Feature Extraction
Transformers
PyTorch
Safetensors
English
bert
scibert
scientific-text
mirror
r-compatible
Instructions to use NetworkIsLife/SciBert_Cased_DAFS with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NetworkIsLife/SciBert_Cased_DAFS with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="NetworkIsLife/SciBert_Cased_DAFS")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("NetworkIsLife/SciBert_Cased_DAFS", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from NetworkIsLife/SciBert_Cased_DAFS: direct link, hf CLI and curl.
- Browser
- Download file 442 MB
-
https://huggingface.co/NetworkIsLife/SciBert_Cased_DAFS/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://NetworkIsLife/SciBert_Cased_DAFS/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/NetworkIsLife/SciBert_Cased_DAFS/resolve/main/pytorch_model.bin
442 MB
- Xet hash:
- 28ea7e5c3f2776efd220195439464574194f790639ff97495df061a3fd5e8ab2
- Size of remote file:
- 442 MB
- SHA256:
- 4311e231db1e463e673732800285f0851de2c84be50c13ba0fdd26229a7a3c5f
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