Instructions to use kbnlresearch/nl2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kbnlresearch/nl2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="kbnlresearch/nl2")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("kbnlresearch/nl2") model = AutoModelForMaskedLM.from_pretrained("kbnlresearch/nl2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 665789526c0e913391049c9f6dc627b592506b09d615d98af777779cfdf1c870
- Size of remote file:
- 712 MB
- SHA256:
- 7b7f34681a842c956740b5ad6f6c28f7d2889aaab21cc27a05da9beb7c670ed0
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