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 training_args.bin from Harini2506/scibert_1: direct link, hf CLI and curl.
- Browser
- Download file 4.47 kB
-
https://huggingface.co/Harini2506/scibert_1/resolve/main/training_args.bin
- Command line
-
hf download hf://Harini2506/scibert_1/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Harini2506/scibert_1/resolve/main/training_args.bin
4.47 kB
- Xet hash:
- 5c6e120626014a7fa48d01cf62cd878eb959bc9a5e2f0631fb469ec79b5bcb71
- Size of remote file:
- 4.47 kB
- SHA256:
- bf901de1a38305f3848943331c905ae4f6f2a1a4915e92cf15f364a91042a83b
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