metadata

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

What this repository contains? :

Label data using LabelStudio NER(Named Entity Recognition or Token Classification) tool. convert to
Convert LabelStudio CSV or JSON to HuggingFace-autoTrain dataset conversion script
Train NER model on Hugginface-autoTrain.
Use Hugginface-autoTrain model to predict labels on new data in LabelStudio using LabelStudio-ML-Backend.
Define python function to predict labels using Hugginface-autoTrain model.
Only label new data from newly predicted-labels-dataset that has falsified labels.
Backtest Truely labelled dataset against real historical data of the stock using zerodha kiteconnect and jugaad_trader.
Evaluate total gained percentage since inception summation-wise and compounded and plot.
Listen to telegram channel for new LIVE messages using telegram API for algotrading.
Serve the app as flask web API for web request and respond to it as labelled tokens.
Outperforming or underperforming results of the telegram channel tips against exchange index by percentage.