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README.md

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+---
+annotations_creators:
+- machine-generated
+language_creators:
+- found
+multilinguality:
+- monolingual
+size_categories:
+- 100K<n<1M
+source_datasets:
+- extended
+task_categories:
+- text-classification
+task_ids:
+- natural-language-inference
+languages:
+- bn
+licenses:
+- cc-by-nc-sa-4.0
+---
+
+# Dataset Card for `xnli_bn`
+
+## Table of Contents
+- [Dataset Card for `xnli_bn`](#dataset-card-for-xnli_bn)
+  - [Table of Contents](#table-of-contents)
+  - [Dataset Description](#dataset-description)
+    - [Dataset Summary](#dataset-summary)
+    - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+    - [Languages](#languages)
+    - [Usage](#usage)
+  - [Dataset Structure](#dataset-structure)
+    - [Data Instances](#data-instances)
+    - [Data Fields](#data-fields)
+    - [Data Splits](#data-splits)
+  - [Dataset Creation](#dataset-creation)
+    - [Curation Rationale](#curation-rationale)
+    - [Source Data](#source-data)
+      - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
+      - [Who are the source language producers?](#who-are-the-source-language-producers)
+    - [Annotations](#annotations)
+      - [Annotation process](#annotation-process)
+      - [Who are the annotators?](#who-are-the-annotators)
+    - [Personal and Sensitive Information](#personal-and-sensitive-information)
+  - [Considerations for Using the Data](#considerations-for-using-the-data)
+    - [Social Impact of Dataset](#social-impact-of-dataset)
+    - [Discussion of Biases](#discussion-of-biases)
+    - [Other Known Limitations](#other-known-limitations)
+  - [Additional Information](#additional-information)
+    - [Dataset Curators](#dataset-curators)
+    - [Licensing Information](#licensing-information)
+    - [Citation Information](#citation-information)
+    - [Contributions](#contributions)
+
+## Dataset Description
+
+- **Repository:** [https://github.com/csebuetnlp/banglabert](https://github.com/csebuetnlp/banglabert)
+- **Paper:** [**"BanglaBERT: Combating Embedding Barrier in Multilingual Models for Low-Resource Language Understanding"**](https://arxiv.org/abs/2101.00204)
+- **Point of Contact:** [Tahmid Hasan](mailto:tahmidhasan@cse.buet.ac.bd)
+
+### Dataset Summary
+
+This is a Natural Language Inference (NLI) dataset for Bengali, curated using the subset of
+MNLI data used in XNLI and state-of-the-art English to Bengali translation model introduced **[here](https://aclanthology.org/2020.emnlp-main.207/).**
+
+
+### Supported Tasks and Leaderboards
+
+[More information needed](https://github.com/csebuetnlp/banglabert)
+
+### Languages
+
+* `Bengali`
+
+### Usage
+```python
+from datasets import load_dataset
+dataset = load_dataset("csebuetnlp/xnli_bn")
+```
+## Dataset Structure
+
+### Data Instances
+
+One example from the dataset is given below in JSON format. 
+  ```
+  {
+    "sentence1": "আসলে, আমি এমনকি এই বিষয়ে চিন্তাও করিনি, কিন্তু আমি এত হতাশ হয়ে পড়েছিলাম যে, শেষ পর্যন্ত আমি আবার তার সঙ্গে কথা বলতে শুরু করেছিলাম",
+    "sentence2": "আমি তার সাথে আবার কথা বলিনি।",
+    "label": "contradiction"
+}
+  ```
+
+### Data Fields
+
+The data fields are as follows:
+
+- `sentence1`: a `string` feature indicating the premise.
+- `sentence2`: a `string` feature indicating the hypothesis.
+- `label`: a classification label, where possible values are `contradiction` (0), `entailment` (1), `neutral` (2) .
+
+### Data Splits
+|   split   |count  |
+|----------|--------|
+|`train`|  381449 |
+|`validation`| 2419  |
+|`test`| 4895 |
+
+
+
+
+## Dataset Creation
+
+The dataset curation procedure was the same as the [XNLI](https://aclanthology.org/D18-1269/) dataset: we translated the [MultiNLI](https://aclanthology.org/N18-1101/) training data using the English to Bangla translation model introduced [here](https://aclanthology.org/2020.emnlp-main.207/). Due to the possibility of incursions of error during automatic translation, we used the [Language-Agnostic BERT Sentence Embeddings (LaBSE)](https://arxiv.org/abs/2007.01852) of the translations and original sentences to compute their similarity. All sentences below a similarity threshold of 0.70 were discarded.
+
+### Curation Rationale
+
+[More information needed](https://github.com/csebuetnlp/banglabert)
+
+### Source Data
+
+[XNLI](https://aclanthology.org/D18-1269/)
+
+#### Initial Data Collection and Normalization
+
+[More information needed](https://github.com/csebuetnlp/banglabert)
+
+
+#### Who are the source language producers?
+
+[More information needed](https://github.com/csebuetnlp/banglabert)
+
+
+### Annotations
+
+[More information needed](https://github.com/csebuetnlp/banglabert)
+
+
+#### Annotation process
+
+[More information needed](https://github.com/csebuetnlp/banglabert)
+
+#### Who are the annotators?
+
+[More information needed](https://github.com/csebuetnlp/banglabert)
+
+### Personal and Sensitive Information
+
+[More information needed](https://github.com/csebuetnlp/banglabert)
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More information needed](https://github.com/csebuetnlp/banglabert)
+
+### Discussion of Biases
+
+[More information needed](https://github.com/csebuetnlp/banglabert)
+
+### Other Known Limitations
+
+[More information needed](https://github.com/csebuetnlp/banglabert)
+
+## Additional Information
+
+### Dataset Curators
+
+[More information needed](https://github.com/csebuetnlp/banglabert)
+
+### Licensing Information
+
+Contents of this repository are restricted to only non-commercial research purposes under the [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/). Copyright of the dataset contents belongs to the original copyright holders.
+### Citation Information
+
+If you use the dataset, please cite the following paper:
+```
+@misc{bhattacharjee2021banglabert,
+      title={BanglaBERT: Combating Embedding Barrier in Multilingual Models for Low-Resource Language Understanding},
+      author={Abhik Bhattacharjee and Tahmid Hasan and Kazi Samin and Md Saiful Islam and M. Sohel Rahman and Anindya Iqbal and Rifat Shahriyar},
+      year={2021},
+      eprint={2101.00204},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```
+
+
+### Contributions
+
+Thanks to [@abhik1505040](https://github.com/abhik1505040) and [@Tahmid](https://github.com/Tahmid04) for adding this dataset.

