Feature Extraction
Transformers
Safetensors
pivot
decision-making
classification
scoring
custom_code
Instructions to use Q1z/Pivot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Q1z/Pivot with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Q1z/Pivot", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Q1z/Pivot", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 374 Bytes
2d8be88 | 1 2 3 4 5 6 7 8 9 10 11 12 | """Configuration for the Pivot set-scored decision encoder."""
from transformers import PretrainedConfig
class PivotConfig(PretrainedConfig):
model_type = "pivot"
def __init__(self, dsbt_config=None, encoder_config=None, **kwargs):
super().__init__(**kwargs)
self.dsbt_config = dsbt_config or {}
self.encoder_config = encoder_config or {}
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