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 training_args.bin from Harini2506/scibert_12: direct link, hf CLI and curl.
- Browser
- Download file 4.54 kB
-
https://huggingface.co/Harini2506/scibert_12/resolve/main/training_args.bin
- Command line
-
hf download hf://Harini2506/scibert_12/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Harini2506/scibert_12/resolve/main/training_args.bin
4.54 kB
- Xet hash:
- bdf6dfae531b1663336f7c7d03b587828f48254eaded2dbb634477b2900a1ddf
- Size of remote file:
- 4.54 kB
- SHA256:
- 6712df8a58362d8059e481e57fecaa3522997f119d339ac9b0dbc26f025df6d9
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