readme: ---
  tags:
  - autotrain
  - token-classification
  language:
  - en
  widget:
  - text: "I love AutoTrain 🤗"
  datasets:
  - hemangjoshi37a/autotrain-data-ratnakar_1000_sample_curated
  co2_eq_emissions:
    emissions: 2.1802563684907916
  ---
  
  # Model Trained Using AutoTrain
  
  - Problem type: Entity Extraction
  - Model ID: 1474454086
  - CO2 Emissions (in grams): 2.1803
  
  ## Validation Metrics
  
  - Loss: 0.177
  - Accuracy: 0.957
  - Precision: 0.839
  - Recall: 0.888
  - F1: 0.863
  
  ## Usage
  
  You can use cURL to access this model:
  
  ```
  $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/hemangjoshi37a/autotrain-ratnakar_1000_sample_curated-1474454086
  ```
  
  Or Python API:
  
  ```
  from transformers import AutoModelForTokenClassification, AutoTokenizer
  
  model = AutoModelForTokenClassification.from_pretrained("hemangjoshi37a/autotrain-ratnakar_1000_sample_curated-1474454086", use_auth_token=True)
  
  tokenizer = AutoTokenizer.from_pretrained("hemangjoshi37a/autotrain-ratnakar_1000_sample_curated-1474454086", use_auth_token=True)
  
  inputs = tokenizer("I love AutoTrain", return_tensors="pt")
  
  outputs = model(**inputs)
  ```
  
  
  # 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)
  
  
  
  Place a custom order on hjLabs.in : [https://hjLabs.in](https://hjlabs.in/?product=custom-algotrading-softwa