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")# pip install -U transformers accelerate # 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
Download flax_model.msgpack from Contrastive-Tension/BERT-Large-NLI-CT: direct link, hf CLI and curl.
- Browser
- Download file 1.34 GB
-
https://huggingface.co/Contrastive-Tension/BERT-Large-NLI-CT/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://Contrastive-Tension/BERT-Large-NLI-CT/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/Contrastive-Tension/BERT-Large-NLI-CT/resolve/main/flax_model.msgpack
1.34 GB
- Xet hash:
- b5bc37a53cf7418ec5fcc04aa6a43248fa9c1ab01e956c83ff79aa6c829c48c5
- Size of remote file:
- 1.34 GB
- SHA256:
- b09be827260999237216ef4ae0f070e6a1759798239f622d4c870301699d6666
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