Update README.md

README.md

@@ -10,7 +10,7 @@ license:
 - other
 multilinguality:
 - monolingual
-pretty_name: The TAC Relation Extraction Dataset
+pretty_name: tacred
 size_categories:
 - 100K<n<1M
 source_datasets:
@@ -50,7 +50,7 @@ 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/) 
+- **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:** 139.2 MB
 - **Total amount of disk used:** 201.5 MB
@@ -84,8 +84,8 @@ An example of 'train' looks as follows:
   "relation": "org:founded_by",
   "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",
@@ -100,7 +100,7 @@ The data fields are the same among all splits.
 
 - `id`: the instance id of this sentence, a `string` feature.
 - `docid`: the TAC KBP document id of this sentence, a `string` feature.
-- `tokens`: the list of tokens of this sentence, obtained with the StanfordNLP toolkit, a `list` of `string` features.
+- `token`: the list of tokens of this sentence, obtained with the StanfordNLP toolkit, a `list` of `string` features.
 - `relation`: the relation label of this instance, a `string` classification label.
 - `subj_start`: the 0-based index of the start token of the relation subject mention, an `ìnt` feature.
 - `subj_end`: the 0-based index of the end token of the relation subject mention, exclusive, an `ìnt` feature.
@@ -108,8 +108,8 @@ The data fields are the same among all splits.
 - `obj_start`: the 0-based index of the start token of the relation object mention, an `ìnt` feature.
 - `obj_end`: the 0-based index of the end token of the relation object mention, exclusive, an `ìnt` feature.
 - `obj_type`: the NER type of the object mention, among 23 fine-grained types used in the [Stanford NER system](https://stanfordnlp.github.io/CoreNLP/ner.html), a `string` feature.
-- `pos_tags`: the part-of-speech tag per token. the NER type of the subject mention, among 23 fine-grained types used in the [Stanford NER system](https://stanfordnlp.github.io/CoreNLP/ner.html), a `list` of `string` features.
-- `ner_tags`: the NER tags of tokens (IO-Scheme), among 23 fine-grained types used in the [Stanford NER system](https://stanfordnlp.github.io/CoreNLP/ner.html), a `list` of `string` features.
+- `stanford_pos`: the part-of-speech tag per token. the NER type of the subject mention, among 23 fine-grained types used in the [Stanford NER system](https://stanfordnlp.github.io/CoreNLP/ner.html), a `list` of `string` features.
+- `stanford_ner`: the NER tags of tokens (IO-Scheme), among 23 fine-grained types used in the [Stanford NER system](https://stanfordnlp.github.io/CoreNLP/ner.html), a `list` of `string` features.
 - `stanford_deprel`: the Stanford dependency relation tag per token, a `list` of `string` features.
 - `stanford_head`: the head (source) token index (0-based) for the dependency relation per token. The root token has a head index of -1, a `list` of `int` features.
 ### Data Splits
@@ -127,11 +127,11 @@ 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
@@ -147,9 +147,9 @@ 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:
@@ -182,4 +182,4 @@ 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.