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:
- 9a5316f0e09e7670815dc71cbfdc7b6af044c5ea7727dfc31b458fcaede43ab5
- Size of remote file:
- 439 MB
- SHA256:
- 942c351d41a9a7fd679b9c2686d41fe6d973874fc5a255c0397f60fdedd661ae
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