Instructions to use OpenMatch/condenser-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMatch/condenser-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="OpenMatch/condenser-large")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("OpenMatch/condenser-large") model = AutoModelForMaskedLM.from_pretrained("OpenMatch/condenser-large") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c60218f537a6cbe95349f7c76bbbb3622c4b34b876e1bc670cf783ad1350b422
- Size of remote file:
- 101 MB
- SHA256:
- df47535849d28b711d01d68297e7bc547f028211366b805b41299a507d626851
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