Instructions to use Harini2506/scibert_12 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Harini2506/scibert_12 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Harini2506/scibert_12")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Harini2506/scibert_12") model = AutoModelForTokenClassification.from_pretrained("Harini2506/scibert_12", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Harini2506/scibert_12: direct link, hf CLI and curl.
- Browser
- Download file 437 MB
-
https://huggingface.co/Harini2506/scibert_12/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Harini2506/scibert_12/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Harini2506/scibert_12/resolve/main/pytorch_model.bin
437 MB
- Xet hash:
- 446955478340a285bca8a3aa1b15eea630886c306b1de18e7f61367ee192d6cf
- Size of remote file:
- 437 MB
- SHA256:
- a74881139bc582ec7f72d4ec492d25b28723f72a7998c00f70b3322ca2460811
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