Instructions to use AnonymousSub/FPDM_Scientific_BERT-Large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AnonymousSub/FPDM_Scientific_BERT-Large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="AnonymousSub/FPDM_Scientific_BERT-Large")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("AnonymousSub/FPDM_Scientific_BERT-Large") model = AutoModel.from_pretrained("AnonymousSub/FPDM_Scientific_BERT-Large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 12bd9050f9dc444ffb596162a6e4ef97a3dc7bb533b46b5359490df29016f909
- Size of remote file:
- 1.34 GB
- SHA256:
- 5a2a8c76b768746ecb7097bc9593c791839ed0ade089a05bb3e45c82c51bb3c4
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