finacial-pharse-bank's Introduction

finacial-pharse-bank

Set up

conda create -n env2 python=3.11
conda activate env2
conda install pytorch==2.1.2 cpuonly -c pytorch


git clone https://github.com/giriprasad51/finacial-pharse-bank.git

cd finacial-pharse-bank
pip install ./requirements.txt

Start application

python app.py

Fine Turing

https://www.kaggle.com/code/giriprasad512/financial-1

financial-training.ipynb

Loss function

Predict - GroundTruth

positive - negative    less     100
negative - positive    less     100
neutral - negative     less      100 client who holds stock will 

neutral - positive     0         40     no loss just for company growth
positive - neutral     0         10	client who buys wont loss 
negative - neutral     0         10	client just sell the stock

positive - positive  more       -10
negative - negative  more       -10
neutral - neutral    more       -10

acc1 = (positive-positive + negative-negative + neutral-neutral)/(positive-positive + negative-negative + neutral-neutral + positive-negative + negative-positive + neutral-negative + neutral-positive)

precision = TP/(TP+FP)
Recall = TP/(TP+FN)
f1 score = 2*PR/(P+R)

Request

POST /thing/

curl -X POST http://127.0.0.1:5000/api/data \
 -H "Content-Type: application/json" \
 -d '{
       "article": "According to the company 's updated strategy for the years 2009-2012, Basware targets a long-term net sales growth in the range of 20% -40% with an operating profit margin of 10% -20% of net sales"
     }'

Response

127.0.0.1 - - [02/Aug/2024 17:28:09] "POST /api/data HTTP/1.1" 201 -
['negative']

{
    "message": "Data received successfully",
    "received_data": [
        "negative"
    ]
}