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README.md

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+---
+license: cc-by-4.0
+language:
+- en
+task_categories:
+- text-generation
+- text-classification
+size_categories:
+- 1K<n<10K
+tags:
+- e-commerce
+- instruction-tuning
+- product-matching
+- teaching
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: train.parquet
+  - split: validation
+    path: val.parquet
+  - split: test
+    path: test.parquet
+---
+
+# ECInstruct subset — e-commerce instruction tuning (teaching)
+
+Subset of [ECInstruct](https://huggingface.co/datasets/NingLab/ECInstruct)
+(Peng et al., *eCeLLM*, ICML 2024), **CC BY 4.0**, restricted to `Product_Matching`, `Multiclass_Product_Classification`, `Product_Substitute_Identification` and
+reformatted as chat messages for `trl`'s `SFTTrainer`.
+
+**Modifications** (as required by CC BY 4.0): task and setting filtering,
+per-task subsampling (1500/task for train, 250/task for each eval split), instruction/input/options folded into a single
+user message, answer kept as the assistant message.
+
+**Attribution.** ECInstruct, Bo Peng, Xinyi Ling, Ziru Chen, Huan Sun, Xia Ning,
+*eCeLLM: Generalizing Large Language Models for E-commerce from Large-scale,
+High-quality Instruction Data*, ICML 2024 — CC BY 4.0. The three tasks kept here
+derive from [Amazon-Google Products](https://dbs.uni-leipzig.de/research/projects/benchmark-datasets-for-entity-resolution)
+(CC BY 4.0, product matching) and the [Shopping Queries Dataset / ESCI](https://github.com/amazon-science/esci-data)
+(Apache 2.0, the two query-product tasks). Tasks whose upstream is unlicensed
+(Amazon Review 2018, AmazonQA) or non-commercial (MAVE, CC BY-NC 4.0, behind
+attribute value extraction) are deliberately excluded.
+
+| Split | Rows |
+|---|---|
+| `train` | 4500 |
+| `val` | 750 |
+| `test` | 750 |
+
+Columns: `messages` (chat), `prompt` / `completion` (the same content, split),
+`task`, `setting`.

test.parquet

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train.parquet

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val.parquet

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