Update parquet files

.gitattributes

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 *.zst filter=lfs diff=lfs merge=lfs -text
+default/train/0000.parquet filter=lfs diff=lfs merge=lfs -text
+default/train/0001.parquet filter=lfs diff=lfs merge=lfs -text
+default/train/0002.parquet filter=lfs diff=lfs merge=lfs -text
+default/train/0003.parquet filter=lfs diff=lfs merge=lfs -text
+default/train/0004.parquet filter=lfs diff=lfs merge=lfs -text
+default/train/0005.parquet filter=lfs diff=lfs merge=lfs -text
+default/test/0000.parquet filter=lfs diff=lfs merge=lfs -text
+default/test/0001.parquet filter=lfs diff=lfs merge=lfs -text
+default/test/0002.parquet filter=lfs diff=lfs merge=lfs -text
+default/test_1k/0000.parquet 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

@@ -1,68 +0,0 @@
-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 → default/test/0000.parquet

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requirements.txt

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-polars
-tqdm