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 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AbstractPhil/captionbert-8192-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="AbstractPhil/captionbert-8192-v2", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("AbstractPhil/captionbert-8192-v2", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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card: this folder is the 3-ANCHOR COLLECTIVE, not the 2-anchor moe run - the moe card had been copied here wholesale (wrong title, wrong anchor table, wrong results, moe-v1 amplitude telemetry). Real 8-task rows, greedy sweep incl. the random capacity control, correct in-folder load paths, masked-vs-solo labelled
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final: 2 anchors + dispatch + card
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arm random
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arm topical
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arm lexical
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arm paraphrase
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arm simplify
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arm equiv
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