Instructions to use approach0/backbone-cocondenser-600 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use approach0/backbone-cocondenser-600 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="approach0/backbone-cocondenser-600")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForPreTraining tokenizer = AutoTokenizer.from_pretrained("approach0/backbone-cocondenser-600") model = AutoModelForPreTraining.from_pretrained("approach0/backbone-cocondenser-600", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download params.json from approach0/backbone-cocondenser-600: direct link, hf CLI and curl.
- Browser
- Download file 21 Bytes
-
https://huggingface.co/approach0/backbone-cocondenser-600/resolve/main/params.json
- Command line
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hf download hf://approach0/backbone-cocondenser-600/params.json
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curl -L -o params.json https://huggingface.co/approach0/backbone-cocondenser-600/resolve/main/params.json
21 Bytes
| ["cocondenser", 2, 6] |