Text Ranking
sentence-transformers
Safetensors
convmemory
memory
agent-memory
validity
reranking
cross-encoder
Instructions to use Purdy0228/ConvMemory-v3-Validity-Context with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Purdy0228/ConvMemory-v3-Validity-Context with sentence-transformers:
from sentence_transformers import CrossEncoder model = CrossEncoder("Purdy0228/ConvMemory-v3-Validity-Context") query = "Which planet is known as the Red Planet?" passages = [ "Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." ] scores = model.predict([(query, passage) for passage in passages]) print(scores) - Notebooks
- Google Colab
- Kaggle
| { | |
| "backbone": "/root/autodl-tmp/hf_models/nli-deberta-v3-base", | |
| "batch_size": 1, | |
| "dev_metrics": { | |
| "demote_recall": 1.0, | |
| "event_all_type_consistency": 1.0, | |
| "fn": 0, | |
| "fp": 0, | |
| "old_target_all_type_consistency": 1.0, | |
| "pair_accuracy": 1.0, | |
| "protect_recall": 1.0, | |
| "target_all_type_consistency": 1.0, | |
| "tn": 1225, | |
| "tp": 175 | |
| }, | |
| "dev_rows": 1400, | |
| "device": "cuda", | |
| "epochs": 1, | |
| "export": "representative_checkpoint", | |
| "lr": 2e-05, | |
| "margin": 1.0, | |
| "max_length": 192, | |
| "module": "ConvMemory v3 Validity Context Layer", | |
| "notes": [ | |
| "The released checkpoint implements the v511 query-conditioned validity method.", | |
| "Multi-seed quality claims should cite the v511/v513/v514 evaluation packets, not this single checkpoint alone.", | |
| "Default mode is context annotation; demotion is explicit opt-in for dense current-state/update workloads." | |
| ], | |
| "params": 184423682, | |
| "role_weight": 1.0, | |
| "seed": 7, | |
| "source_config": "results/v514_v3_freeze_config/final_config.json", | |
| "source_method_metrics": "results/v511_memora_retrieval_demotion_benchmark_5seed/REPORT.md", | |
| "source_recipe": "experiments/v511_memora_retrieval_demotion_benchmark.py", | |
| "threshold": 0.5, | |
| "train_frac": 0.8, | |
| "train_rows": 5520, | |
| "train_seconds": 31.500682814978063, | |
| "training_curves": [ | |
| { | |
| "ce_loss": 0.27147598006590473, | |
| "epoch": 1, | |
| "role_loss": 0.17152404859223389, | |
| "train_loss": 0.4430000293751895 | |
| } | |
| ], | |
| "wall_seconds": 39.408347606658936, | |
| "hub_repo": "Purdy0228/ConvMemory-v3-Validity-Context", | |
| "artifact_sha256": { | |
| "cross_encoder/model.safetensors": "446ee0cf6df4a8967e1a78c46d2ff3a2d777de65efbf475d2278d99468faa8d9", | |
| "validity_config.json": "81eddb5f2ff4545dcf4b7655fedd1f7cf846248ad8962394195e6960a2e07849" | |
| } | |
| } | |