Update files from the datasets library (from 1.6.1)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.6.1

README.md

@@ -1,4 +1,68 @@
 ---
+annotations_creators:
+- unknown
+language_creators:
+- unknown
+languages:
+- en
+licenses:
+- cc-by-4-0
+multilinguality:
+- monolingual
+size_categories:
+- 10K<n<100K
+source_datasets:
+- unknown
+task_categories:
+  ax:
+  - text-classification
+  cola:
+  - text-classification
+  mnli:
+  - text-classification
+  mnli_matched:
+  - text-classification
+  mnli_mismatched:
+  - text-classification
+  mrpc:
+  - text-classification
+  qnli:
+  - text-classification
+  qqp:
+  - text-classification
+  rte:
+  - text-classification
+  sst2:
+  - text-classification
+  stsb:
+  - text-scoring
+  wnli:
+  - text-classification
+task_ids:
+  ax:
+  - natural-language-inference
+  cola:
+  - acceptability-classification
+  mnli:
+  - natural-language-inference
+  mnli_matched:
+  - natural-language-inference
+  mnli_mismatched:
+  - natural-language-inference
+  mrpc:
+  - text-classification-other-paraphrase-identification
+  qnli:
+  - text-classification-other-qa-nli
+  qqp:
+  - text-classification-other-paraphrase-identification
+  rte:
+  - natural-language-inference
+  sst2:
+  - sentiment-classification
+  stsb:
+  - semantic-similarity-scoring
+  wnli:
+  - text-classification-other-coreference-nli
 ---
 
 # Dataset Card for "glue"
@@ -39,9 +103,55 @@
 
 ### Dataset Summary
 
-GLUE, the General Language Understanding Evaluation benchmark
-(https://gluebenchmark.com/) is a collection of resources for training,
-evaluating, and analyzing natural language understanding systems.
+GLUE, the General Language Understanding Evaluation benchmark (https://gluebenchmark.com/) is a collection of resources for training, evaluating, and analyzing natural language understanding systems.
+
+#### ax
+
+A manually-curated evaluation dataset for fine-grained analysis of system performance on a broad range of linguistic phenomena. This dataset evaluates sentence understanding through Natural Language Inference (NLI) problems. Use a model trained on MulitNLI to produce predictions for this dataset.
+
+#### cola
+
+The Corpus of Linguistic Acceptability consists of English acceptability judgments drawn from books and journal articles on linguistic theory. Each example is a sequence of words annotated with whether it is a grammatical English sentence.
+
+#### mnli
+
+The Multi-Genre Natural Language Inference Corpus is a crowdsourced collection of sentence pairs with textual entailment annotations. Given a premise sentence and a hypothesis sentence, the task is to predict whether the premise entails the hypothesis (entailment), contradicts the hypothesis (contradiction), or neither (neutral). The premise sentences are gathered from ten different sources, including transcribed speech, fiction, and government reports. The authors of the benchmark use the standard test set, for which they obtained private labels from the RTE authors, and evaluate on both the matched (in-domain) and mismatched (cross-domain) section. They also uses and recommend the SNLI corpus as 550k examples of auxiliary training data.
+
+#### mnli_matched
+
+The matched validation and test splits from MNLI. See the "mnli" BuilderConfig for additional information.
+
+#### mnli_mismatched
+
+The mismatched validation and test splits from MNLI. See the "mnli" BuilderConfig for additional information.
+
+#### mrpc
+
+The Microsoft Research Paraphrase Corpus (Dolan & Brockett, 2005) is a corpus of sentence pairs automatically extracted from online news sources, with human annotations for whether the sentences in the pair are semantically equivalent.
+
+#### qnli
+
+The Stanford Question Answering Dataset is a question-answering dataset consisting of question-paragraph pairs, where one of the sentences in the paragraph (drawn from Wikipedia) contains the answer to the corresponding question (written by an annotator). The authors of the benchmark convert the task into sentence pair classification by forming a pair between each question and each sentence in the corresponding context, and filtering out pairs with low lexical overlap between the question and the context sentence. The task is to determine whether the context sentence contains the answer to the question. This modified version of the original task removes the requirement that the model select the exact answer, but also removes the simplifying assumptions that the answer is always present in the input and that lexical overlap is a reliable cue.
+
+#### qqp
+
+The Quora Question Pairs2 dataset is a collection of question pairs from the community question-answering website Quora. The task is to determine whether a pair of questions are semantically equivalent.
+
+#### rte
+
+The Recognizing Textual Entailment (RTE) datasets come from a series of annual textual entailment challenges. The authors of the benchmark combined the data from RTE1 (Dagan et al., 2006), RTE2 (Bar Haim et al., 2006), RTE3 (Giampiccolo et al., 2007), and RTE5 (Bentivogli et al., 2009). Examples are constructed based on news and Wikipedia text. The authors of the benchmark convert all datasets to a two-class split, where for three-class datasets they collapse neutral and contradiction into not entailment, for consistency.
+
+#### sst2
+
+The Stanford Sentiment Treebank consists of sentences from movie reviews and human annotations of their sentiment. The task is to predict the sentiment of a given sentence. It uses the two-way (positive/negative) class split, with only sentence-level labels.
+
+#### stsb
+
+The Semantic Textual Similarity Benchmark (Cer et al., 2017) is a collection of sentence pairs drawn from news headlines, video and image captions, and natural language inference data. Each pair is human-annotated with a similarity score from 1 to 5.
+
+#### wnli
+
+The Winograd Schema Challenge (Levesque et al., 2011) is a reading comprehension task in which a system must read a sentence with a pronoun and select the referent of that pronoun from a list of choices. The examples are manually constructed to foil simple statistical methods: Each one is contingent on contextual information provided by a single word or phrase in the sentence. To convert the problem into sentence pair classification, the authors of the benchmark construct sentence pairs by replacing the ambiguous pronoun with each possible referent. The task is to predict if the sentence with the pronoun substituted is entailed by the original sentence. They use a small evaluation set consisting of new examples derived from fiction books that was shared privately by the authors of the original corpus. While the included training set is balanced between two classes, the test set is imbalanced between them (65% not entailment). Also, due to a data quirk, the development set is adversarial: hypotheses are sometimes shared between training and development examples, so if a model memorizes the training examples, they will predict the wrong label on corresponding development set example. As with QNLI, each example is evaluated separately, so there is not a systematic correspondence between a model's score on this task and its score on the unconverted original task. The authors of the benchmark call converted dataset WNLI (Winograd NLI).
 
