Update README.md

README.md

@@ -52,8 +52,8 @@ task_ids:
 - **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:** 62.3 MB
-- **Size of the generated dataset:** 145.8 MB
-- **Total amount of disk used:** 198.1 MB
+- **Size of the generated dataset:** 139.2 MB
+- **Total amount of disk used:** 201.5 MB
 ### Dataset Summary
 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 
@@ -67,14 +67,13 @@ 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)
-
 ### Languages
 The language in the dataset is English.
 ## Dataset Structure
 ### Data Instances
 - **Size of downloaded dataset files:** 62.3 MB
-- **Size of the generated dataset:** 145.8 MB
-- **Total amount of disk used:** 198.1 MB
+- **Size of the generated dataset:** 139.2 MB
+- **Total amount of disk used:** 201.5 MB
 
 An example of 'train' looks as follows:
 ```json
@@ -98,20 +97,20 @@ An example of 'train' looks as follows:
 ### Data Fields
 The data fields are the same among all splits.
 
-- `id`: the instance id of this sentence
-- `docid`: the TAC KBP document id of this sentence
-- `tokens`: the list of tokens of this sentence, obtained with the StanfordNLP toolkit.
-- `relation`: the relation label of this instance.
-- `subj_start`: the 0-based index of the start token of the relation subject mention.
-- `subj_end`: the 0-based index of the end token of the relation subject mention, exclusive.
-- `subj_type`: 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).
-- `obj_start`: the 0-based index of the start token of the relation object mention.
-- `obj_end`: the 0-based index of the end token of the relation object mention, exclusive.
-- `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).
-- `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).
-- `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).
-- `stanford_deprel`: the Stanford dependency relation tag per token.
-- `stanford_head`: the head (source) token index (0-based) for the dependency relation per token. The root token has a head index of -1.
+- `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.
+- `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.
+- `subj_type`: 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 `string` feature.
+- `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_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
 To miminize dataset bias, TACRED is stratified across years in which the TAC KBP challenge was run:
 |           | Train  |  Dev  | Test |