuggingFace Models :
- 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. ## 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. ## We sell Gigs on Fiverr : 
- code android and ios app for you using flutter firebase software stack
- code custom algotrading software for zerodha or angel broking
TAGS
#region-us 
 # 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: ## 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. # GitHub Link to this project : Telegram Trade Msg Backtest ML # Need custom model for your application? : Place a order on URL : 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 URL : URL


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- pyPortMan
- transformers_stock_prediction
- TrendMaster
- hjAlgos_notebooks
- AutoCut
- My_Projects
- Cool Arduino and ESP8266 or NodeMCU Projects
- Telegram Trade Msg Backtest ML ### Checkout Our Other Products

- WiFi IoT LED Matrix Display
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- AutoCut : Automatic Wire Cutter Machine
- Custom AlgoTrading Software Coding Services
- SWiBoard :Tasmota MQTT Control App
- Custom Token Classification or Named Entity Recognition (NER) model as in Natural Language Processing (NLP) Machine Learning ## Some Cool Arduino and ESP8266 (or NodeMCU) IoT projects:
- IoT_LED_over_ESP8266_NodeMCU : Turn LED on and off using web server hosted on a nodemcu or esp8266
- ESP8266_NodeMCU_BasicOTA : Simple OTA (Over The Air) upload code from Arduino IDE using WiFi to NodeMCU or ESP8266  
- IoT_CSV_SD : Read analog value of Voltage and Current and write it to SD Card in CSV format for Arduino, ESP8266, NodeMCU etc  
- Honeywell_I2C_Datalogger : Log data in A SD Card from a Honeywell I2C HIH8000 or HIH6000 series sensor having external I2C RTC clock
- IoT_Load_Cell_using_ESP8266_NodeMC : Read ADC value from High Precision 12bit ADS1015 ADC Sensor and Display on SSD1306 SPI Display as progress bar for Arduino or ESP8266 or NodeMCU
- IoT_SSD1306_ESP8266_NodeMCU : Read from High Precision 12bit ADC seonsor ADS1015 and display to SSD1306 SPI as progress bar in ESP8266 or NodeMCU or Arduino ## Checkout Our Awesome 3D GrabCAD Models:
- AutoCut : Automatic Wire Cutter Machine
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- Arcylic Bending Machine w/ Hot Air Gun
- Automatic Wire Cutter/Stripper ## Our HuggingFace Models :
- 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. ## 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. ## We sell Gigs on Fiverr : 
- code android and ios app for you using flutter firebase software stack
- code custom algotrading software for zerodha or angel broking
passage: TAGS
#region-us 
# 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:## 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.# GitHub Link to this project : Telegram Trade Msg Backtest ML# Need custom model for your application? : Place a order on URL : Custom Token Classification or Named Entity Recognition (NER) model as in Natural Language Processing (NLP) Machine Learning passage: ## 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 URL : URL


----------------------------------------------------------------------### Social Media :
* WhatsApp/917016525813
* telegram/hjlabs 
* Gmail/hemangjoshi37a@URL
* Facebook/hemangjoshi37
* Twitter/HemangJ81509525
* LinkedIn/hemang-joshi-046746aa
* Tumblr/hemangjoshi37a-blog
* Pinterest/hemangjoshi37a
* Blogger/hemangjoshi
* Instagram/hemangjoshi37

----------------------------------------------------------------------### Checkout Our Other Repositories

- pyPortMan
- transformers_stock_prediction
- TrendMaster
- hjAlgos_notebooks
- AutoCut
- My_Projects
- Cool Arduino and ESP8266 or NodeMCU Projects
- Telegram Trade Msg Backtest ML### Checkout Our Other Products

- WiFi IoT LED Matrix Display
- SWiBoard WiFi Switch Board IoT Device
- Electric Bicycle
- Product 3D Design Service with Solidworks
- AutoCut : Automatic Wire Cutter Machine
- Custom AlgoTrading Software Coding Services
- SWiBoard :Tasmota MQTT Control App
- Custom Token Classification or Named Entity Recognition (NER) model as in Natural Language Processing (NLP) Machine Learning