Instructions to use KM4STfulltext/SSCI-BERT-e2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KM4STfulltext/SSCI-BERT-e2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="KM4STfulltext/SSCI-BERT-e2")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("KM4STfulltext/SSCI-BERT-e2") model = AutoModelForMaskedLM.from_pretrained("KM4STfulltext/SSCI-BERT-e2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7491f7882eb849ae3ecb8272f69be94942bf6480ae3d05eeb3b25cedbca5bbd9
- Size of remote file:
- 433 MB
- SHA256:
- 04ec99b52f2b2d0bd06b0259d45bd006711da7b684ba56c64d2ea36d38d272f1
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