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")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("devanshrj/scibert-ner") model = AutoModelForTokenClassification.from_pretrained("devanshrj/scibert-ner") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 815b9049c2a4672066dee4f80f339bab9573845f472a57f198f5ec840e4eba09
- Size of remote file:
- 4.54 kB
- SHA256:
- 172e71df95ba7f570c268f6381eb063c7b80b7d09c6c264533c837c92d5a9b70
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.