Update swiss judgment prediction (#5019)

* updated swiss_judgment_prediction dataset with new data

* fixed some problems

* Update datasets/swiss_judgment_prediction/README.md

Co-authored-by: Albert Villanova del Moral <8515462+albertvillanova@users.noreply.github.com>

* simplified code

* ran make style

* simplified code

* updated dummy data and dataset card and simplified code

* added dummy_data and updated dataset_infos.json

* removed unnecessary variable

Co-authored-by: Albert Villanova del Moral <8515462+albertvillanova@users.noreply.github.com>

Commit from https://github.com/huggingface/datasets/commit/9ff1278226ee2f4239e3d78104dacb9851fce7c4

README.md

@@ -8,6 +8,7 @@ language:
 - de 
 - fr
 - it
+- en
 license:
 - cc-by-sa-4.0
 multilinguality:
@@ -74,6 +75,8 @@ Switzerland has four official languages with 3 languages (German, French and Ita
 
 ## Dataset Structure
 
+In version 2 we added machine translated data using [EasyNMT](https://github.com/UKPLab/EasyNMT) for all documents into German, French, Italian and English as an additional training set.
+
 ### Data Instances
 
 **Multilingual use of the dataset**
@@ -124,7 +127,7 @@ dataset = load_dataset('swiss_judgment_prediction', 'de')
 
 **Multilingual use of the dataset**
 
-The following data fields are provided for documents (`train`, `dev`, `test`):
+The following data fields are provided for documents (`train`, `validation`, `test`):
 
 `id`: (**int**) a unique identifier of the for the document \
 `year`: (**int**) the publication year \
@@ -139,7 +142,7 @@ The following data fields are provided for documents (`train`, `dev`, `test`):
 
 **Monolingual use of the dataset**
 
-The following data fields are provided for documents (`train`, `dev`, `test`):
+The following data fields are provided for documents (`train`, `validation`, `test`):
 
 `id`: (**int**) a unique identifier of the for the document \
 `year`: (**int**) the publication year \
@@ -153,11 +156,16 @@ The following data fields are provided for documents (`train`, `dev`, `test`):
 
 ### Data Splits
 
-| Language |  ISO code | Number of Documents (Training/Dev/Test) | 
-| ---- | ---- |  ---- |  
-| German    | **de**   | 35'452 / 4'705 / 9'725
-| French    | **fr**   | 21'179 / 3'095 / 6'820
-Italian     | **it**   | 3'072 / 408 / 812
+| Language   | Subset     | Number of Documents (Training/Validation/Test) | 
+|------------|------------|------------------------------------------------|  
+| German     | **de**     | 35'452 / 4'705 / 9'725                         |
+| French     | **fr**     | 21'179 / 3'095 / 6'820                         |
+| Italian    | **it**     | 3'072 / 408 / 812                              |
+| All        | **all**    | 59'709 / 8'208 / 17'357                        |
+| MT German  | **mt_de**  | 24'251 / 0 / 0                                 |
+| MT French  | **mt_fr**  | 38'524 / 0 / 0                                 |
+| MT Italian | **mt_it**  | 56'631 / 0 / 0                                 |
+| MT All     | **all+mt** | 238'818 / 8'208 / 17'357                       |
 
