Instructions to use KM4STfulltext/SSCI-SciBERT-e4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KM4STfulltext/SSCI-SciBERT-e4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="KM4STfulltext/SSCI-SciBERT-e4")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("KM4STfulltext/SSCI-SciBERT-e4") model = AutoModelForMaskedLM.from_pretrained("KM4STfulltext/SSCI-SciBERT-e4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ec6755e161e2196767b4f121688c9b67c33a699c6bfd148087885a17973aca28
- Size of remote file:
- 440 MB
- SHA256:
- 0ab12801dad4159151ee013dc399b04d0fc10c4a163dc05810b83b73cdfc5c9f
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