Instructions to use devanshrj/scibert-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use devanshrj/scibert-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="devanshrj/scibert-ner")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("devanshrj/scibert-ner") model = AutoModelForTokenClassification.from_pretrained("devanshrj/scibert-ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from devanshrj/scibert-ner: direct link, hf CLI and curl.
- Browser
- Download file 437 MB
-
https://huggingface.co/devanshrj/scibert-ner/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://devanshrj/scibert-ner/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/devanshrj/scibert-ner/resolve/main/pytorch_model.bin
437 MB
- Xet hash:
- 5716560f026d49bbf5c51459114b25b1b7a40d63af7fd4157c1bd38840603b7b
- Size of remote file:
- 437 MB
- SHA256:
- fef43e720a294eb727cf9d3b1d3c45486bd093432d0259e7e18e3a1ff702b6e1
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