Text Classification
Transformers
Safetensors
bert
finance
finbert
market
financial
Generated from Trainer
stocks
sentiment
text-embeddings-inference
Instructions to use baptle/FinBERT_market_based with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use baptle/FinBERT_market_based with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="baptle/FinBERT_market_based")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("baptle/FinBERT_market_based") model = AutoModelForSequenceClassification.from_pretrained("baptle/FinBERT_market_based") - Notebooks
- Google Colab
- Kaggle
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README.md
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Our FinBERT model, finetuned on impactful news headlines about global equity markets, has shown significant performance improvements over standard models.
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Its training on real-world market impact rather than subjective financial expert opinions sets a new standard for unbiased financial sentiment analysis. 📈
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**Outperforms FinBERT**
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- 🎯 +25% precision
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Our FinBERT model, finetuned on impactful news headlines about global equity markets, has shown significant performance improvements over standard models.
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Its training on real-world market impact rather than subjective financial expert opinions sets a new standard for unbiased financial sentiment analysis. 📈
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The dataset is uploaded on HuggingFace [here](https://huggingface.co/datasets/baptle/financial_headlines_market_based).
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**Outperforms FinBERT**
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- 🎯 +25% precision
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