Merge branch 'main' of https://huggingface.co/datasets/SLPL/naab into main

README.md

@@ -6,7 +6,7 @@ license:
 multilinguality:
 - monolingual
 size_categories:
-- 200M<n<300M
+- 100M<n<1B
 task_categories:
 - language-modeling
 - masked-language-modeling
@@ -44,7 +44,7 @@ _[If you want to join our community to keep up with news, models and datasets fr
 ## Dataset Description
 
 - **Homepage:** [Sharif Speech and Language Processing Lab](https://huggingface.co/SLPL)
-- **Paper:** [If the dataset was introduced by a paper or there was a paper written describing the dataset, add URL here (landing page for Arxiv paper preferred)]()
+- **Paper:** [naab: A ready-to-use plug-and-play corpus for Farsi](https://arxiv.org/abs/2208.13486)
 - **Point of Contact:** [Sadra Sabouri](mailto:[email protected])
 
 ### Dataset Summary
@@ -56,8 +56,6 @@ from datasets import load_dataset
 
 dataset = load_dataset("SLPL/naab")
 ```
-_Note: be sure that your machine has at least 130 GB free space, also it may take a while to download._
-
 You may need to download parts/splits of this corpus too, if so use the command below (You can find more ways to use it [here](https://huggingface.co/docs/datasets/loading#slice-splits)):
 ```python
 from datasets import load_dataset
@@ -65,6 +63,42 @@ from datasets import load_dataset
 dataset = load_dataset("SLPL/naab", split="train[:10%]")
 ```
 
+**Note: be sure that your machine has at least 130 GB free space, also it may take a while to download. If you are facing disk or internet shortage, you can use below code snippet helping you download your costume sections of the naab:**
+
+```python
+from datasets import load_dataset
+
+# ==========================================================
+# You should just change this part in order to download your 
+# parts of corpus.
+indices = {
+    "train": [5, 1, 2],
+    "test": [0, 2]
+}
+# ==========================================================
+
+
+N_FILES = {
+    "train": 126,
+    "test": 3
+}
+_BASE_URL = "https://huggingface.co/datasets/SLPL/naab/resolve/main/data/"
+data_url = {
+    "train": [_BASE_URL + "train-{:05d}-of-{:05d}.txt".format(x, N_FILES["train"]) for x in range(N_FILES["train"])],
+    "test": [_BASE_URL + "test-{:05d}-of-{:05d}.txt".format(x, N_FILES["test"]) for x in range(N_FILES["test"])],
+}
+for index in indices['train']:
+    assert index < N_FILES['train']
+for index in indices['test']:
+    assert index < N_FILES['test']
+data_files = {
+    "train": [data_url['train'][i] for i in indices['train']],
+    "test": [data_url['test'][i] for i in indices['test']]
+}
+print(data_files)
+dataset = load_dataset('text', data_files=data_files, use_auth_token=True)
+```
+
 ### Supported Tasks and Leaderboards
 
 This corpus can be used for training all language models which can be trained by Masked Language Modeling (MLM) or any other self-supervised objective.
@@ -165,17 +199,20 @@ mit?
 
 ### Citation Information
 
-Provide the [BibTex](http://www.bibtex.org/)-formatted reference for the dataset. For example:
 ```
-@article{article_id,
-  author    = {Author List},
-  title     = {Dataset Paper Title},
-  journal   = {Publication Venue},
-  year      = {2525}
+@misc{https://doi.org/10.48550/arxiv.2208.13486,
+  doi = {10.48550/ARXIV.2208.13486},
+  url = {https://arxiv.org/abs/2208.13486},
+  author = {Sabouri, Sadra and Rahmati, Elnaz and Gooran, Soroush and Sameti, Hossein},
+  keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
+  title = {naab: A ready-to-use plug-and-play corpus for Farsi},
+  publisher = {arXiv},
+  year = {2022},
+  copyright = {Creative Commons Attribution Non Commercial Share Alike 4.0 International}
 }
 ```
 
-If the dataset has a [DOI](https://www.doi.org/), please provide it here.
+DOI: [https://doi.org/10.48550/arXiv.2208.13486](https://doi.org/10.48550/arXiv.2208.13486)
 
 ### Contributions
 

dataset_info.json

@@ -1,8 +1,8 @@
 {
     "description": "naab: A ready-to-use plug-and-play corpus in Farsi",
-    "citation": "",
-    "homepage": "",
-    "license": "",
+    "citation": "@misc{https://doi.org/10.48550/arxiv.2208.13486, doi = {10.48550/ARXIV.2208.13486}, url = {https://arxiv.org/abs/2208.13486}, author = {Sabouri, Sadra and Rahmati, Elnaz and Gooran, Soroush and Sameti, Hossein}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {naab: A ready-to-use plug-and-play corpus for Farsi}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution Non Commercial Share Alike 4.0 International}}",
+    "homepage": "https://huggingface.co/SLPL",
+    "license": "mit",
     "features": {
         "text": {
             "dtype": "string",

naab.py

@@ -24,6 +24,17 @@ import datasets
 # TODO: Add BibTeX citation
 # Find for instance the citation on arxiv or on the dataset repo/website
 _CITATION = """\
+@misc{https://doi.org/10.48550/arxiv.2208.13486,
+  doi = {10.48550/ARXIV.2208.13486},
+  url = {https://arxiv.org/abs/2208.13486},
+  author = {Sabouri, Sadra and Rahmati, Elnaz and Gooran, Soroush and Sameti, Hossein},
+  keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
+  title = {naab: A ready-to-use plug-and-play corpus for Farsi},
+  publisher = {arXiv},
+  year = {2022},
+  copyright = {Creative Commons Attribution Non Commercial Share Alike 4.0 International}
+}
+
 """
 
 # You can copy an official description
@@ -33,7 +44,6 @@ Huge corpora of textual data are always known to be a crucial need for training
 
 _HOMEPAGE = "https://huggingface.co/datasets/SLPL/naab"
 
-# TODO: ?
 _LICENSE = "mit"
 
 N_FILES = {
@@ -96,24 +106,25 @@ class Naab(datasets.GeneratorBasedBuilder):
             datasets.SplitGenerator(
                 name=datasets.Split.TRAIN,
                 gen_kwargs={
-                    "filepath": train_downloaded_files,
+                    "filepaths": train_downloaded_files,
                     "split": "train"
                 }
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.TEST,
                 gen_kwargs={
-                    "filepath": test_downloaded_files,
+                    "filepaths": test_downloaded_files,
                     "split": "test"
                 }
             ),
         ]
  
 
-    def _generate_examples(self, filepath, split):
-        with open(filepath, encoding="utf-8") as f:
-            for key, row in enumerate(f):
-                if row.strip():
-                    yield idx, {"text": row}
-                else:
-                    yield idx, {"text": ""}
+    def _generate_examples(self, filepaths, split):
+        for filepath in filepaths:
+            with open(filepath, encoding="utf-8") as f:
+                for key, row in enumerate(f):
+                    if row.strip():
+                        yield key, {"text": row}
+                    else:
+                        yield key, {"text": ""}