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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1c5559fa12a29d17de3068045710d26f42e0c6d854b06ad3880f71b99da2dcea
- Size of remote file:
- 1.34 GB
- SHA256:
- 96d0cd30084c829489c48f23923085fa415a0766375ce3785f5f87445c2b481a
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