Update README.md

README.md

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 ---
 license: mit
+task_categories:
+- summarization
+language:
+- de
+tags:
+- wikipedia
+- wikidata
+- Relation Extraction
+- REBEL
+pretty_name: German REBEL Dataset
+size_categories:
+- 100K<n<1M
 ---
+
+# Dataset Card for German REBEL Dataset
+
+### Dataset Summary
+
+This dataset is the German version of Babelscape/rebel-dataset. It has been generated using [CROCODILE](https://github.com/Babelscape/crocodile).
+The Wikipedia Version is from November 2022. 
+
+### Languages
+
+- German
+
+## Dataset Structure
+
+```
+{"docid": "9400003",
+ "title": "Odin-Gletscher",
+ "uri": "Q7077818",
+ "text": "Der Odin-Gletscher ist ein kleiner Gletscher im ostantarktischen Viktorialand. Er fließt von den Westhängen des Mount Odin in der Asgard Range.\n\nDas New Zealand Antarctic Place-Names Committee benannte ihn in Anlehnung an die Benennung des Mount Odin nach Odin, Göttervater, Kriegs- und Totengott der nordischen Mythologie.",
+ "entities": [{"uri": "Q35666", "boundaries": [35, 44], "surfaceform": "Gletscher", "annotator": "Me"}, ... ],
+ "triples": [{"subject": {"uri": "Q7077818", "boundaries": [4, 18], "surfaceform": "Odin-Gletscher", "annotator": "Me"},
+              "predicate": {"uri": "P31", "boundaries": null, "surfaceform": "ist ein(e)", "annotator": "NoSubject-Triple-aligner"},
+              "object": {"uri": "Q35666", "boundaries": [35, 44], "surfaceform": "Gletscher", "annotator": "Me"}, "sentence_id": 0,
+              "dependency_path": null,
+              "confidence": 0.99560546875,
+              "annotator": "NoSubject-Triple-aligner"}, ...]
+}
+```
+
+### Data Instances
+
+
+### Data Fields
+
+"docid": "9644601",
+"title": Wikipedia Title 
+"uri": "Q4290759", 
+"text": Wikipedia Abstract 
+"entities": A list of Entities 
+   - uri: Wikidata URI
+   - boundaries: Tuple of indices of the entity in the abstract
+   - surfaceform: text form of entity
+   - annotator: different annotator classes  
+"triples": List of Triples as dictionaries
+  - sentence_id: Sentence number the triple appears in.
+  - "confidence": float, the confidence of the NLI Model
+  - subject
+    - uri: Wikidata Entity URI
+    - boundaries
+    - surfaceform
+    - annotator 
+  - predicate
+    - uri: Wikidata Relation URI
+    - boundaries: always null,
+    - surfaceform: Wikidata Relation Name
+    - annotator
+  - object:
+    - uri: Wikidata Entity URI
+    - boundaries
+    - surfaceform
+    - annotator 
+
+      
+
+### Data Splits
+
+No splits are provided for now since the relation classes are quite imbalanced.
+To read the dataset you can adapt the function provided by https://github.com/Babelscape/rebel
+
+```
+def _generate_examples(self, filepath):
+        """This function returns the examples in the raw (text) form."""