 ## Dataset Creation
 

dataset_infos.json

@@ -1 +1 @@
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+{"de": {"description": "\nSwiss-Judgment-Prediction is a multilingual, diachronic dataset of 85K Swiss Federal Supreme Court (FSCS) cases annotated with the respective binarized judgment outcome (approval/dismissal), posing a challenging text classification task. We also provide additional metadata, i.e., the publication year, the legal area and the canton of origin per case, to promote robustness and fairness studies on the critical area of legal NLP.\n", "citation": "@InProceedings{niklaus-etal-2021-swiss,\n  author = {Niklaus, Joel\n                and Chalkidis, Ilias\n                and St\u00fcrmer, Matthias},\n  title = {Swiss-Court-Predict: A Multilingual Legal Judgment Prediction Benchmark},\n  booktitle = {Proceedings of the 2021 Natural Legal Language Processing Workshop},\n  year = {2021},\n  location = {Punta Cana, Dominican Republic},\n}", "homepage": "https://github.com/JoelNiklaus/SwissCourtRulingCorpus", "license": "", "features": {"id": {"dtype": "int32", "id": null, "_type": "Value"}, "year": {"dtype": "int32", "id": null, "_type": "Value"}, "text": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"num_classes": 2, "names": ["dismissal", "approval"], "id": null, "_type": "ClassLabel"}, "language": {"dtype": "string", "id": null, "_type": "Value"}, "region": {"dtype": "string", "id": null, "_type": "Value"}, "canton": {"dtype": "string", "id": null, "_type": "Value"}, "legal area": {"dtype": "string", "id": null, "_type": "Value"}, "source_language": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "swiss_judgment_prediction", "config_name": "de", "version": {"version_str": "2.0.0", "description": "", "major": 2, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 104270719, "num_examples": 35458, "dataset_name": "swiss_judgment_prediction"}, "validation": {"name": "validation", "num_bytes": 12131878, "num_examples": 4705, "dataset_name": "swiss_judgment_prediction"}, "test": {"name": "test", "num_bytes": 26056177, "num_examples": 9725, "dataset_name": "swiss_judgment_prediction"}}, "download_checksums": {"https://zenodo.org/record/7109926/files/train.jsonl": {"num_bytes": 234401262, "checksum": "191992c204ad10d76c2a08005f1cdd94531b531ba12f5ea889cec7cd94dbb232"}, "https://zenodo.org/record/7109926/files/train_mt.jsonl": {"num_bytes": 667946800, "checksum": "f57b8998acba9ac4b06fef7bcb03b8718da7b1d61228efb06afb8088f057d80c"}, "https://zenodo.org/record/7109926/files/val.jsonl": {"num_bytes": 29157311, "checksum": "a395f523de0e536953ac3af7960243b7be90423183097a1856275fc694b6e415"}, "https://zenodo.org/record/7109926/files/test.jsonl": {"num_bytes": 68876958, "checksum": "3e7a6542cd061579599cd93ec4c3af48171f0f8c2331c81477e77fac253247c1"}}, "download_size": 1000382331, "post_processing_size": null, "dataset_size": 142458774, "size_in_bytes": 1142841105}, "fr": {"description": "\nSwiss-Judgment-Prediction is a multilingual, diachronic dataset of 85K Swiss Federal Supreme Court (FSCS) cases annotated with the respective binarized judgment outcome (approval/dismissal), posing a challenging text classification task. 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+oid sha256:40781150ee4cbf7b759d75e7061f1ff889c9e3e2f7f971fc0b5c8e70e00726ba
+size 27112

dummy/{de/1.0.0 → all/2.0.0}/dummy_data.zip

@@ -1,3 +1,3 @@
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-oid sha256:e5fd6b04e5c9804664357d6929f8db046b61ce4e578ffa9dfe5dae8d501a6eb1
-size 5283
+oid sha256:0a4891a32a28da27c63678dbec7be568f7228102e3879ed3820ca1d1172de418
+size 27112

dummy/{fr/1.0.0 → de/2.0.0}/dummy_data.zip

@@ -1,3 +1,3 @@
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-oid sha256:e4081dbe49cc10caf72a9285274c12fa0b4088914c90e4149f5451230e521974
-size 5130
+oid sha256:07f5c330d7741fb29f4ad77d41489a6b494b540a4a5acf7473fc37c199e3aaaa
+size 27112

dummy/{it/1.0.0 → fr/2.0.0}/dummy_data.zip

@@ -1,3 +1,3 @@
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-oid sha256:05769bf13b52f08f34d87f2dc9f7c3ae7ab350e91f1449258db8269d31c2a667
-size 3453
+oid sha256:799d02d1d694728dd61ab86ff71eff43b3315a9052ed3251cbfd79d83767dba5
+size 27112

