Instructions to use mhmsadegh/rectom-causal-reasoning-crossencoder-bce-v2-709items with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use mhmsadegh/rectom-causal-reasoning-crossencoder-bce-v2-709items with sentence-transformers:
from sentence_transformers import CrossEncoder model = CrossEncoder("mhmsadegh/rectom-causal-reasoning-crossencoder-bce-v2-709items") 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
RecToM Causal-Reasoning Cross-Encoder v2 (BCE, 709-item training pool)
Same BCE cross-encoder recipe as rectom-causal-reasoning-crossencoder-bce
(the {'MATCH@1': 0.064, 'Hit@1': 0.064, 'Hit@3': 0.218, 'Hit@5': 0.305, 'MRR': 0.179} 266-item model), retrained
on a 709-item leakage-safe Case Bank (docs129 large-scale
expansion) to test whether scaling causal-path supervision improves
reasoning-aware retrieval over frozen semantic (BGE) retrieval.
Group-disjoint 5-fold CV results (bare-dialogue_id grouping, fixing a grouping-key bug found in docs129): { "crossencoder_frozen": { "n": 709, "MATCH@1": 0.06770098730606489, "Hit@1": 0.06770098730606489, "Hit@3": 0.17489421720733428, "Hit@5": 0.2679830747531735, "MRR": 0.16755665214761337, "per_task_hit3": { "belief": 0.49019607843137253, "desire": 0.18110236220472442, "judgement": 0.10483870967741936, "prediction": 0.09375, "intention": 0.11764705882352941 }, "per_task_n": { "belief": 102, "desire": 127, "judgement": 248, "prediction": 96, "intention": 136 } }, "crossencoder_finetuned": { "n": 709, "MATCH@1": 0.08180535966149506, "Hit@1": 0.08180535966149506, "Hit@3": 0.2059238363892807, "Hit@5": 0.2947813822284908, "MRR": 0.1802352172741932, "per_task_hit3": { "belief": 0.3333333333333333, "desire": 0.14960629921259844, "judgement": 0.22580645161290322, "prediction": 0.07291666666666667, "intention": 0.22058823529411764 }, "per_task_n": { "belief": 102, "desire": 127, "judgement": 248, "prediction": 96, "intention": 136 } } }
McNemar (fine-tuned vs frozen): {'ft_only_wins': 100, 'frozen_only_wins': 78, 'n_discordant': 178, 'p_value': np.float64(0.11523195131755465)} Bootstrap 95% CI (Hit@3 delta): {'point_delta': 0.03102961918194641, 'ci_95_lo': -0.004231311706629048, 'ci_95_hi': 0.06770098730606489}
No causal path, gold answer, Direct/Causal outcome, or Benefit/Harm label is ever used as model input -- supervision-only, via the pair label.
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Model tree for mhmsadegh/rectom-causal-reasoning-crossencoder-bce-v2-709items
Base model
microsoft/MiniLM-L12-H384-uncased