Feature Extraction
Transformers
Safetensors
English
bert
token-classification
text-embeddings-inference
Instructions to use noystl/scibert_token_classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use noystl/scibert_token_classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="noystl/scibert_token_classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("noystl/scibert_token_classifier") model = AutoModelForTokenClassification.from_pretrained("noystl/scibert_token_classifier") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b7ed2642c6b1ddc689a1bdd1cd7eabf219bb94b69858e76280839fc4a3160b2d
- Size of remote file:
- 437 MB
- SHA256:
- 1765b520cb53295d1044c79659fea4d2238037a16465a15f117665840251fc2e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.