data/squad_bn.tar.bz2

@@ -0,0 +1,3 @@
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+size 8432345

squad_bn.py

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+"""SQuAD Bengali Dataset"""
+
+import os
+import json
+
+import datasets
+from datasets.tasks import QuestionAnsweringExtractive
+
+
+_CITATION = """\
+@misc{bhattacharjee2021banglabert,
+      title={BanglaBERT: Combating Embedding Barrier in Multilingual Models for Low-Resource Language Understanding},
+      author={Abhik Bhattacharjee and Tahmid Hasan and Kazi Samin and Md Saiful Islam and M. Sohel Rahman and Anindya Iqbal and Rifat Shahriyar},
+      year={2021},
+      eprint={2101.00204},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+"""
+
+_DESCRIPTION = """\
+SQuAD-bn is derived from the SQuAD-2.0 and TyDI-QA datasets.
+"""
+
+_HOMEPAGE = "https://github.com/csebuetnlp/banglabert"
+_LICENSE = "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0)"
+_URL = "https://huggingface.co/datasets/csebuetnlp/squad_bn/resolve/main/data/squad_bn.tar.bz2"
+_VERSION = datasets.Version("0.0.1")
+
+
+
+class SquadBn(datasets.GeneratorBasedBuilder):
+    """SQuAD Bengali Dataset"""
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name="squad_bn",
+            version=_VERSION,
+            description=_DESCRIPTION,
+        )
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "title": datasets.Value("string"),
+                    "context": datasets.Value("string"),
+                    "question": datasets.Value("string"),
+                    "answers": datasets.features.Sequence(
+                        {
+                            "text": datasets.Value("string"),
+                            "answer_start": datasets.Value("int32"),
+                        }
+                    ),
+                }
+            ),
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+            task_templates=[
+                QuestionAnsweringExtractive(
+                    question_column="question", context_column="context", answers_column="answers"
+                )
+            ],
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        data_dir = os.path.join(dl_manager.download_and_extract(_URL), "squad_bn")
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "train.json"),
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "test.json"),
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "validation.json"),
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath):
+        """Yields examples as (key, example) tuples."""
+        
+        with open(filepath, encoding="utf-8") as f:
+            data = json.load(f)
+            for example in data["data"]:
+                title = example.get("title", "")
+                for paragraph in example["paragraphs"]:
+                    context = paragraph["context"].strip()
+                    for qa in paragraph["qas"]:
+                        question = qa["question"].strip()
+                        id_ = qa["id"]
+
+                        answer_starts = [answer["answer_start"] for answer in qa["answers"]]
+                        answers = [answer["text"].strip() for answer in qa["answers"]]
+
+                        yield id_, {
+                            "title": title,
+                            "context": context,
+                            "question": question,
+                            "id": id_,
+                            "answers": {
+                                "answer_start": answer_starts,
+                                "text": answers,
+                            },
+                        }