Update README.md

README.md

@@ -20,6 +20,7 @@ size_categories:
 ### Dataset Summary
 
 This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
+Note that the 2000 examples included in MNLI (and machine translated in French here) on the subject of 9/11 are different from the 249 examples in the validation subset and the 501 ones in the test subset of XNLI on the same subject.
 
 ### Supported Tasks and Leaderboards
 
@@ -117,26 +118,6 @@ This dataset card aims to be a base template for new datasets. It has been gener
   location = "New Orleans, Louisiana",
   url = "http://aclweb.org/anthology/N18-1101"
 }
-
-@inproceedings{conneau-etal-2018-xnli,
-    title = "{XNLI}: Evaluating Cross-lingual Sentence Representations",
-    author = "Conneau, Alexis  and
-      Rinott, Ruty  and
-      Lample, Guillaume  and
-      Williams, Adina  and
-      Bowman, Samuel  and
-      Schwenk, Holger  and
-      Stoyanov, Veselin",
-    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
-    month = oct # "-" # nov,
-    year = "2018",
-    address = "Brussels, Belgium",
-    publisher = "Association for Computational Linguistics",
-    url = "https://aclanthology.org/D18-1269",
-    doi = "10.18653/v1/D18-1269",
-    pages = "2475--2485",
-    abstract = "State-of-the-art natural language processing systems rely on supervision in the form of annotated data to learn competent models. These models are generally trained on data in a single language (usually English), and cannot be directly used beyond that language. Since collecting data in every language is not realistic, there has been a growing interest in cross-lingual language understanding (XLU) and low-resource cross-language transfer. In this work, we construct an evaluation set for XLU by extending the development and test sets of the Multi-Genre Natural Language Inference Corpus (MultiNLI) to 14 languages, including low-resource languages such as Swahili and Urdu. We hope that our dataset, dubbed XNLI, will catalyze research in cross-lingual sentence understanding by providing an informative standard evaluation task. In addition, we provide several baselines for multilingual sentence understanding, including two based on machine translation systems, and two that use parallel data to train aligned multilingual bag-of-words and LSTM encoders. We find that XNLI represents a practical and challenging evaluation suite, and that directly translating the test data yields the best performance among available baselines.",
-}
 ```
 
 ### Contributions