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
step 2000
Browse files
amoe/sts/captionbert-v2-sts-anchor.anchor.pt
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version https://git-lfs.github.com/spec/v1
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amoe/sts/metrics.json
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"STS-B_ON": 0.7151407457911756,
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"SICK-R_OFF": 0.652602297708298,
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"SICK-R_ON": 0.7578871303476999
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}
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],
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"step":
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"config": {
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"run_name": "captionbert-v2-sts-anchor",
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"trunk_repo": "AbstractPhil/captionbert-8192-v2",
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"STS-B_ON": 0.7151407457911756,
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"SICK-R_OFF": 0.652602297708298,
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"SICK-R_ON": 0.7578871303476999
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},
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{
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"step": 2000,
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"STS-B_OFF": 0.574721280584061,
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"STS-B_ON": 0.7154519898827421,
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"SICK-R_OFF": 0.652602297708298,
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"SICK-R_ON": 0.7573430151835525
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}
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],
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"step": 2000,
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"config": {
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"run_name": "captionbert-v2-sts-anchor",
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"trunk_repo": "AbstractPhil/captionbert-8192-v2",
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