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Use HF papers (#38184)

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* Use hf papers

* Hugging Face papers

* doi to hf papers

* style
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Quentin Gallouédec
2025-06-13 13:07:09 +02:00
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@@ -78,7 +78,7 @@ Additionally, the following method can be used to load values from a data file a
quality of cross-lingual text representations. XNLI is crowd-sourced dataset based on [*MultiNLI*](http://www.nyu.edu/projects/bowman/multinli/): pairs of text are labeled with textual entailment annotations for 15
different languages (including both high-resource language such as English and low-resource languages such as Swahili).
It was released together with the paper [XNLI: Evaluating Cross-lingual Sentence Representations](https://arxiv.org/abs/1809.05053)
It was released together with the paper [XNLI: Evaluating Cross-lingual Sentence Representations](https://huggingface.co/papers/1809.05053)
This library hosts the processor to load the XNLI data:
@@ -93,8 +93,8 @@ An example using these processors is given in the [run_xnli.py](https://github.c
[The Stanford Question Answering Dataset (SQuAD)](https://rajpurkar.github.io/SQuAD-explorer//) is a benchmark that
evaluates the performance of models on question answering. Two versions are available, v1.1 and v2.0. The first version
(v1.1) was released together with the paper [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://arxiv.org/abs/1606.05250). The second version (v2.0) was released alongside the paper [Know What You Don't
Know: Unanswerable Questions for SQuAD](https://arxiv.org/abs/1806.03822).
(v1.1) was released together with the paper [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://huggingface.co/papers/1606.05250). The second version (v2.0) was released alongside the paper [Know What You Don't
Know: Unanswerable Questions for SQuAD](https://huggingface.co/papers/1806.03822).
This library hosts a processor for each of the two versions: