system HF staff commited on
Commit
dee3cf8
1 Parent(s): 7a6289f

Update files from the datasets library (from 1.3.0)

Browse files

Release notes: https://github.com/huggingface/datasets/releases/tag/1.3.0

Files changed (1) hide show
  1. README.md +5 -0
README.md CHANGED
@@ -47,6 +47,7 @@ task_ids:
47
  - [Dataset Curators](#dataset-curators)
48
  - [Licensing Information](#licensing-information)
49
  - [Citation Information](#citation-information)
 
50
 
51
  ## Dataset Description
52
 
@@ -162,3 +163,7 @@ Self supervised (see paper)
162
  abstract = "Current state of the art systems in NLP heavily rely on manually annotated datasets, which are expensive to construct. Very little work adequately exploits unannotated data {--} such as discourse markers between sentences {--} mainly because of data sparseness and ineffective extraction methods. In the present work, we propose a method to automatically discover sentence pairs with relevant discourse markers, and apply it to massive amounts of data. Our resulting dataset contains 174 discourse markers with at least 10k examples each, even for rare markers such as {``}coincidentally{''} or {``}amazingly{''}. We use the resulting data as supervision for learning transferable sentence embeddings. In addition, we show that even though sentence representation learning through prediction of discourse marker yields state of the art results across different transfer tasks, it{'}s not clear that our models made use of the semantic relation between sentences, thus leaving room for further improvements.",
163
  }
164
  ```
 
 
 
 
47
  - [Dataset Curators](#dataset-curators)
48
  - [Licensing Information](#licensing-information)
49
  - [Citation Information](#citation-information)
50
+ - [Contributions](#contributions)
51
 
52
  ## Dataset Description
53
 
163
  abstract = "Current state of the art systems in NLP heavily rely on manually annotated datasets, which are expensive to construct. Very little work adequately exploits unannotated data {--} such as discourse markers between sentences {--} mainly because of data sparseness and ineffective extraction methods. In the present work, we propose a method to automatically discover sentence pairs with relevant discourse markers, and apply it to massive amounts of data. Our resulting dataset contains 174 discourse markers with at least 10k examples each, even for rare markers such as {``}coincidentally{''} or {``}amazingly{''}. We use the resulting data as supervision for learning transferable sentence embeddings. In addition, we show that even though sentence representation learning through prediction of discourse marker yields state of the art results across different transfer tasks, it{'}s not clear that our models made use of the semantic relation between sentences, thus leaving room for further improvements.",
164
  }
165
  ```
166
+
167
+ ### Contributions
168
+
169
+ Thanks to [@sileod](https://github.com/sileod) for adding this dataset.