Instructions to use sasi2400/EquityVsDebtIssue with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sasi2400/EquityVsDebtIssue with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="sasi2400/EquityVsDebtIssue")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("sasi2400/EquityVsDebtIssue") model = AutoModelForSequenceClassification.from_pretrained("sasi2400/EquityVsDebtIssue", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 696631f49d3fb8d722b304f1820810be2aa067f82d305489fb9da1ab5ef47487
- Size of remote file:
- 3.45 kB
- SHA256:
- 8048a625f267c73d2dcf7c226fca7682aa0be57a808f444b2c676ab434062a1b
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