Text Classification
Transformers
Safetensors
English
modernbert
finance
sentiment
financial-sentiment-analysis
sentiment-analysis
text-embeddings-inference
Instructions to use beethogedeon/Modern-FinBERT-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use beethogedeon/Modern-FinBERT-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="beethogedeon/Modern-FinBERT-large")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("beethogedeon/Modern-FinBERT-large") model = AutoModelForSequenceClassification.from_pretrained("beethogedeon/Modern-FinBERT-large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Verified signature--- 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…f6a2c3e
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