dummy/it/2.0.0/dummy_data.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4c17d49f768c7ed3297222632eb0a790dea79549c8a69f4479a5d130633443b6
+size 27112

dummy/mt_de/2.0.0/dummy_data.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f5e321a06ede89b1c0ce46e78fd977e82f45da245de949a3aca91471bcd4ab83
+size 27112

dummy/mt_en/2.0.0/dummy_data.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f3eb14d3ce1b7edd06f1ad6fecdcf4ce08f2b3d2b0df6e5ef680e3c8616aa4d7
+size 27112

dummy/mt_fr/2.0.0/dummy_data.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2d40c4f36ba333b14a8666f4949905eae044ab00c192dc090398fc4ac92df74f
+size 27112

dummy/mt_it/2.0.0/dummy_data.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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+size 27112

swiss_judgment_prediction.py

@@ -36,58 +36,68 @@ _DESCRIPTION = """
 Swiss-Judgment-Prediction is a multilingual, diachronic dataset of 85K Swiss Federal Supreme Court (FSCS) cases annotated with the respective binarized judgment outcome (approval/dismissal), posing a challenging text classification task. We also provide additional metadata, i.e., the publication year, the legal area and the canton of origin per case, to promote robustness and fairness studies on the critical area of legal NLP.
 """
 
-_LANGUAGES = [
+_ORIGINAL_LANGUAGES = [
     "de",
     "fr",
     "it",
 ]
+_MT_LANGUAGES = [
+    "mt_de",
+    "mt_fr",
+    "mt_it",
+    "mt_en",
+]
+_LANGUAGES = _ORIGINAL_LANGUAGES + _MT_LANGUAGES
 
-_URL = "https://zenodo.org/record/5529712/files/"
+_URL = "https://zenodo.org/record/7109926/files/"
 _URLS = {
     "train": _URL + "train.jsonl",
-    "test": _URL + "test.jsonl",
+    "train_mt": _URL + "train_mt.jsonl",
     "val": _URL + "val.jsonl",
+    "test": _URL + "test.jsonl",
 }
 
 
 class SwissJudgmentPredictionConfig(datasets.BuilderConfig):
     """BuilderConfig for SwissJudgmentPrediction."""
 
-    def __init__(self, language: str, languages=None, **kwargs):
+    def __init__(self, language: str, **kwargs):
         """BuilderConfig for SwissJudgmentPrediction.
 
         Args:
-        language: One of de,fr,it, or all_languages
+        language: One of de, fr, it, or all, or all+mt
           **kwargs: keyword arguments forwarded to super.
         """
         super(SwissJudgmentPredictionConfig, self).__init__(**kwargs)
         self.language = language
-        if language != "all_languages":
-            self.languages = [language]
-        else:
-            self.languages = languages if languages is not None else _LANGUAGES
 
 
 class SwissJudgmentPrediction(datasets.GeneratorBasedBuilder):
     """SwissJudgmentPrediction: A Multilingual Legal Judgment PredictionBenchmark"""
 
-    VERSION = datasets.Version("1.0.0", "")
+    VERSION = datasets.Version("2.0.0", "")
     BUILDER_CONFIG_CLASS = SwissJudgmentPredictionConfig
     BUILDER_CONFIGS = [
         SwissJudgmentPredictionConfig(
             name=lang,
             language=lang,
-            version=datasets.Version("1.0.0", ""),
+            version=datasets.Version("2.0.0", ""),
             description=f"Plain text import of SwissJudgmentPrediction for the {lang} language",
         )
         for lang in _LANGUAGES
     ] + [
         SwissJudgmentPredictionConfig(
-            name="all_languages",
-            language="all_languages",
-            version=datasets.Version("1.0.0", ""),
+            name="all",
+            language="all",
+            version=datasets.Version("2.0.0", ""),
             description="Plain text import of SwissJudgmentPrediction for all languages",
-        )
+        ),
+        SwissJudgmentPredictionConfig(
+            name="all+mt",
+            language="all+mt",
+            version=datasets.Version("2.0.0", ""),
+            description="Plain text import of SwissJudgmentPrediction for all languages with machine translation",
+        ),
     ]
 
