Instructions to use Harini2506/scibert_1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Harini2506/scibert_1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Harini2506/scibert_1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Harini2506/scibert_1") model = AutoModelForTokenClassification.from_pretrained("Harini2506/scibert_1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Harini2506/scibert_1: direct link, hf CLI and curl.
- Browser
- Download file 555 MB
-
https://huggingface.co/Harini2506/scibert_1/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Harini2506/scibert_1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Harini2506/scibert_1/resolve/main/pytorch_model.bin
555 MB
- Xet hash:
- 4a07768c60de4bf285cc33009a74cd42140bdf17d371d79840e81b6275959564
- Size of remote file:
- 555 MB
- SHA256:
- 3300472537ac6ba30d1771ea11fefeb70ec4ca3d1f35abac80f5e2fbd8328ea7
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