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:
- bd906e3a6d9d63308e24932af591cb15b1e871eb8150513975e7f00d1593505c
- Size of remote file:
- 438 MB
- SHA256:
- 6dca3cc2d5e609aed2c8c39979599b508cbe670fbaea79331ce15c79c89fb6ae
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.