 ### Supported Tasks
 
@@ -65,7 +175,12 @@ We show detailed information for up to 5 configurations of the dataset.
 
 An example of 'test' looks as follows.
 ```
-
+{
+  "premise": "The cat sat on the mat.",
+  "hypothesis": "The cat did not sit on the mat.",
+  "label": -1,
+  "idx: 0
+}
 ```
 
 #### cola
@@ -76,7 +191,11 @@ An example of 'test' looks as follows.
 
 An example of 'train' looks as follows.
 ```
-
+{
+  "sentence": "Our friends won't buy this analysis, let alone the next one we propose.",
+  "label": 1,
+  "id": 0
+}
 ```
 
 #### mnli
@@ -87,7 +206,12 @@ An example of 'train' looks as follows.
 
 An example of 'train' looks as follows.
 ```
-
+{
+  "premise": "Conceptually cream skimming has two basic dimensions - product and geography.",
+  "hypothesis": "Product and geography are what make cream skimming work.",
+  "label": 1,
+  "idx": 0
+}
 ```
 
 #### mnli_matched
@@ -96,9 +220,14 @@ An example of 'train' looks as follows.
 - **Size of the generated dataset:** 3.52 MB
 - **Total amount of disk used:** 301.82 MB
 
-An example of 'validation' looks as follows.
+An example of 'test' looks as follows.
 ```
-
+{
+  "premise": "Hierbas, ans seco, ans dulce, and frigola are just a few names worth keeping a look-out for.",
+  "hypothesis": "Hierbas is a name worth looking out for.",
+  "label": -1,
+  "idx": 0
+}
 ```
 
 #### mnli_mismatched
@@ -107,11 +236,44 @@ An example of 'validation' looks as follows.
 - **Size of the generated dataset:** 3.73 MB
 - **Total amount of disk used:** 302.02 MB
 
-An example of 'validation' looks as follows.
+An example of 'test' looks as follows.
 ```
-
+{
+  "premise": "What have you decided, what are you going to do?",
+  "hypothesis": "So what's your decision?,
+  "label": -1,
+  "idx": 0
+}
 ```
 
+#### mrpc
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+#### qnli
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+#### qqp
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+#### rte
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+#### sst2
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+#### stsb
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+#### wnli
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
 ### Data Fields
 
 The data fields are the same among all splits.
@@ -145,6 +307,34 @@ The data fields are the same among all splits.
 - `label`: a classification label, with possible values including `entailment` (0), `neutral` (1), `contradiction` (2).
 - `idx`: a `int32` feature.
 
+#### mrpc
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+#### qnli
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+#### qqp
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+#### rte
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+#### sst2
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+#### stsb
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+#### wnli
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
 ### Data Splits Sample Size
 
 #### ax
@@ -177,6 +367,34 @@ The data fields are the same among all splits.
 |---------------|---------:|---:|
 |mnli_mismatched|      9832|9847|
 
+#### mrpc
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+#### qnli
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+#### qqp
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+#### rte
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+#### sst2
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+#### stsb
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+#### wnli
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
 ## Dataset Creation
 
 ### Curation Rationale