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Purdy0228/ConvMemory-OPC-Student-BGE
ConvMemory-OPC-Student-BGE is a machine learning model from Purdy0228. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for sentence-transformers. The card lists the license as mit.
This checkpoint is an OPC-style Chinese ConvMemory student reranker. It keeps the ConvMemory inference pattern while using a Chinese-specialized BGE encoder:
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.pt22.9 MB · 100%
From the Hugging Face model README
This checkpoint is an OPC-style Chinese ConvMemory student reranker. It keeps the ConvMemory inference pattern while using a Chinese-specialized BGE encoder:
bge-base-zh embeddings -> ConvMemory window encoder -> CE-lite reranker head.
from convmemory import OPCConvMemory
model = OPCConvMemory.from_pretrained(
"Purdy0228/ConvMemory-OPC-Student-BGE",
device="cuda", # or "cpu"
)
ranked = model.rerank(
query="现在定价方案是什么?",
memories=[
{"id": "m1", "text": "定价方案最终是基础版 99 元,专业版 299 元。"},
{"id": "m2", "text": "获客渠道第一批先做小红书。"},
{"id": "m3", "text": "旧方案曾经考虑过 49 元,后来被否掉。"},
],
top_k=3,
)
BAAI/bge-base-zh-v1.5BAAI/bge-reranker-v2-m3The teacher is not used at inference time. Evaluation uses construction-gold memories from the synthetic OPC-style data generator, not teacher agreement.
Use this checkpoint for Chinese OPC-style memory retrieval when candidate pools are large enough that an online cross-encoder teacher is too slow.
For small candidate pools, a strong dense retriever such as
BAAI/bge-base-zh-v1.5 may be sufficient. The phase-0 gate did not show a
statistically robust improvement over BGE cosine retrieval, so this checkpoint
should be treated as a ConvMemory student operating point rather than a universal
replacement for the base retriever.
Package path: OPCConvMemory.from_pretrained(...)
| split | questions | R@1 | R@5 | R@10 | Hit@1 | Hit@10 | MRR |
|---|---|---|---|---|---|---|---|
| phase-0 pass-only test | 43 | 0.4516 | 0.9583 | 0.9913 | 0.7442 | 1.0000 | 0.8484 |
| pool size | teacher MRR | student MRR | teacher ms/query | student ms/query |
|---|---|---|---|---|
| 10 | 0.9302 | 0.9380 | 19.89 | 5.54 |
| 50 | 0.8702 | 0.8411 | 48.24 | 26.60 |
| 200 | 0.8302 | 0.8070 | 166.53 | 30.17 |
| 500 | 0.7760 | 0.7445 | 390.79 | 35.66 |
| 1000 | 0.6803 | 0.6599 | 779.67 | 126.18 |
student.pt: ConvMemory window encoder and CE-lite scorer weightsconfig.json: model and architecture configMANIFEST.json: provenance and checksumsstudent.pt SHA256:
e56a74d937446d05de7381307f9188cffb8794e07805ee9837838ed8503713ebconfig.json SHA256:
24e838d0e86bd839f618e51890a4330d9d677bdda790d27bea53ed98614c9b77c8f595ee8afd2aa2d1a2f6336ac172f297667e3ec3b69bd7f8e876b925a1319d