Update README.md

README.md

@@ -10,7 +10,7 @@ license:
 - other
 multilinguality:
 - monolingual
-pretty_name: tacred_dfki
+pretty_name: multilingual_tacred
 size_categories:
 - 100K<n<1M
 source_datasets:
@@ -22,7 +22,7 @@ task_categories:
 task_ids:
 - multi-class-classification
 ---
-# Dataset Card for "tacred_dfki"
+# Dataset Card for "multilingual_tacred"
 ## Table of Contents
 - [Table of Contents](#table-of-contents)
 - [Dataset Description](#dataset-description)
@@ -50,27 +50,25 @@ task_ids:
 ## Dataset Description
 - **Homepage:** [https://nlp.stanford.edu/projects/tacred](https://nlp.stanford.edu/projects/tacred)
 - **Paper:** [Position-aware Attention and Supervised Data Improve Slot Filling](https://aclanthology.org/D17-1004/)
-- **Point of Contact:** See [https://nlp.stanford.edu/projects/tacred/](https://nlp.stanford.edu/projects/tacred/) 
-- **Size of downloaded dataset files:** 40.9 MB
+- **Point of Contact:** See [https://nlp.stanford.edu/projects/tacred/](https://nlp.stanford.edu/projects/tacred/)
+- **Size of downloaded dataset files:** 62.3 MB
 - **Size of the generated dataset:** 40.9 MB
-- **Total amount of disk used:** 40.9 MB
+- **Total amount of disk used:** 103.2 MB
 ### Dataset Summary
 Note: This dataset reader is intended for a JSONL version of the TACRED dataset that is used internally by DFKI SLT.
 We use the JSONL reader because it is more efficient as a generator than fully parsing the JSON files of the
 original TACRED dataset. You can find the TACRED dataset reader for the JSON version of
 the dataset [here](https://huggingface.co/datasets/DFKI-SLT/tacred).
 
-
-The TAC Relation Extraction Dataset (TACRED) is a large-scale relation extraction dataset with 106,264 examples built over newswire and web text from the corpus used in the yearly TAC Knowledge 
-Base Population (TAC KBP) challenges. Examples in TACRED cover 41 relation types as used in the TAC KBP challenges (e.g., per:schools_attended 
-and org:members) or are labeled as no_relation if no defined relation is held. These examples are created by combining available human annotations from the TAC 
+The TAC Relation Extraction Dataset (TACRED) is a large-scale relation extraction dataset with 106,264 examples built over newswire and web text from the corpus used in the yearly TAC Knowledge
+Base Population (TAC KBP) challenges. Examples in TACRED cover 41 relation types as used in the TAC KBP challenges (e.g., per:schools_attended
+and org:members) or are labeled as no_relation if no defined relation is held. These examples are created by combining available human annotations from the TAC
 KBP challenges and crowdsourcing. Please see our EMNLP paper, or our EMNLP slides for full details.
 
-Note: There is currently a label-corrected version of the TACRED dataset, which you should consider using instead of 
-the original version released in 2017. For more details on this new version, see the [TACRED Revisited paper](https://aclanthology.org/2020.acl-main.142/) 
+Note: There is currently a label-corrected version of the TACRED dataset, which you should consider using instead of
+the original version released in 2017. For more details on this new version, see the [TACRED Revisited paper](https://aclanthology.org/2020.acl-main.142/)
 published at ACL 2020.
 
-
 ### Supported Tasks and Leaderboards
 - **Tasks:** Relation Classification
 - **Leaderboards:** [https://paperswithcode.com/sota/relation-extraction-on-tacred](https://paperswithcode.com/sota/relation-extraction-on-tacred)
@@ -79,19 +77,19 @@ published at ACL 2020.
 The language in the dataset is English.
 ## Dataset Structure
 ### Data Instances
-- **Size of downloaded dataset files:** 40.9 MB
+- **Size of downloaded dataset files:** 62.3 MB
 - **Size of the generated dataset:** 40.9 MB
-- **Total amount of disk used:** 40.9 MB
+- **Total amount of disk used:** 103.2 MB
 
 An example of 'train' looks as follows:
 ```json
 {
   "id": "61b3a5c8c9a882dcfcd2",
   "relation": "org:founded_by",
-  "tokens": ["Tom", "Thabane", "resigned", "in", "October", "last", "year", "to", "form", "the", "All", "Basotho", "Convention", "-LRB-", "ABC", "-RRB-", ",", "crossing", "the", "floor", "with", "17", "members", "of", "parliament", ",", "causing", "constitutional", "monarch", "King", "Letsie", "III", "to", "dissolve", "parliament", "and", "call", "the", "snap", "election", "."],
+  "token": ["Tom", "Thabane", "resigned", "in", "October", "last", "year", "to", "form", "the", "All", "Basotho", "Convention", "-LRB-", "ABC", "-RRB-", ",", "crossing", "the", "floor", "with", "17", "members", "of", "parliament", ",", "causing", "constitutional", "monarch", "King", "Letsie", "III", "to", "dissolve", "parliament", "and", "call", "the", "snap", "election", "."],
   "subj_start": 10,
-  "subj_end": 13, 
-  "obj_start": 0, 
+  "subj_end": 13,
+  "obj_start": 0,
   "obj_end": 2,
   "subj_type": "ORGANIZATION",
   "obj_type": "PERSON"
@@ -124,11 +122,11 @@ To miminize dataset bias, TACRED is stratified across years in which the TAC KBP
 [More Information Needed]
 ### Annotations
 #### Annotation process
-See the Stanford paper and the Tacred Revisited paper, plus their appendices. 
+See the Stanford paper and the Tacred Revisited paper, plus their appendices.
 
-To ensure that models trained on TACRED are not biased towards predicting false positives on real-world text, 
-all sampled sentences where no relation was found between the mention pairs were fully annotated to be negative examples. As a result, 79.5% of the examples 
-are labeled as no_relation. 
+To ensure that models trained on TACRED are not biased towards predicting false positives on real-world text,
+all sampled sentences where no relation was found between the mention pairs were fully annotated to be negative examples. As a result, 79.5% of the examples
+are labeled as no_relation.
 #### Who are the annotators?
 [More Information Needed]
 ### Personal and Sensitive Information
@@ -144,9 +142,9 @@ are labeled as no_relation.
 ### Dataset Curators
 [More Information Needed]
 ### Licensing Information
-To respect the copyright of the underlying TAC KBP corpus, TACRED is released via the 
-Linguistic Data Consortium ([LDC License](https://catalog.ldc.upenn.edu/license/ldc-non-members-agreement.pdf)). 
-You can download TACRED from the [LDC TACRED webpage](https://catalog.ldc.upenn.edu/LDC2018T24). 
+To respect the copyright of the underlying TAC KBP corpus, TACRED is released via the
+Linguistic Data Consortium ([LDC License](https://catalog.ldc.upenn.edu/license/ldc-non-members-agreement.pdf)).
+You can download TACRED from the [LDC TACRED webpage](https://catalog.ldc.upenn.edu/LDC2018T24).
 If you are an LDC member, the access will be free; otherwise, an access fee of $25 is needed.
 ### Citation Information
 The original dataset:
@@ -179,4 +177,4 @@ For the revised version, please also cite:
 }
 ```
 ### Contributions
-#Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset.
+#Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset.