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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


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

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

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Dataset used to train hemangjoshi37a/autotrain-ratnakar_1000_sample_curated-1474454086