Instructions to use Factiverse/claim_detection_unquantized with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Factiverse/claim_detection_unquantized with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Factiverse/claim_detection_unquantized")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Factiverse/claim_detection_unquantized") model = AutoModelForSequenceClassification.from_pretrained("Factiverse/claim_detection_unquantized") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d70551ea3e86acd93f2854bdf391854489a2a9e52bbc9e41d0833a81ad11f805
- Size of remote file:
- 2.24 GB
- SHA256:
- 95dbd3325e041a1e9b93ba64e8fc5b353eb68443acaabca84d064b131751ecbc
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.