Create README.md

README.md

@@ -0,0 +1,156 @@
+## Overview
+Original dataset [here](https://github.com/decompositional-semantics-initiative/DNC).
+
+This dataset has been proposed in [Collecting Diverse Natural Language Inference Problems for Sentence Representation Evaluation](https://www.aclweb.org/anthology/D18-1007/).
+
+
+## Dataset curation
+This version of the dataset does not include the `type-of-inference` "KG" as its label set is 
+`[1, 2, 3, 4, 5]` while here we focus on NLI-related label sets, i.e. `[entailed, not-entailed]`.
+For this reason, I named the dataset DNLI for _Diverse_ NLI, as in [Liu et al 2020](https://aclanthology.org/2020.conll-1.48/), instead of DNC.
+
+This version of the dataset contains columns from the `*_data.json` and the `*_metadata.json` files available in the repo.
+In the original repo, each data file has the following keys and values:
+
+- `context`: The context sentence for the NLI pair. The context is already tokenized.
+- `hypothesis`: The hypothesis sentence for the NLI pair. The hypothesis is already tokenized.
+- `label`: The label for the NLI pair
+- `label-set`: The set of possible labels for the specific NLI pair
+- `binary-label`: A `True` or `False` label. See the paper for details on how we convert the `label` into a binary label.
+- `split`: This can be `train`, `dev`, or `test`.
+- `type-of-inference`: A string indicating what type of inference is tested in this example.
+- `pair-id`: A unique integer id for the NLI pair. The `pair-id` is used to find the corresponding metadata for any given NLI pair
+
+while each metadata file has the following columns
+
+- `pair-id`: A unique integer id for the NLI pair. 
+- `corpus`: The original corpus where this example came from.
+- `corpus-sent-id`: The id of the sentence (or example) in the original dataset that we recast.
+- `corpus-license`: The license for the data from the original dataset.
+- `creation-approach`: Determines the method used to recast this example. Options are `automatic`, `manual`, or `human-labeled`.
+- `misc`: A dictionary of other relevant information. This is an optional field.
+
+The files are merged on the `pair-id` key. I **do not** include the `misc` column as it is not essential for NLI.
+
+NOTE: the label mapping is **not** the custom (i.e., 3 class) for NLI tasks. They used a binary target and I encoded them
+with the following mapping `{"not-entailed": 0, "entailed": 1}`.
+
+NOTE: some instances are present in multiple splits (matching performed by exact matching on "context", "hypothesis", and "label").
+
+## Code to create the dataset
+```python
+import pandas as pd
+from datasets import Dataset, ClassLabel, Value, Features, DatasetDict, Sequence
+from pathlib import Path
+
+
+paths = {
+    "train": "<path_to_folder>/DNC-master/train",
+    "dev": "<path_to_folder>/DNC-master/dev",
+    "test": "<path_to_folder>/DNC-master/test",
+}
+
+# read all data files
+dfs = []
+for split, path in paths.items():
+    for f_name in Path(path).rglob("*_data.json"):
+        df = pd.read_json(str(f_name))
+        df["file_split_data"] = split
+        dfs.append(df)
+data = pd.concat(dfs, ignore_index=False, axis=0)
+
+# read all metadata files
+meta_dfs = []
+for split, path in paths.items():
+    for f_name in Path(path).rglob("*_metadata.json"):
+        df = pd.read_json(str(f_name))
+        meta_dfs.append(df)
+metadata = pd.concat(meta_dfs, ignore_index=False, axis=0)
+
+# merge
+dataset = pd.merge(data, metadata, on="pair-id", how="left")
+
+# check that the split column reflects file splits
+assert sum(dataset["split"] != dataset["file_split_data"]) == 0
+dataset = dataset.drop(columns=["file_split_data"])
+
+# fix `binary-label` column
+dataset.loc[~dataset["label"].isin(["entailed", "not-entailed"]), "binary-label"] = False
+dataset.loc[dataset["label"].isin(["entailed", "not-entailed"]), "binary-label"] = True
+
+# fix datatype
+dataset["corpus-sent-id"] = dataset["corpus-sent-id"].astype(str)
+
+# order columns as shown in the README.md
+columns = [
+    "context",
+    "hypothesis",
+    "label",
+    "label-set",
+    "binary-label",
+    "split",
+    "type-of-inference",
+    "pair-id",
+    "corpus",
+    "corpus-sent-id",
+    "corpus-license",
+    "creation-approach",
+    "misc",
+]
+dataset = dataset.loc[:, columns]
+
+# remove misc column
+dataset = dataset.drop(columns=["misc"])
+
+# remove KG for NLI
+dataset.loc[(dataset["label"].isin([1, 2, 3, 4, 5])), "type-of-inference"].value_counts()
+# > the only split with label-set [1, 2, 3, 4, 5], so remove as we focus on NLI
+dataset = dataset.loc[~(dataset["type-of-inference"] == "KG")]
+
+# encode labels
+dataset["label"] = dataset["label"].map({"not-entailed": 0, "entailed": 1})
+
+# fill NA in label-set
+dataset["label-set"] = dataset["label-set"].ffill()
+
+features = Features(
+    {
+        "context": Value(dtype="string"),
+        "hypothesis": Value(dtype="string"),
+        "label": ClassLabel(num_classes=2, names=["not-entailed", "entailed"]),
+        "label-set": Sequence(length=2, feature=Value(dtype="string")),
+        "binary-label": Value(dtype="bool"),
+        "split": Value(dtype="string"),
+        "type-of-inference": Value(dtype="string"),
+        "pair-id": Value(dtype="int64"),
+        "corpus": Value(dtype="string"),
+        "corpus-sent-id": Value(dtype="string"),
+        "corpus-license": Value(dtype="string"),
+        "creation-approach": Value(dtype="string"),
+    }
+)
+
+dataset_splits = {}
+for split in ("train", "dev", "test"):
+    df_split = dataset.loc[dataset["split"] == split]
+    dataset_splits[split] = Dataset.from_pandas(df_split, features=features)
+
+dataset_splits = DatasetDict(dataset_splits)
+dataset_splits.push_to_hub("pietrolesci/dnli", token="<your token>")
+
+# check overlap between splits
+from itertools import combinations
+for i, j in combinations(dataset_splits.keys(), 2):
+    print(
+        f"{i} - {j}: ",
+        pd.merge(
+            dataset_splits[i].to_pandas(), 
+            dataset_splits[j].to_pandas(), 
+            on=["context", "hypothesis", "label"], 
+            how="inner",
+        ).shape[0],
+    )
+#> train - dev:  127
+#> train - test:  55
+#> dev - test:  54
+```