initial commit

.gitattributes

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

.gitignore

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+.venv
+.mypy_cache

README.md

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+---
+language:
+  - en
+license: apache-2.0
+tags:
+  - text
+pretty_name: Amazon ESCI dataset in nixietune format
+size_categories:
+  - "100K<n<1M"
+source_datasets:
+  - Amazon ESCI
+task_categories:
+  - sentence-similarity
+dataset_info:
+  config_name: default
+  features:
+  - name: query
+    dtype: string
+  - name: pos
+    sequence: string
+  - name: neg
+    sequence: string
+  - name: negscore
+    sequence: float
+  splits:
+    - name: train
+      num_bytes: 361711993
+      num_examples: 74589
+    - name: test
+      num_bytes: 109820429
+      num_examples: 22398
+    - name: test_1k
+      num_bytes: 109820429
+      num_examples: 22398
+train-eval-index:
+  - config: default
+    task: sentence-similarity
+    splits:
+      train_split: train
+      eval_split: test
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: "data/train/*"
+  - split: test
+    path: "data/test/*"
+  - split: test_1k
+    path: "data/test_1k/*"
+---
+
+# Amazon ESCI dataset
+
+A dataset in a [nixietune](https://github.com/nixiesearch/nixietune) compatible format:
+
+```json
+{
+{
+  "query": "# cellist thats not a hashtag",
+  "pos": "Funny Cellists That's Not A Hashtag Music Sweatshirt",
+  "neg": [
+    "Feelin Good Tees My Opinion Offended You Adult Humor T Shirt XL Black",
+    "Christian Faith & Cross T-Shirt - Christian Faith T Shirts T-Shirt",
+    "PLUS PLUS - 240 Piece Basic Mix - Construction Building Stem/Steam Toy, Mini Puzzle Blocks for Kids",
+    "Caution I Learned to Drive Through Video Games - Funny Gamer T-Shirt",
+    "People Who Tolerate Me On A Daily Basis T Shirt L Black",
+  ]
+}
+```
+
+This is the expanded version of the [Amazon ESCI small-en](https://github.com/amazon-science/esci-data) dataset:
+* can be loaded with [HF datasets](https://huggingface.co/docs/datasets/index) directly.
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+data = load_dataset('nixiesearch/amazon-esci', split="train")
+```
+
+## License
+
+Apache 2.0

convert.py

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+import polars as pl
+import sys
+import json
+from tqdm import tqdm
+
+labelmap = {"E": 1.0, "S": 0.1, "C": 0.01, "I": 0.0}
+split = sys.argv[3]
+
+products = (
+    pl.read_parquet(sys.argv[1])
+    .filter((pl.col("product_locale") == "us"))
+    .with_columns(
+        pl.concat_str(
+            [
+                pl.col("product_title"),
+                pl.col("product_description"),
+                pl.col("product_bullet_point"),
+                pl.col("product_brand"),
+                pl.col("product_color"),
+            ],
+            separator=" ",
+            ignore_nulls=True,
+        ).alias("text")
+    )
+    .select(pl.col("product_id", "text"))
+)
+examples = (
+    pl.read_parquet(sys.argv[2])
+    .filter(
+        (pl.col("product_locale") == "us")
+        & (pl.col("small_version") == 1)
+        & (pl.col("split") == split)
+    )
+    .with_columns(
+        pl.col("esci_label").replace(labelmap).alias("score").cast(pl.Float64)
+    )
+    .select(pl.col("query_id", "query", "product_id", "score"))
+)
+merged = examples.join(products, on="product_id", how="left")
+
+print(merged)
+
+result = merged.group_by("query_id").agg(
+    pl.first("query"), pl.col("text"), pl.col("score")
+)
+
+
+def save_json(df: pl.DataFrame, path: str):
+    with open(path, "w") as f:
+        for row in tqdm(result.to_dicts(), desc=f"saving {path}"):
+            query = row["query"]
+            pos = []
+            neg = []
+            negscore = []
+            for doc, score in zip(row["text"], row["score"]):
+                if score == 1.0:
+                    pos.append(doc)
+                else:
+                    neg.append(doc)
+                    negscore.append(score)
+            for p in pos:
+                line = json.dumps(
+                    {"query": query, "pos": p, "neg": neg, "negscore": negscore}
+                )
+                f.write(line + "\n")
+
+
+save_json(result, f"{split}.jsonl")

data/test/test.jsonl.zst

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+size 73921476

data/test_1k/test.json.zst

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+size 949594

data/train/train.jsonl.zst

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requirements.txt

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+polars
+tqdm