Update README.md

README.md

@@ -46,145 +46,28 @@ Git repository for software associated with the 2016 ACL paper "Identifying Caus
 Disclaimer: The team releasing altlex did not upload the dataset to the Hub and did not write a dataset card.
 These steps were done by the Hugging Face team.
 
-### Supported Tasks and Leaderboards
+### Supported Tasks
 
-[More Information Needed](https://github.com/chridey/altlex)
+- [Sentence Transformers](https://huggingface.co/sentence-transformers) training. 
 
 ### Languages
 
-[More Information Needed](https://github.com/chridey/altlex)
-
-## Dataset Structure
-
-Parallel Wikipedia Format
-
-This is a gzipped, JSON-formatted file. The "titles" array is the shared title name of the English and Simple Wikipedia articles. 
-The "articles" array consists of two arrays and each of those arrays must be the same length as the "titles" array and 
-the indices into these arrays must point to the aligned articles and titles. 
-Each article within the articles array is an array of tokenized sentence strings (but not word tokenized).
-
-The format of the dictionary is as follows:
-
-```
-{"files": [english_name, simple_name],
- "articles": [
-              [[article_1_sentence_1_string, article_1_sentence_2_string, ...],
-               [article_2_sentence_1_string, article_2_sentence_2_string, ...],
-               ...
-              ],
-              [[article_1_sentence_1_string, article_1_sentence_2_string, ...],
-               [article_2_sentence_1_string, article_2_sentence_2_string, ...],
-               ...
-              ]
-             ],
-  "titles": [title_1_string, title_2_string, ...]
-}
-
-```
-
-Parsed Wikipedia Format
-
-This is a gzipped, JSON-formatted list of parsed Wikipedia article pairs. 
-The list stored at 'sentences' is of length 2 and stores each version
-of the English and Wikipedia article with the same title.
-
-The data is formatted as follows:
-
-```
-[
- {
-  "index": article_index,
-  "title": article_title_string,
-  "sentences": [[parsed_sentence_1, parsed_sentence_2, ...],
-                [parsed_sentence_1, parsed_sentence_2, ...]
-               ]
- },
- ...
-]
-
-```
-
-Parsed Pairs Format
+- English.
 
-This is a gzipped, JSON-formatted list of parsed sentences. Paraphrase pairs are consecutive 
-even and odd indices. For the parsed sentence, see "Parsed Sentence Format."
+## Dataset Structure: Equivalent sentence pairs.
 
-The data is formatted as follows:
+Each example in the dataset contains a pair of equivalent sentences and is formated as a dictionary:
 
 ```
-[
-  ...,
-  parsed_sentence_2,
-  parsed_sentence_3,
-  ...
-]
-
-```
-
-Parsed Sentence Format
-
-Each parsed sentence is of the following format:
-
-```
-{
-   "dep": [[[governor_index, dependent_index, relation_string], ...], ...], 
-   "lemmas": [[lemma_1_string, lemma_2_string, ...], ...],
-   "pos": [[pos_1_string, pos_2_string, ...], ...],
-   "parse": [parenthesized_parse_1_string, ...], 
-   "words": [[word_1_string, word_2_string, ...], ...] , 
-   "ner": [[ner_1_string, ner_2_string, ...], ...]
-}
-
-```
-
-Feature Extractor Config Format
-
-```
-{"framenetSettings": 
-   {"binary": true/false}, 
- "featureSettings": 
-   {
-   "arguments_cat_curr": true/false, 
-   "arguments_verbnet_prev": true/false, 
-   "head_word_cat_curr": true/false, 
-   "head_word_verbnet_prev": true/false, 
-   "head_word_verbnet_altlex": true/false, 
-   "head_word_cat_prev": true/false, 
-   "head_word_cat_altlex": true/false, 
-   "kld_score": true/false, 
-   "head_word_verbnet_curr": true/false, 
-   "arguments_verbnet_curr": true/false, 
-   "framenet": true/false, 
-   "arguments_cat_prev": true/false, 
-   "connective": true/false
-   }, 
- "kldSettings": 
-   {"kldDir": $kld_name}
-}
-
+{"set": [sentence_1, sentence_2]}
+{"set": [sentence_1, sentence_2]}
+...
+{"set": [sentence_1, sentence_2]}
 ```
 
-Data Point Format
+This dataset is useful for training Sentence Transformers models. Refer to the following post on how to train models using similar pairs of sentences.
 
-It is also possible to run the feature extractor directly on a single data point. 
-From the featureExtraction module create a FeatureExtractor object and call the method addFeatures
-on a DataPoint object (note that this does not create any interaction features,
-for that you will also need to call makeInteractionFeatures). 
-The DataPoint class takes a dictionary as input, in the following format:
-
-```
-{
-"sentences": {[{"ner": [...], "pos": [...], "words": [...], "stems": [...], "lemmas": [...], "dependencies": [...]}, {...}]}
-"altlexLength": integer,
-"altlex": {"dependencies": [...]}
-}
-The sentences list is the pair of sentences/spans where the first span begins with the altlex. Dependencies must be a list where at index i there is a dependency relation string and governor index integer or a NoneType. Index i into the words list is the dependent of this relation. To split single sentence dependency relations, use the function splitDependencies in utils.dependencyUtils.
 
-```
-
-### Curation Rationale
-
-[More Information Needed](https://github.com/chridey/altlex)
 
 ### Source Data
 
@@ -238,6 +121,6 @@ The sentences list is the pair of sentences/spans where the first span begins wi
 
 ### Contributions
 
-Thanks to [@chridey](https://github.com/chridey/altlex/commits?author=chridey) for adding this dataset.
+- [@chridey](https://github.com/chridey/altlex/commits?author=chridey) for adding this dataset to Github.
 
 ---