Update readme

---
widget:
- text: "growth is strong and we have plenty of liquidity"
---
`Modern-FinBERT-large` is a pre-trained NLP model to analyze sentiment of financial text. It is built by further training the [ModernBERT-large](https://huggingface.co/answerdotai/ModernBERT-large) 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.

More technical details on `ModernBERT`: [Click Link](https://arxiv.org/abs/2412.13663)

# How to use
You can use this model with Transformers pipeline for sentiment analysis.
```bash
pip install -U transformers
```

```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline

# Load the pre-trained model and tokenizer
model = AutoModelForSequenceClassification.from_pretrained('beethogedeon/Modern-FinBERT-large', num_labels=3)
tokenizer = AutoTokenizer.from_pretrained('answerdotai/ModernBERT-large')

# Initialize the NLP pipeline
nlp = pipeline("text-classification", model=model, tokenizer=tokenizer)

sentence = "Stocks rallied and the British pound gained."

print(nlp(sentence))
```

README.md

@@ -0,0 +1,19 @@
+---
+license: apache-2.0
+datasets:
+- takala/financial_phrasebank
+language:
+- en
+metrics:
+- f1
+base_model:
+- answerdotai/ModernBERT-large
+new_version: ProsusAI/finbert
+pipeline_tag: text-classification
+library_name: transformers
+tags:
+- finance
+- sentiment
+- financial-sentiment-analysis
+- sentiment-analysis
+---