Text Classification
Transformers
PyTorch
English
bert
finance
sentiment analysis
regression
sentence bert
text-embeddings-inference
Instructions to use LHF/FinEAS with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LHF/FinEAS with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="LHF/FinEAS")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("LHF/FinEAS") model = AutoModelForSequenceClassification.from_pretrained("LHF/FinEAS") - Notebooks
- Google Colab
- Kaggle
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# FinEAS: Financial Embedding Analysis of Sentiment
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SentenceBERT for Financial News Sentiment Regression
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## Introduction
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Analyzing the sentiment of financial news is a complex task that requires a large understanding of the financial slang, as well as the knowledge of the context of each one of the companies, and the interactions of the whole economy as an ecosystem.
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# FinEAS: Financial Embedding Analysis of Sentiment
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SentenceBERT for Financial News Sentiment Regression
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**DISCLAIMER:** This model has been successfully tested with a test set of the same distribution. However, it is **not** a production-ready model as it probably needs to be updated continuously. Furthermore, the model should have been trained with more than two years of historical data. Additionally, it would need a supplementary assessment on bias, security and consistency.
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## Introduction
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Analyzing the sentiment of financial news is a complex task that requires a large understanding of the financial slang, as well as the knowledge of the context of each one of the companies, and the interactions of the whole economy as an ecosystem.
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