| ## Overview | |
| Original dataset is available on the HuggingFace Hub [here](https://huggingface.co/datasets/scitail). | |
| ## Dataset curation | |
| This is the same as the `snli_format` split of the SciTail dataset available on the HuggingFace Hub (i.e., same data, same splits, etc). | |
| The only differences are the following: | |
| - selecting only the columns `["sentence1", "sentence2", "gold_label", "label"]` | |
| - renaming columns with the following mapping `{"sentence1": "premise", "sentence2": "hypothesis"}` | |
| - creating a new column "label" from "gold_label" with the following mapping `{"entailment": "entailment", "neutral": "not_entailment"}` | |
| - encoding labels with the following mapping `{"not_entailment": 0, "entailment": 1}` | |
| Note that there are 10 overlapping instances (as found by merging on columns "label", "premise", and "hypothesis") between | |
| `train` and `test` splits. | |
| ## Code to create the dataset | |
| ```python | |
| from datasets import Features, Value, ClassLabel, Dataset, DatasetDict, load_dataset | |
| # load datasets from the Hub | |
| dd = load_dataset("scitail", "snli_format") | |
| ds = {} | |
| for name, df_ in dd.items(): | |
| df = df_.to_pandas() | |
| # select important columns | |
| df = df[["sentence1", "sentence2", "gold_label"]] | |
| # rename columns | |
| df = df.rename(columns={"sentence1": "premise", "sentence2": "hypothesis"}) | |
| # encode labels | |
| df["label"] = df["gold_label"].map({"entailment": "entailment", "neutral": "not_entailment"}) | |
| df["label"] = df["label"].map({"not_entailment": 0, "entailment": 1}) | |
| # cast to dataset | |
| features = Features({ | |
| "premise": Value(dtype="string", id=None), | |
| "hypothesis": Value(dtype="string", id=None), | |
| "label": ClassLabel(num_classes=2, names=["not_entailment", "entailment"]), | |
| }) | |
| ds[name] = Dataset.from_pandas(df, features=features) | |
| dataset = DatasetDict(ds) | |
| dataset.push_to_hub("scitail", token="<token>") | |
| # check overlap between splits | |
| from itertools import combinations | |
| for i, j in combinations(dataset.keys(), 2): | |
| print( | |
| f"{i} - {j}: ", | |
| pd.merge( | |
| dataset[i].to_pandas(), | |
| dataset[j].to_pandas(), | |
| on=["label", "premise", "hypothesis"], | |
| how="inner", | |
| ).shape[0], | |
| ) | |
| #> train - test: 10 | |
| #> train - validation: 0 | |
| #> test - validation: 0 | |
| ``` |