Lead with model information, move license terms to the end

README.md

@@ -10,15 +10,31 @@ widget:
 
 # FinBERT
 
-## 📜 License and Usage Terms
+FinBERT is a pre-trained NLP model to analyze sentiment of financial text. It is built by further training the BERT language model in the finance domain, using a large financial corpus and thereby fine-tuning it for financial sentiment classification. [Financial PhraseBank](https://www.researchgate.net/publication/251231107_Good_Debt_or_Bad_Debt_Detecting_Semantic_Orientations_in_Economic_Texts) by Malo et al. (2014) is used for fine-tuning. For more details, please see the paper [FinBERT: Financial Sentiment Analysis with Pre-trained Language Models](https://arxiv.org/abs/1908.10063) and our related [blog post](https://medium.com/prosus-ai-tech-blog/finbert-financial-sentiment-analysis-with-bert-b277a3607101) on Medium.
 
-### Model Weights License
-The FinBERT model weights are made available under **CC BY-NC-SA 4.0** 
-(Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International).
+The model will give softmax outputs for three labels: positive, negative or neutral.
 
-**This license restricts commercial use.** You may use this model for 
-non-commercial purposes only, and any adaptations must be shared under 
-the same license.
+## How to use
+
+```python
+from transformers import pipeline
+
+classifier = pipeline("text-classification", model="ProsusAI/finbert")
+classifier("Stocks rallied and the British pound gained.")
+# [{'label': 'positive', 'score': 0.898...}]
+```
+
+To get the scores for all three labels, pass `top_k=None` to the classifier.
+
+## Model details
+
+- **Architecture**: BERT base (12 layers, hidden size 768, 12 attention heads) with a sequence classification head
+- **Labels**: `0` positive, `1` negative, `2` neutral
+- **Language**: English
+- **Frameworks**: weights are available for PyTorch, TensorFlow and JAX/Flax
+- **Code**: training and prediction code is on [GitHub](https://github.com/ProsusAI/finBERT)
+
+## Training data
 
 ### Training Data Attribution
 This model was trained using the **Financial PhraseBank** dataset:
@@ -33,6 +49,42 @@ The Financial PhraseBank dataset is licensed under **CC BY-NC-SA 3.0**.
 The underlying language model was further pre-trained on a subset of the 
 Reuters TRC2 corpus, which is distributed by NIST under its own access terms.
 
+## Citation
+
+```bibtex
+@article{araci2019finbert,
+  title={FinBERT: Financial Sentiment Analysis with Pre-trained Language Models},
+  author={Araci, Dogu},
+  journal={arXiv preprint arXiv:1908.10063},
+  year={2019}
+}
+```
+
+## About Prosus
+
+Prosus is a global consumer internet group and one of the largest technology investors in the world. Operating and investing globally in markets with long-term growth potential, Prosus builds leading consumer internet companies that empower people and enrich communities. For more information, please visit www.prosus.com.
+
+## 📧 Contact
+
+For questions about this research project:
+- Dogu Araci: dogu.araci[at]prosus.com
+- Zulkuf Genc: zulkuf.genc[at]prosus.com
+
+**Note**: Questions about commercial licensing or legal permissions 
+cannot be addressed by the research team. See the License section below.
+
+---
+
+## 📜 License and Usage Terms
+
+### Model Weights License
+The FinBERT model weights are made available under **CC BY-NC-SA 4.0** 
+(Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International).
+
+**This license restricts commercial use.** You may use this model for 
+non-commercial purposes only, and any adaptations must be shared under 
+the same license.
+
 ## ⚠️ User Responsibilities
 
 **You are solely responsible for**:
@@ -58,24 +110,3 @@ for this model.
 
 **LIABILITY**: In no event shall MIH AI B.V. or Prosus be liable for any 
 claim, damages, or other liability arising from the use of this model.
-
-## Model Description
-
-FinBERT is a pre-trained NLP model to analyze sentiment of financial text. It is built by further training the BERT language model in the finance domain, using a large financial corpus and thereby fine-tuning it for financial sentiment classification. [Financial PhraseBank](https://www.researchgate.net/publication/251231107_Good_Debt_or_Bad_Debt_Detecting_Semantic_Orientations_in_Economic_Texts) by Malo et al. (2014) is used for fine-tuning. For more details, please see the paper [FinBERT: Financial Sentiment Analysis with Pre-trained Language Models](https://arxiv.org/abs/1908.10063) and our related [blog post](https://medium.com/prosus-ai-tech-blog/finbert-financial-sentiment-analysis-with-bert-b277a3607101) on Medium.
-
-The model will give softmax outputs for three labels: positive, negative or neutral.
-
----
-
-About Prosus
-
-Prosus is a global consumer internet group and one of the largest technology investors in the world. Operating and investing globally in markets with long-term growth potential, Prosus builds leading consumer internet companies that empower people and enrich communities. For more information, please visit www.prosus.com.
-
-## 📧 Contact
-
-For questions about this research project:
-- Dogu Araci: dogu.araci[at]prosus.com
-- Zulkuf Genc: zulkuf.genc[at]prosus.com
-
-**Note**: Questions about commercial licensing or legal permissions 
-cannot be addressed by the research team. See the License section above.