Added MTEB availability and partial reproduction steps

README.md

@@ -25,59 +25,145 @@ configs:
 
 # **DAPFAM** dataset
 
-For more details on the dataset construction and full set of experimentations and results, see the accompanying paper: **Ayaou et al., 2025 — “DAPFAM: A Domain-Aware Family-level Dataset to benchmark cross domain patent retrieval”[(Here)](https://doi.org/10.48550/arXiv.2506.22141) .**
+> **What’s new (Sept 2025)** — **DAPFAM patent family retrieval tasks are now in MTEB.** 18 tasks (ALL / IN / OUT × 3 query views × 3 target views) are available, including the 6 main ones used in our paper. You can benchmark any model with a single script and reproduce the paper’s results by selecting the same encoder (Snowflake/snowflake-arctic-embed-m-v2.0). Our paper used int8 quantization for hardware reasons; results may vary very slightly (not significantly) if you run in float16/32.
 
+### DAPFAM — A Domain‑Aware Family‑level Dataset to benchmark cross‑domain patent retrieval
 
-## Summary
+**License:** CC‑BY‑NC‑SA‑4.0  
+**Tasks:** text‑retrieval (patent family prior‑art retrieval)  
+**Languages:** English (eng‑Latn)  
+**Evaluation date span:** 1964‑06‑26 → 2023‑06‑20  
+**Cite:** Ayaou et al., 2025 — _DAPFAM: A Domain‑Aware Family‑level Dataset to benchmark cross‑domain patent retrieval_ (arXiv:2506.22141)
 
-DAPFAM provides **1 247 domain balanced full-text query patent families** and **45 336 full-text target families** with forward/backward‑citation relevance labels (≈ 50 K pairs). Each relevant link is explicitly marked **in‑domain** or **out‑of‑domain** according to IPC 3‑char overlap, enabling rigorous cross‑domain evaluation.
+---
 
-* Full text **(title · abstract · claims · description)** plus rich metadata for *every* family.
-* Multi‑jurisdictional, English‑only text (families may originate in US, JP, EP, CN, …).
-* Parquet qrel file: `qrels_all.parquet`.
+### Summary
 
-## Dataset Structure
+**DAPFAM** provides **1,247 query patent families** and **45,336 target families** with **citation‑based relevance** and explicit **domain labels** (IN/OUT). Each positive pair is IN‑domain if query and target share at least one IPC3 code, OUT‑domain otherwise. Text is at **family‑level full text** (title, abstract, claims, description). Supports both **document-** and **passage‑level** retrieval.
 