     def _info(self):
@@ -101,6 +111,7 @@ class SwissJudgmentPrediction(datasets.GeneratorBasedBuilder):
                 "region": datasets.Value("string"),
                 "canton": datasets.Value("string"),
                 "legal area": datasets.Value("string"),
+                "source_language": datasets.Value("string"),
             }
         )
         return datasets.DatasetInfo(
@@ -114,61 +125,48 @@ class SwissJudgmentPrediction(datasets.GeneratorBasedBuilder):
     def _split_generators(self, dl_manager):
         # dl_manager is a datasets.download.DownloadManager that can be used to
         # download and extract URLs
-        urls_to_dl = _URLS
         try:
-            dl_dir = dl_manager.download_and_extract(urls_to_dl)
+            dl_dir = dl_manager.download(_URLS)
         except Exception:
             logger.warning(
-                "This dataset is downloaded through Zenodo which is flaky. If this download failed try a few times before reporting an issue"
+                "This dataset is downloaded through Zenodo which is flaky. "
+                "If this download failed try a few times before reporting an issue"
             )
             raise
         return [
             datasets.SplitGenerator(
                 name=datasets.Split.TRAIN,
                 # These kwargs will be passed to _generate_examples
-                gen_kwargs={"filepath": dl_dir["train"], "split": "train"},
+                gen_kwargs={"filepath": dl_dir["train"], "mt_filepath": dl_dir["train_mt"]},
             ),
             datasets.SplitGenerator(
-                name=datasets.Split.TEST,
+                name=datasets.Split.VALIDATION,
                 # These kwargs will be passed to _generate_examples
-                gen_kwargs={"filepath": dl_dir["test"], "split": "test"},
+                gen_kwargs={"filepath": dl_dir["val"], "mt_filepath": None},
             ),
             datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
+                name=datasets.Split.TEST,
                 # These kwargs will be passed to _generate_examples
-                gen_kwargs={"filepath": dl_dir["val"], "split": "dev"},
+                gen_kwargs={"filepath": dl_dir["test"], "mt_filepath": None},
             ),
         ]
 
-    def _generate_examples(self, filepath, split):
+    def _generate_examples(self, filepath, mt_filepath):
         """This function returns the examples in the raw (text) form."""
-
-        if self.config.language == "all_languages":
-            with open(filepath, encoding="utf-8") as f:
-                for id_, row in enumerate(f):
-                    data = json.loads(row)
-                    yield id_, {
-                        "id": data["id"],
-                        "year": data["year"],
-                        "text": data["text"],
-                        "label": data["label"],
-                        "language": data["language"],
-                        "region": data["region"],
-                        "canton": data["canton"],
-                        "legal area": data["legal area"],
-                    }
-        else:
+        if self.config.language in ["all", "all+mt"] + _ORIGINAL_LANGUAGES:
             with open(filepath, encoding="utf-8") as f:
                 for id_, row in enumerate(f):
                     data = json.loads(row)
-                    if data["language"] == self.config.language:
-                        yield id_, {
-                            "id": data["id"],
-                            "year": data["year"],
-                            "text": data["text"],
-                            "label": data["label"],
-                            "language": data["language"],
-                            "region": data["region"],
-                            "canton": data["canton"],
-                            "legal area": data["legal area"],
-                        }
+                    _ = data.setdefault("source_language", "n/a")
+                    if self.config.language in ["all", "all+mt"] or data["language"] == self.config.language:
+                        yield id_, data
+        if self.config.language in ["all+mt"] + _MT_LANGUAGES:
+            if mt_filepath:  # yield data from mt_filepath
+                with open(mt_filepath, encoding="utf-8") as f:
+                    for id_, row in enumerate(f):
+                        data = json.loads(row)
+                        _ = data.setdefault("source_language", "n/a")
+                        if (
+                            self.config.language == "all+mt" or data["language"] in self.config.language
+                        ):  # "de" in "mt_de"
+                            yield f"mt_{id_}", data