pszemraj/deberta-v3-small-sp500-edgar-10k

import json
from transformers import pipeline
from huggingface_hub import hf_hub_download

model_repo_name = "pszemraj/deberta-v3-small-sp500-edgar-10k"
pipe = pipeline("text-classification", model=model_repo_name)
pipe.tokenizer.model_max_length = 1024

# Download the regression_config.json file
regression_config_path = hf_hub_download(
    repo_id=model_repo_name, filename="regression_config.json"
)
with open(regression_config_path, "r") as f:
    regression_config = json.load(f)

def inverse_scale(prediction, config):
    """apply inverse scaling to a prediction"""
    min_value, max_value = config["min_value"], config["max_value"]
    return prediction * (max_value - min_value) + min_value

def predict_with_pipeline(text, pipe, config, ndigits=5):
    result = pipe(text, truncation=True)[0] 
    scaled_score = inverse_scale(result['score'], config)
    return round(scaled_score, ndigits)

text = "This is an example text for regression prediction."

# Get predictions
predictions = predict_with_pipeline(text, pipe, regression_config)
print("Predicted Value:", predictions)