Instructions to use steve1989/dasent-fin-news-sentiment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use steve1989/dasent-fin-news-sentiment with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="steve1989/dasent-fin-news-sentiment")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("steve1989/dasent-fin-news-sentiment") model = AutoModelForSequenceClassification.from_pretrained("steve1989/dasent-fin-news-sentiment") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 02f9cd3d807df1d7f1cb48ad46e1a1ca1a3457560d8195ef4f73cafcfeec08cf
- Size of remote file:
- 443 MB
- SHA256:
- ba5f7ad65767ee1ea91cd2f47062fe120da0aa113ae3e2509c5fd2960dd1500e
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