Edit model card

Model Trained Using AutoTrain

  • Problem type: Entity Extraction
  • Model ID: 1676759313
  • CO2 Emissions (in grams): 0.0110

Validation Metrics

  • Loss: 0.097
  • Accuracy: 0.973
  • Precision: 0.903
  • Recall: 0.921
  • F1: 0.912

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-stocks-ner-2000-sample-test-1676759313

Or Python API:

from transformers import AutoModelForTokenClassification, AutoTokenizer

model = AutoModelForTokenClassification.from_pretrained("hemangjoshi37a/autotrain-stocks-ner-2000-sample-test-1676759313", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("hemangjoshi37a/autotrain-stocks-ner-2000-sample-test-1676759313", 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

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


Social Media :

Checkout Our Other Repositories

Checkout Our Other Products

Some Cool Arduino and ESP8266 (or NodeMCU) IoT projects:

Checkout Our Awesome 3D GrabCAD Models:

Our HuggingFace Models :

Our HuggingFace Datasets :

We sell Gigs on Fiverr :

Awesome Fiverr. Gigs:

Downloads last month
4
Inference Examples
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.

Dataset used to train hemangjoshi37a/autotrain-stocks-ner-2000-sample-test-1676759313