Instructions to use ishan/bert-base-uncased-mnli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ishan/bert-base-uncased-mnli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ishan/bert-base-uncased-mnli")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ishan/bert-base-uncased-mnli") model = AutoModelForSequenceClassification.from_pretrained("ishan/bert-base-uncased-mnli", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from ishan/bert-base-uncased-mnli: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/ishan/bert-base-uncased-mnli/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://ishan/bert-base-uncased-mnli/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/ishan/bert-base-uncased-mnli/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 8b60f389c8521f9f51e70fe0641e0c10baa82fa4d853924c049447b7b06056dd
- Size of remote file:
- 438 MB
- SHA256:
- 713bb0d91a119e57a680a535569f8c44455e149dca314debaa55b4c03cd219da
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.