add : hard/internet shortage section added.

README.md

@@ -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 you 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.