Upload model from local: bge-m3-ifc-kbob-finetuned

.gitattributes

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 *.zst filter=lfs diff=lfs merge=lfs -text
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+tokenizer.json filter=lfs diff=lfs merge=lfs -text

1_Pooling/config.json

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+}

README.md

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+---
+license: mit
+base_model: BAAI/bge-m3
+language:
+- de
+- en
+- multilingual
+library_name: sentence-transformers
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- feature-extraction
+- sentence-similarity
+- retrieval
+- semantic-search
+- ifc
+- lca
+
+---
+
+# Hygroskopisch/bge-m3-ifc-kbob-finetuned
+
+Sentence-Transformers model finetuned from BAAI/bge-m3 for IFC-based construction material retrieval in KBOB/LCA workflows.
+
+## Model Summary
+
+- Model ID: Hygroskopisch/bge-m3-ifc-kbob-finetuned
+- Release: v3 (2026-04-16)
+- Base model: BAAI/bge-m3
+- Embedding dimension: 1024
+- Max sequence length: 128
+- Similarity: cosine
+
+The model is optimized for queries generated from IFC element metadata and maps them to KBOB-like material labels for downstream environmental impact workflows.
+
+## Intended Use
+
+- IFC-to-material retrieval in building and infrastructure datasets.
+- Candidate generation before manual validation in LCA pipelines.
+- Semantic search over construction material catalogs with domain-specific wording.
+
+## Out-of-Scope Use
+
+- Legal, compliance, or procurement decisions without human review.
+- Safety-critical engineering sign-off.
+- Use as a standalone source of truth for environmental declarations.
+
+## Responsible Use
+
+- Keep a human-in-the-loop for final material assignment.
+- Validate results against project context, standards, and local regulations.
+- Contact: sbert-lca@pm.me
+
+## Training Data
+
+The v3 run used project-internal data artifacts and generated pair files.
+
+- Query source files: [Training/query_generation/generated_queries](https://github.com/Hygros/ifc-kbob-ai-matcher/tree/main/Training/query_generation/generated_queries)
+- Expected mapping source files: [Training/query_generation/generated_queries](https://github.com/Hygros/ifc-kbob-ai-matcher/tree/main/Training/query_generation/generated_queries)
+- Hard-negative strategy: fallback mode with random_preselected selection, up to 2 hard negatives per record
+
+Train/dev counts from run metadata:
+
+- Total pairs: 16386
+- Train pairs: 14748
+- Dev pairs: 1638
+
+## Evaluation Data
+
+Evaluation artifacts for this release:
+
+- eval/normal_queries/summary_eval-bge-m3-ifc-kbob-finetuned_model-1d06a0d7_queries-b9bc9eb9_no-reranker-7521044b.csv
+- eval/normal_queries/details_eval-bge-m3-ifc-kbob-finetuned_model-1d06a0d7_queries-b9bc9eb9_no-reranker-7521044b.csv
+
+Evaluation query count: 389
+
+## Evaluation Results
+
+The following results mirror the full evaluation summary in the main project README for the v3 model.
+
+### Core metrics by query set
+
+| Queries | Cases | Hit@1 | Hit@10 | Hit@20 | Hit@30 | Hit@50 | MRR@10 | MAP@10 | nDCG@10 | Recall@10 |
+| --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
+| Normal | 389 | 97.43% | 99.49% | 99.74% | 99.74% | 100.00% | 0.984 | 0.932 | 0.954 | 0.960 |
+| Typos | 389 | 88.43% | 94.86% | 98.20% | 98.97% | 99.49% | 0.909 | 0.844 | 0.876 | 0.890 |
+| Missing Attribute | 389 | 75.32% | 92.80% | 96.40% | 98.20% | 98.71% | 0.803 | 0.750 | 0.794 | 0.860 |
+| Missing + Typos | 389 | 68.12% | 88.17% | 94.34% | 96.92% | 98.46% | 0.739 | 0.682 | 0.731 | 0.805 |
+
+95% confidence intervals (bootstrap from summary files):
+
+| Queries | Hit@1 95% CI | Hit@10 95% CI | MRR@10 95% CI | nDCG@10 95% CI |
+| --- | --- | --- | --- | --- |
+| Normal | [95.37%, 98.97%] | [98.71%, 100.00%] | [0.971, 0.994] | [0.939, 0.968] |
+| Typos | [84.83%, 91.77%] | [92.80%, 96.66%] | [0.881, 0.935] | [0.847, 0.902] |
+| Missing Attribute | [70.69%, 79.18%] | [89.97%, 94.99%] | [0.766, 0.835] | [0.759, 0.824] |
+| Missing + Typos | [63.36%, 72.49%] | [84.95%, 91.14%] | [0.695, 0.778] | [0.690, 0.767] |
+
+### Query set definitions
+
+The four query files test robustness under controlled perturbations.
+
+| Queries | Transformation | Hard invariants |
+| --- | --- | --- |
+| Normal | Unchanged query (reference run) | No perturbation |
+| Missing file | Removes one allowed token from `PredefinedType`, `Material`, `StrengthClass`, or `insitu/precast` (`Ortbeton/Fertigteil`) | `IfcEntity` is never removed |
+| Typos file | 1 to 2 typos per line, max 1 typo per token/word | `IfcEntity` remains correct |
+| Combined file | First remove one allowed token, then inject 1 to 2 typos into remaining allowed tokens (max 1 typo per token) | `IfcEntity` remains correct |