+        logging.info("generating examples from = %s", filepath)
+        relations_df = pd.read_csv(self.config.data_files['relations'], header = None, sep='\t')
+        relations = list(relations_df[0])
+
+        with open(filepath, encoding="utf-8") as f:
+            for id_, row in enumerate(f):
+                article = json.loads(row)
+                prev_len = 0
+                if len(article['triples']) == 0:
+                    continue
+                count = 0
+                for text_paragraph in article['text'].split('\n'):
+                    if len(text_paragraph) == 0:
+                        continue
+                    sentences = re.split(r'(?<=[.])\s', text_paragraph)
+                    text = ''
+                    for sentence in sentences:
+                        text += sentence + ' '
+                        if any([entity['boundaries'][0] < len(text) + prev_len < entity['boundaries'][1] for entity in article['entities']]):
+                            continue
+                        entities = sorted([entity for entity in article['entities'] if prev_len < entity['boundaries'][1] <= len(text)+prev_len], key=lambda tup: tup['boundaries'][0])
+                        decoder_output = '<triplet> '
+                        for int_ent, entity in enumerate(entities):
+                            triplets = sorted([triplet for triplet in article['triples'] if triplet['subject'] == entity and prev_len< triplet['subject']['boundaries'][1]<=len(text) + prev_len and prev_len< triplet['object']['boundaries'][1]<=len(text)+ prev_len and triplet['predicate']['surfaceform'] in relations], key=lambda tup: tup['object']['boundaries'][0])
+                            if len(triplets) == 0:
+                                continue
+                            decoder_output += entity['surfaceform'] + ' <subj> '
+                            for triplet in triplets:
+                                decoder_output += triplet['object']['surfaceform'] + ' <obj> '  + triplet['predicate']['surfaceform'] + ' <subj> '
+                            decoder_output = decoder_output[:-len(' <subj> ')]
+                            decoder_output += ' <triplet> '
+                        decoder_output = decoder_output[:-len(' <triplet> ')]
+                        count += 1
+                        prev_len += len(text)
+
+                        if len(decoder_output) == 0:
+                            text = ''
+                            continue
+
+                        text = re.sub('([\[\].,!?()])', r' \1 ', text.replace('()', ''))
+                        text = re.sub('\s{2,}', ' ', text)
+
+                        yield article['uri'] + '-' + str(count), {
+                            "title": article['title'],
+                            "context": text,
+                            "id": article['uri'] + '-' + str(count),
+                            "triplets": decoder_output,
+                        }
+                        text = ''
+``` 
+
+## Dataset Creation
+
+### Curation Rationale
+
+This dataset was created to enable the training of a german BART based model as pre-training phase for Relation Extraction.
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed]
+
+#### Who are the source language producers?
+
+Any Wikipedia and Wikidata contributor.
+
+### Annotations
+
+#### Annotation process
+
+The dataset extraction pipeline cRocoDiLe: Automatic Relation Extraction Dataset with NLI filtering.
+
+#### Who are the annotators?
+
+Automatic annottations
+
+### Personal and Sensitive Information
+
+All text is from Wikipedia, any Personal or Sensitive Information there may be present in this dataset.
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+The dataset serves as a pre-training step for Relation Extraction models. It is distantly annotated, hence it should only be used as such. A model trained solely on this dataset may produce allucinations coming from the silver nature of the dataset. 
+
+### Discussion of Biases
+
+Since the dataset was automatically created from Wikipedia and Wikidata, it may reflect the biases withing those sources.
+
+For Wikipedia text, see for example Dinan et al 2020 on biases in Wikipedia (esp. Table 1), or Blodgett et al 2020 for a more general discussion of the topic.
+
+For Wikidata, there are class imbalances, also resulting from Wikipedia.
+
+### Other Known Limitations
+
+Not for now
+
+## Additional Information
+
+### Dataset Curators
+
+Me
+
+### Licensing Information
+
+Since anyone can create the dataset on their own using the linked GitHub Repository, I am going to use the MIT Licence. 
+
+### Citation Information
+
+Inspiration by: 
+```
+@inproceedings{huguet-cabot-navigli-2021-rebel,
+title = "REBEL: Relation Extraction By End-to-end Language generation",
+author = "Huguet Cabot, Pere-Llu{\'\i}s  and
+    Navigli, Roberto",
+booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
+month = nov,
+year = "2021",
+address = "Online and in the Barceló Bávaro Convention Centre, Punta Cana, Dominican Republic",
+publisher = "Association for Computational Linguistics",
+url = "https://github.com/Babelscape/rebel/blob/main/docs/EMNLP_2021_REBEL__Camera_Ready_.pdf",
+}
+```
+
+### Contributions
+
+None for now