Instructions to use moneim/intent-bart-mnli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use moneim/intent-bart-mnli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="moneim/intent-bart-mnli")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("moneim/intent-bart-mnli") model = AutoModelForSequenceClassification.from_pretrained("moneim/intent-bart-mnli", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from moneim/intent-bart-mnli: direct link, hf CLI and curl.
- Browser
- Download file 5.24 kB
-
https://huggingface.co/moneim/intent-bart-mnli/resolve/main/training_args.bin
- Command line
-
hf download hf://moneim/intent-bart-mnli/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/moneim/intent-bart-mnli/resolve/main/training_args.bin
5.24 kB
- Xet hash:
- dcd4b1a20798308d0173889e998dec358b50b0c278b5e7c4cbd748bd15e90bd7
- Size of remote file:
- 5.24 kB
- SHA256:
- 161cb55fa88199144b29442e413856725c78ceaa1e7931ac39b7441917bfe82c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.