Instructions to use Contrastive-Tension/BERT-Distil-NLI-CT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Contrastive-Tension/BERT-Distil-NLI-CT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Contrastive-Tension/BERT-Distil-NLI-CT")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Contrastive-Tension/BERT-Distil-NLI-CT") model = AutoModelForMaskedLM.from_pretrained("Contrastive-Tension/BERT-Distil-NLI-CT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Contrastive-Tension/BERT-Distil-NLI-CT: direct link, hf CLI and curl.
- Browser
- Download file 265 MB
-
https://huggingface.co/Contrastive-Tension/BERT-Distil-NLI-CT/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Contrastive-Tension/BERT-Distil-NLI-CT/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Contrastive-Tension/BERT-Distil-NLI-CT/resolve/main/pytorch_model.bin
265 MB
- Xet hash:
- 2c619e5adbd6ae4be11af4af277b4dbe996c1eaf74f41b3736b83c6780ce2e3a
- Size of remote file:
- 265 MB
- SHA256:
- 28eb21bb6076f492788b25a497eb7acca8c0bcd102458b6af6fc3d4156d4d6e4
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