Add results for bisectgroup/BiCA-base (#366)

results/bisectgroup__BiCA-base/31237a836e5ae908c308a256573e5f0986498574/AILACasedocs.json

@@ -0,0 +1,166 @@
+{
+  "dataset_revision": "4106e6bcc72e0698d714ea8b101355e3e238431a",
+  "task_name": "AILACasedocs",
+  "mteb_version": "2.3.11",
+  "scores": {
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+        "cv_recall_at_1000": 1.0,
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+        "hf_subset": "default",
+        "languages": [
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+        ]
+      }
+    ]
+  },
+  "evaluation_time": 5.264985084533691,
+  "kg_co2_emissions": null
+}

results/bisectgroup__BiCA-base/31237a836e5ae908c308a256573e5f0986498574/AILAStatutes.json

@@ -0,0 +1,166 @@
+{
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+      }
+    ]
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+}

results/bisectgroup__BiCA-base/31237a836e5ae908c308a256573e5f0986498574/AppsRetrieval.json

@@ -0,0 +1,167 @@
+{
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+}

results/bisectgroup__BiCA-base/31237a836e5ae908c308a256573e5f0986498574/ChatDoctorRetrieval.json

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results/bisectgroup__BiCA-base/31237a836e5ae908c308a256573e5f0986498574/DS1000Retrieval.json

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results/bisectgroup__BiCA-base/31237a836e5ae908c308a256573e5f0986498574/FinQARetrieval.json

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results/bisectgroup__BiCA-base/31237a836e5ae908c308a256573e5f0986498574/FinanceBenchRetrieval.json

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results/bisectgroup__BiCA-base/31237a836e5ae908c308a256573e5f0986498574/FreshStackRetrieval.json

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+}

results/bisectgroup__BiCA-base/31237a836e5ae908c308a256573e5f0986498574/HC3FinanceRetrieval.json

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results/bisectgroup__BiCA-base/31237a836e5ae908c308a256573e5f0986498574/HumanEvalRetrieval.json

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results/bisectgroup__BiCA-base/31237a836e5ae908c308a256573e5f0986498574/LegalQuAD.json

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+}

results/bisectgroup__BiCA-base/31237a836e5ae908c308a256573e5f0986498574/LegalSummarization.json

@@ -0,0 +1,166 @@
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results/bisectgroup__BiCA-base/31237a836e5ae908c308a256573e5f0986498574/MBPPRetrieval.json

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results/bisectgroup__BiCA-base/31237a836e5ae908c308a256573e5f0986498574/WikiSQLRetrieval.json

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results/bisectgroup__BiCA-base/31237a836e5ae908c308a256573e5f0986498574/model_meta.json

@@ -0,0 +1 @@
+{"loader_kwargs": {}, "name": "bisectgroup/BiCA-base", "revision": "31237a836e5ae908c308a256573e5f0986498574", "release_date": "2025-11-12", "languages": ["eng-Latn"], "n_parameters": "110_000_000", "memory_usage_mb": "418", "max_tokens": 512, "embed_dim": 768, "license": "mit", "open_weights": true, "public_training_code": "https://github.com/NiravBhattLab/BiCA", "public_training_data": "https://huggingface.co/datasets/bisectgroup/hard-negatives-traversal", "framework": ["PyTorch", "Sentence Transformers"], "reference": null, "similarity_fn_name": "cosine", "use_instructions": null, "training_datasets": null, "adapted_from": "thenlper/gte-base", "superseded_by": null, "modalities": ["text"], "is_cross_encoder": null, "citation": "@misc{sinha2025bicaeffectivebiomedicaldense, title={BiCA: Effective Biomedical Dense Retrieval with Citation-Aware Hard Negatives}, author={Aarush Sinha and Pavan Kumar S and Roshan Balaji and Nirav Pravinbhai Bhatt}, year={2025}, eprint={2511.08029}, archivePrefix={arXiv}, primaryClass={cs.IR}, url={https://arxiv.org/abs/2511.08029}}", "contacts": null, "loader": "sentence_transformers_loader"}