Instructions to use Sandy0909/finance_sentiment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sandy0909/finance_sentiment with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Sandy0909/finance_sentiment")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Sandy0909/finance_sentiment") model = AutoModelForSequenceClassification.from_pretrained("Sandy0909/finance_sentiment", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 97f26e2b7aff7819a997e5de6eb34c6d67e946d6e5ceee252a7df92242ad0e3c
- Size of remote file:
- 439 MB
- SHA256:
- de228d017db87430e26f388b90483211caf84188db7d7e61bf0edbead4a4f0c1
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