+**What makes DAPFAM different?**
+- **Explicit domain partitions** (IN vs OUT) → enables true cross‑domain evaluation.
+- **Family‑level aggregation** → reduces cross‑jurisdiction redundancy.
+- **Compute‑aware** → Small enough to support passage level experimentations on consumer-grade hardware.
+
+---
+
+### Benchmark DAPFAM on MTEB
+
+**18 retrieval tasks** have been added (ALL / IN / OUT × 3 query × 3 target field views). Six of them were directly evaluated in the paper.
+
+#### Task naming scheme
+- Query view: **TA** (Title+Abstract) or **TAC** (Title+Abstract+Claims)  
+- Target view: **TA**, **TAC**, or **FullText** (adds description)
+- Subsets: **ALL**, **IN**, **OUT** (IPC overlap filtering)
+
+#### Task list (18 total)
+
+**ALL**
+- `DAPFAMAllTitlAbsToTitlAbsRetrieval`
+- `DAPFAMAllTitlAbsToTitlAbsClmRetrieval` **(in-paper)**
+- `DAPFAMAllTitlAbsToFullTextRetrieval`
+- `DAPFAMAllTitlAbsClmToTitlAbsRetrieval`
+- `DAPFAMAllTitlAbsClmToTitlAbsClmRetrieval` **(in-paper)**
+- `DAPFAMAllTitlAbsClmToFullTextRetrieval`
+
+**IN**
+- `DAPFAMInTitlAbsToTitlAbsRetrieval`
+- `DAPFAMInTitlAbsToTitlAbsClmRetrieval` **(in-paper)**
+- `DAPFAMInTitlAbsToFullTextRetrieval`
+- `DAPFAMInTitlAbsClmToTitlAbsRetrieval`
+- `DAPFAMInTitlAbsClmToTitlAbsClmRetrieval` **(in-paper)**
+- `DAPFAMInTitlAbsClmToFullTextRetrieval`
+
+**OUT**
+- `DAPFAMOutTitlAbsToTitlAbsRetrieval`
+- `DAPFAMOutTitlAbsToTitlAbsClmRetrieval` **(in-paper)**
+- `DAPFAMOutTitlAbsToFullTextRetrieval`
+- `DAPFAMOutTitlAbsClmToTitlAbsRetrieval`
+- `DAPFAMOutTitlAbsClmToTitlAbsClmRetrieval` **(in-paper)**
+- `DAPFAMOutTitlAbsClmToFullTextRetrieval`
+
+#### Quick start — run all tasks
+```python
+import mteb
+from sentence_transformers import SentenceTransformer
+
+model_name = "Snowflake/snowflake-arctic-embed-m-v2.0"
+model = SentenceTransformer(model_name, trust_remote_code=True,
+                            model_kwargs={"torch_dtype":"float16"}).cuda().eval()
+
+task_names = [
+  # ALL
+  "DAPFAMAllTitlAbsToTitlAbsRetrieval",
+  "DAPFAMAllTitlAbsToTitlAbsClmRetrieval",
+  "DAPFAMAllTitlAbsToFullTextRetrieval",
+  "DAPFAMAllTitlAbsClmToTitlAbsRetrieval",
+  "DAPFAMAllTitlAbsClmToTitlAbsClmRetrieval",
+  "DAPFAMAllTitlAbsClmToFullTextRetrieval",
+  # IN
+  "DAPFAMInTitlAbsToTitlAbsRetrieval",
+  "DAPFAMInTitlAbsToTitlAbsClmRetrieval",
+  "DAPFAMInTitlAbsToFullTextRetrieval",
+  "DAPFAMInTitlAbsClmToTitlAbsRetrieval",
+  "DAPFAMInTitlAbsClmToTitlAbsClmRetrieval",
+  "DAPFAMInTitlAbsClmToFullTextRetrieval",
+  # OUT
+  "DAPFAMOutTitlAbsToTitlAbsRetrieval",
+  "DAPFAMOutTitlAbsToTitlAbsClmRetrieval",
+  "DAPFAMOutTitlAbsToFullTextRetrieval",
+  "DAPFAMOutTitlAbsClmToTitlAbsRetrieval",
+  "DAPFAMOutTitlAbsClmToTitlAbsClmRetrieval",
+  "DAPFAMOutTitlAbsClmToFullTextRetrieval",
+]
+
+tasks = mteb.get_tasks(tasks=task_names)
+results = mteb.MTEB(tasks=tasks).run(
+    model,
+    output_folder=f"mteb_res/{model_name}",
+    encode_kwargs={"batch_size": 16, "prompt_name": None}
+)
+print(results)
 ```
-corpus.parquet   # 45 336 rows, targets – every original column from the paper
-queries.parquet  # 1 247 rows,   queries – same columns + abstract_keywords
-qrels_all.parquet  # (all | in | out) four‑column tables → query_id · relevant_id · relevance_score · domain_rel
-```
 
-## How to load
+> To reproduce the **paper’s reported MTEB-compatible results**, restrict to the six **in-paper tasks** listed above. The encoder was run with int8 quantization in the paper; float16 runs on GPU may differ slightly.
+
+---
+
+### How to Load the Dataset
 
 ```python
 from datasets import load_dataset
 
-#According to your usage, you might not need to load all 3 subsets
+dc = load_dataset("datalyes/DAPFAM_patent", "corpus")      # 45,336 targets
+dq = load_dataset("datalyes/DAPFAM_patent", "queries")     # 1,247 queries
+dr = load_dataset("datalyes/DAPFAM_patent", "relations")   # qrels: all/in/out
+```
 
-dc = load_dataset("datalyes/DAPFAM_patent", "corpus")
+**Counts**
+- Queries: **1,247**
+- Targets: **45,336**
+- Qrels (all): **≈49,869** (positives + sampled negatives)
+- Positive qrels: **IN ~19,736**, **OUT ~5,193**
 
-dq = load_dataset("datalyes/DAPFAM_patent", "queries")
+---
 
-dr = load_dataset("datalyes/DAPFAM_patent", "relations")
-```
+### Evaluation choices
+- Metrics: **NDCG@100** (primary), **Recall@100** (secondary).
+- Document-level views in MTEB; paper also explores **passage-level** retrieval and **RRF fusion** separately.
+- Encoder: `Snowflake/snowflake-arctic-embed-m-v2.0`; in-paper runs quantized to int8 for efficiency.
 
-## Citation
+---
 
-If you find our paper or dataset helpful, please consider citing as follows:
 
+### Citation
 ```
 @misc{ayaou2025dapfamdomainawarefamilyleveldataset,
-      title={DAPFAM: A Domain-Aware Family-level Dataset to benchmark cross domain patent retrieval}, 
-      author={Iliass Ayaou and Denis Cavallucci and Hicham Chibane},
-      year={2025},
-      eprint={2506.22141},
-      archivePrefix={arXiv},
-      primaryClass={cs.CL},
-      url={https://arxiv.org/abs/2506.22141}, 
+  title={DAPFAM: A Domain-Aware Family-level Dataset to benchmark cross domain patent retrieval},
+  author={Iliass Ayaou and Denis Cavallucci and Hicham Chibane},
+  year={2025},
+  eprint={2506.22141},
+  archivePrefix={arXiv},
+  primaryClass={cs.CL},
+  url={https://arxiv.org/abs/2506.22141},
 }
-```
-
-## Quick Stats
-
-* **Queries**: 1,247
-* **Corpus (targets)**: 45,336
-* **Qrels (all)**: 49,869
-* **Qrels (in)**: 19,736
-* **Qrels (out)**: 5,193
+```