Instructions to use Contrastive-Tension/BERT-Large-NLI-CT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Contrastive-Tension/BERT-Large-NLI-CT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Contrastive-Tension/BERT-Large-NLI-CT")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Contrastive-Tension/BERT-Large-NLI-CT") model = AutoModelForMaskedLM.from_pretrained("Contrastive-Tension/BERT-Large-NLI-CT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bdd1afeef8deef5c42a1a0107413886bf8fb1a9218d50fe1a9bffbb0089c06e3
- Size of remote file:
- 1.34 GB
- SHA256:
- 582344139d348933565b816a53535bbff4ce2d5fee279ffdff9a44dfcf008e26
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.