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
Update README.md
Browse files
README.md
CHANGED
|
@@ -33,6 +33,8 @@ datasets:
|
|
| 33 |
|
| 34 |
# Model Card for Finetuned FinBERT on Market-Based Facts
|
| 35 |
|
|
|
|
|
|
|
| 36 |
Our FinBERT model, finetuned on impactful news headlines about global equity markets, has shown significant performance improvements over standard models.
|
| 37 |
Its training on real-world market impact rather than subjective financial expert opinions sets a new standard for unbiased financial sentiment analysis. 📈
|
| 38 |
|
|
|
|
| 33 |
|
| 34 |
# Model Card for Finetuned FinBERT on Market-Based Facts
|
| 35 |
|
| 36 |
+
**<font color="orange">This LLM is fine-tuned on market reactions to events. By utilizing market-based data, it avoids human biases present in traditional annotation methods.</font>**
|
| 37 |
+
|
| 38 |
Our FinBERT model, finetuned on impactful news headlines about global equity markets, has shown significant performance improvements over standard models.
|
| 39 |
Its training on real-world market impact rather than subjective financial expert opinions sets a new standard for unbiased financial sentiment analysis. 📈
|
| 40 |
|