Instructions to use Contrastive-Tension/BERT-Base-NLI-CT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Contrastive-Tension/BERT-Base-NLI-CT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Contrastive-Tension/BERT-Base-NLI-CT")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Contrastive-Tension/BERT-Base-NLI-CT") model = AutoModelForMaskedLM.from_pretrained("Contrastive-Tension/BERT-Base-NLI-CT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from Contrastive-Tension/BERT-Base-NLI-CT: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/Contrastive-Tension/BERT-Base-NLI-CT/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://Contrastive-Tension/BERT-Base-NLI-CT/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/Contrastive-Tension/BERT-Base-NLI-CT/resolve/main/flax_model.msgpack
438 MB
- Xet hash:
- 13f38b28d67cd793c97f0d01ddf27be2498729178d8777fdd965e9ff901d9b45
- Size of remote file:
- 438 MB
- SHA256:
- ddf5b7c7c1982cd3e38ea8167495ff6922010d249038a441a04ca85dea3b0383
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