Text Classification
Transformers
English
financial-nlp
sentiment-analysis
topic-classification
multitask-learning
bert
financial-news
Instructions to use farhanage/MTL-FinancialNews-Topic-Sentiment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use farhanage/MTL-FinancialNews-Topic-Sentiment with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="farhanage/MTL-FinancialNews-Topic-Sentiment")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("farhanage/MTL-FinancialNews-Topic-Sentiment", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1d3a5604cde400b61671aa8fd229642c03205b6657ddd5534b676063ae82c89b
- Size of remote file:
- 438 MB
- SHA256:
- 457a7b4e5ca33ac0eb64f8cee58db05396fb5d1a467ccbf735e232464be80292
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