Feature Extraction
Transformers
TensorBoard
Safetensors
English
captionbert_v2
sentence-similarity
consensus-distillation
geometric-deep-learning
amoe
custom_code
Instructions to use AbstractPhil/captionbert-8192-v2-b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AbstractPhil/captionbert-8192-v2-b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="AbstractPhil/captionbert-8192-v2-b", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("AbstractPhil/captionbert-8192-v2-b", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Ctrl+K
card: the b-collective shipped with NO README. Adds the native 8-task table (.6031 -> .7294, BIOSSES inverts favourably vs v2), the trunk-bound decomposition (port -.0424, keys recover 29%, retrain recovers all), alignment health, and the final_model.pt-vs-best_model.pt root distinction between the two repos
fbd7a0d verified