Instructions to use cbrew475/mpnet-metric with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cbrew475/mpnet-metric with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="cbrew475/mpnet-metric")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("cbrew475/mpnet-metric") model = AutoModelForSequenceClassification.from_pretrained("cbrew475/mpnet-metric", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from cbrew475/mpnet-metric: direct link, hf CLI and curl.
- Browser
- Download file 2.93 kB
-
https://huggingface.co/cbrew475/mpnet-metric/resolve/main/training_args.bin
- Command line
-
hf download hf://cbrew475/mpnet-metric/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/cbrew475/mpnet-metric/resolve/main/training_args.bin
2.93 kB
- Xet hash:
- 9866b732468a633c8c6d090a6557c0c02802aff1598e77d54ad89f55ff3f5add
- Size of remote file:
- 2.93 kB
- SHA256:
- 292e7a8288c43b303abd31559aec885616dd7d87f70adfd70022f96ffad2e087
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.