Instructions to use Capreolus/electra-base-msmarco with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Capreolus/electra-base-msmarco with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Capreolus/electra-base-msmarco")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Capreolus/electra-base-msmarco") model = AutoModelForSequenceClassification.from_pretrained("Capreolus/electra-base-msmarco", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- db024b0e6845fec87dc120a98d1049c0276af06d9b218a12fc36fd490c5c1f57
- Size of remote file:
- 438 MB
- SHA256:
- a2462d6c9a8e4b2487e29da05c400b38374d2d8224efd6b9704dc6842d85ca2b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.