engibeer/financial-ner-entities-bist30

🏷️ Financial NER Model for Entity Extraction

This is a fine-tuned BERT-based token classification model that extracts structured financial entities from Turkish tweets related to the stock market.

Entities include:

• 🏢 Stock Tickers (e.g., AKBNK, EREGL)
• 🎯 Target Prices (Hedef)
• 📉 Support Prices (Destek)
• 📈 Resistance Prices (Direnç)
• ⏱ Time spans (Vade)
• 📊 Predicted values (Tahmin)

It is a core part of a larger NLP pipeline for parsing and analyzing finance-related predictions on social media.

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🧠 Model Details

• Developed by: damlakonur
• Model type: BERT fine-tuned for token-classification
• Language(s): Turkish
• Finetuned from: bert-base-cased
• Trained using: Hugging Face Trainer API
• License: MIT

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🚀 How to Use

from transformers import pipeline

model = pipeline(
    "token-classification",
    model="your-username/financial-ner-entities-bist30",
    aggregation_strategy="simple"
)

text = "#AKBNK hedef 60 TL, destek 52 TL."
output = model(text)
print(output)