pietrolesci
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Create README.md
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README.md
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1 |
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## Overview
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Original dataset available [here](https://wellecks.github.io/dialogue_nli/).
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## Dataset curation
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Original `label` column is renamed `original_label`. The original classes are renamed as follows
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```
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{"positive": "entailment", "negative": "contradiction", "neutral": "neutral"})
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```
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and encoded with the following mapping
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```
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{"entailment": 0, "neutral": 1, "contradiction": 2}
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```
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and stored in the newly created column `label`.
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The following splits and the corresponding columns are present in the original files
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```
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train {'dtype', 'id', 'sentence1', 'sentence2', 'original_label', 'label', 'triple2', 'triple1'}
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dev {'dtype', 'id', 'sentence1', 'sentence2', 'original_label', 'label', 'triple2', 'triple1'}
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test {'dtype', 'id', 'sentence1', 'sentence2', 'original_label', 'label', 'triple2', 'triple1'}
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verified_test {'dtype', 'annotation3', 'sentence1', 'sentence2', 'annotation1', 'annotation2', 'original_label', 'label', 'triple2', 'triple1'}
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extra_test {'dtype', 'id', 'sentence1', 'sentence2', 'original_label', 'label', 'triple2', 'triple1'}
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extra_dev {'dtype', 'id', 'sentence1', 'sentence2', 'original_label', 'label', 'triple2', 'triple1'}
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extra_train {'dtype', 'id', 'sentence1', 'sentence2', 'original_label', 'label', 'triple2', 'triple1'}
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valid_havenot {'dtype', 'id', 'sentence1', 'sentence2', 'original_label', 'label', 'triple2', 'triple1'}
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valid_attributes {'dtype', 'id', 'sentence1', 'sentence2', 'original_label', 'label', 'triple2', 'triple1'}
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valid_likedislike {'dtype', 'id', 'sentence1', 'sentence2', 'original_label', 'label', 'triple2', 'triple1'}
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```
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Note that I only keep the common columns, which means that I drop "annotation{1, 2, 3" from `verified_test`.
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Note that there are some splits with the same instances, as found by matching on "original_label", "sentence1", "sentence2".
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## Code to create dataset
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```python
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import pandas as pd
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from pathlib import Path
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import json
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from datasets import Features, Value, ClassLabel, Dataset, DatasetDict, Sequence
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# load data
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ds = {}
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for path in Path(".").rglob("<path to folder>/*.jsonl"):
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print(path, flush=True)
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with path.open("r") as fl:
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data = fl.read()
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try:
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d = json.loads(data, encoding="utf-8")
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except json.JSONDecodeError as error:
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print(error)
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df = pd.DataFrame(d)
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# encode labels
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df["original_label"] = df["label"]
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df["label"] = df["label"].map({"positive": "entailment", "negative": "contradiction", "neutral": "neutral"})
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df["label"] = df["label"].map({"entailment": 0, "neutral": 1, "contradiction": 2})
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ds[path.name.split(".")[0]] = df
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# prettify names of data splits
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datasets = {
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k.replace("dialogue_nli_", "").replace("uu_", "").lower(): v
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for k, v in ds.items()
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}
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datasets.keys()
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#> dict_keys(['train', 'dev', 'test', 'verified_test', 'extra_test', 'extra_dev', 'extra_train', 'valid_havenot', 'valid_attributes', 'valid_likedislike'])
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# cast to datasets using only common columns
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features = Features({
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"label": ClassLabel(num_classes=3, names=["entailment", "neutral", "contradiction"]),
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"sentence1": Value(dtype="string", id=None),
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"sentence2": Value(dtype="string", id=None),
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"triple1": Sequence(feature=Value(dtype="string", id=None), length=3),
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"triple2": Sequence(feature=Value(dtype="string", id=None), length=3),
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"dtype": Value(dtype="string", id=None),
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"id": Value(dtype="string", id=None),
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"original_label": Value(dtype="string", id=None),
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})
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ds = {}
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for name, df in datasets.items():
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if "id" not in df.columns:
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df["id"] = ""
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ds[name] = Dataset.from_pandas(df.loc[:, list(features.keys())], features=features)
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ds = DatasetDict(ds)
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ds.push_to_hub("dialogue_nli", token="<token>")
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# check overlap between splits
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from itertools import combinations
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for i, j in combinations(ds.keys(), 2):
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print(
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f"{i} - {j}: ",
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pd.merge(
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ds[i].to_pandas(),
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ds[j].to_pandas(),
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on=["original_label", "sentence1", "sentence2"],
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how="inner",
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).shape[0],
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)
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#> train - dev: 58
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#> train - test: 98
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#> train - verified_test: 90
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#> train - extra_test: 0
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#> train - extra_dev: 0
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#> train - extra_train: 0
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#> train - valid_havenot: 0
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#> train - valid_attributes: 0
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#> train - valid_likedislike: 0
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#> dev - test: 19
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#> dev - verified_test: 19
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#> dev - extra_test: 0
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#> dev - extra_dev: 75
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#> dev - extra_train: 75
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#> dev - valid_havenot: 75
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#> dev - valid_attributes: 75
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#> dev - valid_likedislike: 75
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#> test - verified_test: 12524
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#> test - extra_test: 34
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#> test - extra_dev: 0
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#> test - extra_train: 0
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#> test - valid_havenot: 0
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#> test - valid_attributes: 0
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#> test - valid_likedislike: 0
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#> verified_test - extra_test: 29
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#> verified_test - extra_dev: 0
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#> verified_test - extra_train: 0
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#> verified_test - valid_havenot: 0
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#> verified_test - valid_attributes: 0
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#> verified_test - valid_likedislike: 0
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#> extra_test - extra_dev: 0
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#> extra_test - extra_train: 0
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#> extra_test - valid_havenot: 0
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#> extra_test - valid_attributes: 0
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#> extra_test - valid_likedislike: 0
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#> extra_dev - extra_train: 250946
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#> extra_dev - valid_havenot: 250946
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#> extra_dev - valid_attributes: 250946
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#> extra_dev - valid_likedislike: 250946
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#> extra_train - valid_havenot: 250946
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#> extra_train - valid_attributes: 250946
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#> extra_train - valid_likedislike: 250946
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#> valid_havenot - valid_attributes: 250946
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#> valid_havenot - valid_likedislike: 250946
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#> valid_attributes - valid_likedislike: 250946
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```
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