predict.py

from finbert.finbert import predict
from pytorch_pretrained_bert.modeling import BertForSequenceClassification
import argparse
from pathlib import Path
import datetime
import os

parser = argparse.ArgumentParser(description='Sentiment analyzer')

parser.add_argument('-a', action="store_true", default=False)

parser.add_argument('--text_path', type=str, help='Path to the text file.')
parser.add_argument('--output_dir', type=str, help='Where to write the results')
parser.add_argument('--model_path', type=str, help='Path to classifier model')

args = parser.parse_args()

if not os.path.exists(args.output_dir):
    os.mkdir(args.output_dir)


with open(args.text_path,'r') as f:
    text = f.read()

model = BertForSequenceClassification.from_pretrained(args.model_path,num_labels=3,cache_dir=None)
#now = datetime.datetime.now().strftime("predictions_%B-%d-%Y-%I:%M.csv")
output = Path(args.text_path).stem + '_predictions.csv'
predict(text,model,write_to_csv=True,path=os.path.join(args.output_dir,output))