+
+Summary of generated perturbation files:
+
+| File | Changed lines | Typo distribution |
+| --- | ---: | --- |
+| Missing | 388 | - |
+| Typos | 388 | 1 typo: 193, 2 typos: 195 |
+| Missing + Typos | 388 | 1 typo: 309, 2 typos: 61 |
+
+### Detailed interpretation
+
+Readability note: metrics are computed on 389 evaluation cases; the perturbation table above reports changed lines in the generated query files.
+
+Degradation versus Normal Queries:
+
+| Queries | Delta Hit@1 | Delta Hit@10 | Delta MRR@10 | Delta nDCG@10 |
+| --- | ---: | ---: | ---: | ---: |
+| Typos | -9.00% | -4.63% | -0.075 | -0.078 |
+| Missing Attribute | -22.11% | -6.69% | -0.181 | -0.160 |
+| Missing + Typos | -29.31% | -11.32% | -0.245 | -0.223 |
+
+Conclusion: token removal hurts more than pure typo noise; the combined perturbation is strongest, as expected.
+
+Typos vs. Missing (direct comparison):
+- Hit@1: Missing is 13.11 percentage points below Typos (75.32% vs 88.43%).
+- Hit@10: Missing is 2.06 percentage points below Typos (92.80% vs 94.86%).
+- MRR@10: Missing is 0.106 below Typos (0.803 vs 0.909).
+- nDCG@10: Missing is 0.082 below Typos (0.794 vs 0.876).
+
+Conclusion: missing semantic slots move correct results further down the ranking than typos.
+
+Top-1 vs Top-10 recovery potential:
+- Normal: Hit@10 - Hit@1 = 2.06%.
+- Typos: Hit@10 - Hit@1 = 6.43%.
+- Missing Attribute: Hit@10 - Hit@1 = 17.48%.
+- Missing + Typos: Hit@10 - Hit@1 = 20.05%.
+
+Conclusion: under perturbation, the correct material often remains in top-10 but drops from rank 1 more frequently.
+
+Statistical separability (Hit@1 CIs):
+- Normal vs Typos: no overlap; interval gap 3.60% (95.37% vs 91.77%).
+- Typos vs Missing: no overlap; interval gap 5.65% (84.83% vs 79.18%).
+- Missing vs Missing + Typos: overlap 1.80% (70.69% to 72.49%).
+
+Conclusion: the first two degradation steps are clearly separated; the final step is smaller but still negative.
+
+Practical implications:
+- High automation precision depends strongly on stable `Material`, `StrengthClass`, and `CastingMethod` slots.
+- For noisy IFC text, UI workflows should prioritize top-10 candidates and avoid relying on top-1 alone.
+- Main improvement lever is robust semantic token extraction/preservation, more than additional typo tolerance.
+
+## Usage (Sentence-Transformers)
+
+Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
+
+```
+pip install -U sentence-transformers
+```
+
+Then you can use the model like this:
+
+```python
+from sentence_transformers import SentenceTransformer
+sentences = [
+  "IfcPile BORED Stahlbeton C40/50 500 INSITU",
+  "Tiefgründung Ortbetonbohrpfahl 700",
+]
+
+model = SentenceTransformer("Hygroskopisch/bge-m3-ifc-kbob-finetuned")
+embeddings = model.encode(sentences)
+print(embeddings)
+```
+
+Load a fixed released revision:
+
+```python
+from sentence_transformers import SentenceTransformer
+
+model = SentenceTransformer(
+  "Hygroskopisch/bge-m3-ifc-kbob-finetuned",
+  revision="v3",
+)
+```
+
+## Training
+
+Core training configuration (v3):
+
+- Epochs: 2
+- Batch size: 32
+- Learning rate: 2e-05
+- Warmup ratio: 0.1
+- FP16: true
+- Seed: 42
+- Device: cuda
+- Prefix mode: no_prefix
+
+DataLoader length: 7418
+
+Loss:
+
+`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
+  ```
+  {'scale': 20.0, 'similarity_fct': 'cos_sim'}
+  ```
+
+fit() parameters:
+```
+{
+    "epochs": 2,
+    "evaluation_steps": 0,
+    "evaluator": "__main__.CombinedHit5Mrr10Evaluator",
+    "max_grad_norm": 1,
+    "optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
+    "optimizer_params": {
+        "lr": 2e-05
+    },
+    "scheduler": "WarmupLinear",
+    "steps_per_epoch": null,
+    "warmup_steps": 1484,
+    "weight_decay": 0.01
+}
+```
+
+
+## Release Notes
+
+### v3 (2026-04-16)
+
+- Replaced previous published checkpoint with the new finetuned weights from the latest IFC/KBOB training run.
+- Updated training data pipeline artifacts and documented exact source file names used for this release.
+- Published baseline retrieval metrics on 389 evaluation queries (no cross-encoder reranker).
+- Behavior change: retrieval rankings can differ from previous versions; if you require reproducibility, pin revision v3.
+- Responsible-use contact added: sbert-lca@pm.me.
+
+
+## Full Model Architecture
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel 
+  (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+  (2): Normalize()
+)
+```
+
+## Citing & Authors
+
+If you use this model in a report or publication, cite the project repository and this Hugging Face model page.

config.json

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