Update README.md

README.md

@@ -42,4 +42,117 @@ configs:
     path: data/validation-*
   - split: test
     path: data/test-*
+task_categories:
+- token-classification
+language:
+- en
+tags:
+- relation-extraction
+pretty_name: CoNLL04
+size_categories:
+- 1K<n<10K
 ---
+# Dataset Card for CoNLL04
+
+## Dataset Description
+
+- **Repository:** https://github.com/lavis-nlp/spert
+- **Paper:** https://aclanthology.org/W04-2401/
+- **Benchmark:** https://paperswithcode.com/sota/relation-extraction-on-conll04
+
+#### Dataset Summary
+
+<!-- Provide a quick summary of the dataset. -->
+
+The CoNLL04 dataset is a benchmark dataset used for relation extraction tasks. It contains 1,437 sentences, each of which has at least one relation. The sentences are annotated with information about entities and their corresponding relation types.
+The data in this repository was converted from ConLL04 format to JSONL format in https://github.com/lavis-nlp/spert/blob/master/scripts/conversion/convert_conll04.py
+The original data can be found here: https://cogcomp.seas.upenn.edu/page/resource_view/43
+
+The sentences in this dataset are tokenized and are annotated with entities (`Peop`, `Loc`, `Org`, `Other`) and relations (`Located_In`, `Work_For`, `OrgBased_In`, `Live_In`, `Kill`).
+Each sentence contains at least one active relation.
+
+### Languages
+
+The language in the dataset is English.
+
+
+## Dataset Structure
+
+<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
+
+### Dataset Instances
+
+An example of 'train' looks as follows:
+```json
+{
+  "tokens": ["Newspaper", "`", "Explains", "'", "U.S.", "Interests", "Section", "Events", "FL1402001894", "Havana", "Radio", "Reloj", "Network", "in", "Spanish", "2100", "GMT", "13", "Feb", "94"],
+  "entities": [
+    {"type": "Loc", "start": 4, "end": 5},
+    {"type": "Loc", "start": 9, "end": 10},
+    {"type": "Org", "start": 10, "end": 13},
+    {"type": "Other", "start": 15, "end": 17},
+    {"type": "Other", "start": 17, "end": 20}
+  ],
+  "relations": [
+    {"type": "OrgBased_In", "head": 2, "tail": 1}
+  ],
+  "orig_id": 3255
+}
+```
+
+### Data Fields
+
+- `tokens`: the text of this example, a `string` feature.
+- `entities`: list of entities
+    - `type`: entity type, a `string` feature.
+    - `start`: start token index of entity, a `int32` feature.
+    - `end`: exclusive end token index of entity, a `int32` feature.
+- `relations`: list of relations
+    - `type`: relation type, a `string` feature.
+    - `head`: index of head entity, a `int32` feature.
+    - `tail`: index of tail entity, a `int32` feature.
+
+
+## Citation
+
+<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+```
+@inproceedings{roth-yih-2004-linear,
+    title = "A Linear Programming Formulation for Global Inference in Natural Language Tasks",
+    author = "Roth, Dan  and
+      Yih, Wen-tau",
+    booktitle = "Proceedings of the Eighth Conference on Computational Natural Language Learning ({C}o{NLL}-2004) at {HLT}-{NAACL} 2004",
+    month = may # " 6 - " # may # " 7",
+    year = "2004",
+    address = "Boston, Massachusetts, USA",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/W04-2401",
+    pages = "1--8",
+}
+@article{eberts-ulges2019spert,
+  author       = {Markus Eberts and
+                  Adrian Ulges},
+  title        = {Span-based Joint Entity and Relation Extraction with Transformer Pre-training},
+  journal      = {CoRR},
+  volume       = {abs/1909.07755},
+  year         = {2019},
+  url          = {http://arxiv.org/abs/1909.07755},
+  eprinttype    = {arXiv},
+  eprint       = {1909.07755},
+  timestamp    = {Mon, 23 Sep 2019 18:07:15 +0200},
+  biburl       = {https://dblp.org/rec/journals/corr/abs-1909-07755.bib},
+  bibsource    = {dblp computer science bibliography, https://dblp.org}
+}
+```
+
+**APA:**
+
+Roth, D., & Yih, W. (2004). A linear programming formulation for global inference in natural language tasks. In Proceedings of the Eighth Conference on Computational Natural Language Learning (CoNLL-2004) at HLT-NAACL 2004 (pp. 1-8). Boston, Massachusetts, USA: Association for Computational Linguistics. https://aclanthology.org/W04-2401
+Eberts, M., & Ulges, A. (2019). Span-based joint entity and relation extraction with transformer pre-training. CoRR, abs/1909.07755. http://arxiv.org/abs/1909.07755
+
+## Dataset Card Authors
+
+[@phucdev](https://github.com/phucdev)