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)# 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
final (checkpoints)
Browse files- checkpoints/metrics.json +2 -13
- checkpoints/state.pt +2 -2
checkpoints/metrics.json
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"n_chunks": 66,
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"chunk_rows": 500000,
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"missing": {
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"cos_to_target": 0.8409875631332397,
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"self_cos": 0.003099770750850439,
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"erank": 102.90254876814863,
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"cv": 0.
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"n": 5120,
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"frame_r1_after_rotation": 0.9984999895095825,
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"frame_cos_after_rotation": 0.8356035351753235,
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"n_chunks": 66,
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"chunk_rows": 500000,
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"missing": {
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],
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"cos_to_target": 0.8409875631332397,
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"self_cos": 0.003099770750850439,
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"erank": 102.90254876814863,
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"cv": 0.09320895820921411,
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"n": 5120,
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"frame_r1_after_rotation": 0.9984999895095825,
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"frame_cos_after_rotation": 0.8356035351753235,
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checkpoints/state.pt
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size
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version https://git-lfs.github.com/spec/v1
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oid sha256:41db5e6ee75e281778f8a6b0d2c1e6fed7bad6dba4c36dd19d1572eee0e15207
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size 699879247
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