SaT Supervised Mixture (SM) Models
Collection
SaT (Segment any Text) models, further trained on a Supervised Mixture of diverse styles and corruptions. Universal Sentence Segmentation models! • 6 items • Updated • 5
How to use segment-any-text/sat-3l-sm with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("token-classification", model="segment-any-text/sat-3l-sm") # Load model directly
from transformers import AutoModelForTokenClassification
model = AutoModelForTokenClassification.from_pretrained("segment-any-text/sat-3l-sm", dtype="auto")Model for wtpsplit.
State-of-the-art sentence segmentation with 3 Transfomer layers.
For details, see our Segment any Text paper