metadata
language: en
tags:
- sentiment-analysis
base_model:
- ProsusAI/finbert
Model Description
This is a fine-tuned version of ProsusAI/finbert for cryptocurrency news sentiment analysis. The model classifies text into three sentiment categories: negative, neutral, and positive.
Key Features
- Base Model: ProsusAI/finbert
- Task: Sentiment Classification (3 classes)
- Domain: Cryptocurrency news and social media
- Custom Tokens: 520 crypto-specific tokens added to vocabulary
Usage
import torch
from transformers import BertForSequenceClassification, AutoTokenizer
from torch.nn import functional as F
tokenizer = AutoTokenizer.from_pretrained('houmanrajabi/CoinPulse')
model = BertForSequenceClassification.from_pretrained('houmanrajabi/CoinPulse')
model.eval()
def predict_sentiment(text, temperature=2.0):
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
with torch.no_grad():
outputs = model(**inputs)
label_map = {0: 'negative', 1: 'neutral', 2: 'positive'}
logits = outputs.logits / temperature
predicted_class_id = logits.argmax().item()
confidence = F.softmax(logits, dim=1)[0, predicted_class_id].item()
return label_map[predicted_class_id].capitalize() , confidence
sample_texts = [
"The company reported record profits and exceeded all expectations.",
"Stock prices plummeted after the disappointing earnings report.",
"The quarterly results were in line with market forecasts."
]
for i, text in enumerate(sample_texts):
sentiment, confidence = predict_sentiment(text)
print(f"{i+1}) {text}\nSentiment: {sentiment}\nConfidence: {round(confidence,2)}\n")