Update README.md

README.md

@@ -1,3 +1,182 @@
 ---
-license: other
+annotations_creators:
+- crowdsourced
+- expert-generated
+language:
+- en
+language_creators:
+- found
+license:
+- other
+multilinguality:
+- monolingual
+pretty_name: tacred
+size_categories:
+- 100K<n<1M
+source_datasets:
+- extended|other
+tags:
+- relation extraction
+task_categories:
+- text-classification
+task_ids:
+- multi-class-classification
 ---
+# Dataset Card for "tacred"
+## Table of Contents
+- [Table of Contents](#table-of-contents)
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+  - [Contributions](#contributions)
+## 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:** 62.3 MB
+- **Size of the generated dataset:** 145.8 MB
+- **Total amount of disk used:** 198.1 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 
+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/) 
+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:** 40.9 MB
+- **Size of the generated dataset:** 40.9 MB
+- **Total amount of disk used:** 40.9 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", "."],
+  "subj_start": 10,
+  "subj_end": 13, 
+  "obj_start": 0, 
+  "obj_end": 2,
+  "subj_type": "ORGANIZATION",
+  "obj_type": "PERSON"
+}
+```
+### Data Fields
+The data fields are the same among all splits.
+
+- `id`: the instance 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).
+### Data Splits
+To miminize dataset bias, TACRED is stratified across years in which the TAC KBP challenge was run:
+|           | Train  |  Dev  | Test |
+| -----     | ------ | ----- | ---- |
+| TACRED     | 68,124 (TAC KBP 2009-2012) | 22,631 (TAC KBP 2013) | 15,509 (TAC KBP 2014) |
+## Dataset Creation
+### Curation Rationale
+[More Information Needed]
+### Source Data
+#### Initial Data Collection and Normalization
+[More Information Needed]
+#### Who are the source language producers?
+[More Information Needed]
+### Annotations
+#### Annotation process
+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. 
+#### Who are the annotators?
+[More Information Needed]
+### Personal and Sensitive Information
+[More Information Needed]
+## Considerations for Using the Data
+### Social Impact of Dataset
+[More Information Needed]
+### Discussion of Biases
+[More Information Needed]
+### Other Known Limitations
+[More Information Needed]
+## Additional Information
+### 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). 
+If you are an LDC member, the access will be free; otherwise, an access fee of $25 is needed.
+### Citation Information
+The original dataset:
+```
+@inproceedings{zhang2017tacred,
+  author = {Zhang, Yuhao and Zhong, Victor and Chen, Danqi and Angeli, Gabor and Manning, Christopher D.},
+  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (EMNLP 2017)},
+  title = {Position-aware Attention and Supervised Data Improve Slot Filling},
+  url = {https://nlp.stanford.edu/pubs/zhang2017tacred.pdf},
+  pages = {35--45},
+  year = {2017}
+}
+```
+
+For the revised version, please also cite:
+```
+@inproceedings{alt-etal-2020-tacred,
+    title = "{TACRED} Revisited: A Thorough Evaluation of the {TACRED} Relation Extraction Task",
+    author = "Alt, Christoph  and
+      Gabryszak, Aleksandra  and
+      Hennig, Leonhard",
+    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
+    month = jul,
+    year = "2020",
+    address = "Online",
+    publisher = "Association for Computational Linguistics",
+    url = "https://www.aclweb.org/anthology/2020.acl-main.142",
+    doi = "10.18653/v1/2020.acl-main.142",
+    pages = "1558--1569",
+}
+```
+### Contributions
+#Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset.