Text Classification
Transformers
PyTorch
Safetensors
English
bert
document sections
sentence classification
document classification
medical
health
biomedical
text-embeddings-inference
Instructions to use ml4pubmed/scibert-scivocab-uncased_pub_section with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ml4pubmed/scibert-scivocab-uncased_pub_section with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ml4pubmed/scibert-scivocab-uncased_pub_section")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ml4pubmed/scibert-scivocab-uncased_pub_section") model = AutoModelForSequenceClassification.from_pretrained("ml4pubmed/scibert-scivocab-uncased_pub_section", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 90809a9d495287869f3140899f94fc313b4950d5e3c1d3eb48279692f32ca5be
- Size of remote file:
- 440 MB
- SHA256:
- 49878f6459ef2d5d68e96eab2fdcc604471e4bc6d441bf2e72f9d2e456702db4
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