Instructions to use prajjwal1/bert-medium-mnli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prajjwal1/bert-medium-mnli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="prajjwal1/bert-medium-mnli")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("prajjwal1/bert-medium-mnli") model = AutoModelForSequenceClassification.from_pretrained("prajjwal1/bert-medium-mnli", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from prajjwal1/bert-medium-mnli: direct link, hf CLI and curl.
- Browser
- Download file 1.52 kB
-
https://huggingface.co/prajjwal1/bert-medium-mnli/resolve/5a6f3ac3adfc40eb4d5dd4d62667d69e713b3d94/training_args.bin
- Command line
-
hf download hf://prajjwal1/bert-medium-mnli@5a6f3ac3adfc40eb4d5dd4d62667d69e713b3d94/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/prajjwal1/bert-medium-mnli/resolve/5a6f3ac3adfc40eb4d5dd4d62667d69e713b3d94/training_args.bin
1.52 kB
- Xet hash:
- edd4e4ec84afaf8c6496577916f341152f5374d0ebf788c91587875b3f8d0284
- Size of remote file:
- 1.52 kB
- SHA256:
- 5268c444f63559950ca1d005f40229d3f3e2695dd7a07e13c09cbd99809737db
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.