Add dataset card

README.md

@@ -1,35 +1,90 @@
 ---
-dataset_info:
-  features:
-  - name: query_inst
-    dtype: string
-  - name: query
-    dtype: string
-  - name: document_inst
-    dtype: string
-  - name: positives
-    list: string
-  - name: negatives
-    list: string
-  splits:
-  - name: train
-    num_bytes: 23328747
-    num_examples: 65633
-  - name: validation
-    num_bytes: 8370970
-    num_examples: 25645
-  - name: test
-    num_bytes: 8324717
-    num_examples: 25537
-  download_size: 3981935
-  dataset_size: 40024434
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: validation
-    path: data/validation-*
-  - split: test
-    path: data/test-*
+license: apache-2.0
+language:
+- en
+pretty_name: MultiWOZ 2.2 Embedding Classification
+source_datasets:
+- pfb30/multi_woz_v22
+task_categories:
+- text-classification
+- sentence-similarity
+tags:
+- embedding
+- hard-negatives
+- multiwoz
+size_categories:
+- 100K<n<1M
 ---
+
+# MultiWOZ 2.2 Embedding Classification
+
+Classification data for training text embedding models, built from the utterances of
+[MultiWOZ 2.2](https://huggingface.co/datasets/pfb30/multi_woz_v22) (`pfb30/multi_woz_v22`,
+config `v2.2`). Each row pairs an utterance (the query) with its label names (the positives)
+and the most confusable wrong label names (the negatives), so labels are treated as documents.
+
+## Tasks
+
+| Task | Labels | Query instruction | Document instruction |
+|---|---|---|---|
+| intent | 10 user intents, e.g. "find restaurant", "book train" | Given a user message to a tourist information assistant, classify what the user wants to do | Represent this user intent for classification |
+| action | 13 dialogue actions, e.g. "asks for information, such as a detail or a preference" | Given a message from a conversation between a tourist and an information assistant, classify the actions it performs | none |
+| topic | 7 domains, e.g. "restaurant", "hotel" | Given a message from a conversation between a tourist and an information assistant, classify which topics it is about | none |
+| question | "question or request", "statement" | Classify whether the given message is a question or a statement | none |
+
+The task of a row is identified by its `query_inst`. Action and topic rows can have several
+positives. A turn counts as a question when it has a question-type dialogue act (Request,
+reqmore, Select, OfferBook) or contains "?".
+
+## Fields
+
+- `query_inst` (string): instruction for the query.
+- `query` (string): the utterance.
+- `document_inst` (string or null): instruction for the label documents.
+- `positives` (list of string): correct label names.
+- `negatives` (list of string): 1 to 4 wrong label names, hardest first.
+
+## How it was built
+
+Every split went through the same steps, scored with
+[Qwen/Qwen3-VL-Embedding-2B](https://huggingface.co/Qwen/Qwen3-VL-Embedding-2B):
+
+1. **Rank filter**: drop a row when any positive is not in the model's top 5 labels; keep the 4 negatives the model scores highest.
+2. **Easy-row downsampling**: rows whose margin (weakest positive score minus hardest negative score) is at least 0.10 are kept with probability 0.25.
+3. **Dedupe**: keep one row per task and query, compared lowercased without punctuation.
+4. **Per-label cap**: at most 3,000 rows per label of each task, drawn in a random order fixed by a hash of each row.
+
+Because of steps 1 and 2, validation and test are easier than raw MultiWOZ; report
+benchmark numbers on the original data.
+
+## Rows
+
+| Task | Train | Validation | Test |
+|---|---|---|---|
+| intent | 21,328 | 4,649 | 4,667 |
+| action | 22,381 | 7,642 | 7,558 |
+| topic | 15,924 | 7,354 | 7,312 |
+| question | 6,000 | 6,000 | 6,000 |
+| **Total** | **65,633** | **25,645** | **25,537** |
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("FaisalHejary/multiwoz-embedding-classification")
+```
+
+## License and citation
+
+Released under Apache 2.0, the license of the source dataset. If you use it, cite MultiWOZ 2.2:
+
+```
+@inproceedings{zang2020multiwoz,
+  title={MultiWOZ 2.2: A Dialogue Dataset with Additional Annotation Corrections and State Tracking Baselines},
+  author={Zang, Xiaoxue and Rastogi, Abhinav and Sunkara, Srinivas and Gupta, Raghav and Zhang, Jianguo and Chen, Jindong},
+  booktitle={Proceedings of the 2nd Workshop on Natural Language Processing for Conversational AI, ACL 2020},
+  pages={109--117},
+  year={2020}
+}
+```