Instructions to use MorcuendeA/MulderFinders with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MorcuendeA/MulderFinders with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="MorcuendeA/MulderFinders", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("MorcuendeA/MulderFinders", trust_remote_code=True) model = AutoModelForSequenceClassification.from_pretrained("MorcuendeA/MulderFinders", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1cf13f8b1fa573cc702f023d459eea0f9c23a9bd3f5232680be4541580cb0c1b
- Size of remote file:
- 5.3 kB
- SHA256:
- 7088a99f6ff3bc21b9e375ebc00f0dcc15c369193b923cac470665b3ab015572
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.