Text Classification
Transformers
PyTorch
TensorFlow
English
financial-sentiment-analysis
sentiment-analysis
Instructions to use yiyanghkust/finbert-tone with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yiyanghkust/finbert-tone with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="yiyanghkust/finbert-tone")# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForSequenceClassification model = AutoModelForSequenceClassification.from_pretrained("yiyanghkust/finbert-tone", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
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`FinBERT` is a BERT model pre-trained on financial communication text. The purpose is to enhance financial NLP research and practice. It is trained on the following three financial communication corpus. The total corpora size is 4.9B tokens.
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- Corporate Reports 10-K & 10-Q: 2.5B tokens
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- Earnings Call Transcripts: 1.3B tokens
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- Analyst Reports: 1.1B tokens
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More details on `FinBERT`: [Click Link](https://github.com/yya518/FinBERT)
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This released `finbert-tone` model is the `FinBERT` model fine-tuned on 10,000 manually annotated (positive, negative, neutral) sentences from analyst reports. This model achieves superior performance on financial tone analysis task. If you are simply interested in using `FinBERT` for financial tone analysis, give it a try.
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