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:
- 6d6ea023fcb398cc6af1a4f3b42ac7153362f303652a5dd38ae12cd376667c81
- Size of remote file:
- 5.3 kB
- SHA256:
- e9bf8330025037b42d854a928006d6f6f6e6f07b712e41b90aaf441e4ca29cb5
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.