Instructions to use ml4pubmed/scibert-scivocab-cased_pub_section with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ml4pubmed/scibert-scivocab-cased_pub_section with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ml4pubmed/scibert-scivocab-cased_pub_section")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ml4pubmed/scibert-scivocab-cased_pub_section") model = AutoModelForSequenceClassification.from_pretrained("ml4pubmed/scibert-scivocab-cased_pub_section", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ca9520effa9288602eced07dc3ebea91b39fa54f4756f47665a507d47658f211
- Size of remote file:
- 440 MB
- SHA256:
- 21bd7b34fe518539ce7e36f80537544e4cd6de0a99eb6eb233a2b3f1ae57719c
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