Update README.md

README.md

@@ -23,7 +23,45 @@ dataset_info:
     num_examples: 18392
   download_size: 4087107539
   dataset_size: 137284467341
+license: apache-2.0
+task_categories:
+- text-generation
+language:
+- hi
 ---
 # Dataset Card for "mC4-hindi"
 
-[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+This dataset is a subset of the mC4 dataset, which is a multilingual colossal, cleaned version of Common Crawl's web crawl corpus. It contains natural text in 101 languages, including Hindi. This dataset is specifically focused on Hindi text, and contains a variety of different types of text, including news articles, blog posts, and social media posts.
+
+This dataset is intended to be used for training and evaluating natural language processing models for Hindi. It can be used for a variety of tasks, such as pretraining language models, machine translation, text summarization, and question-answering.
+
+**Data format**
+
+The dataset is in JSONL format. Each line in the file contains a JSON object with the following fields:
+
+* `text`: field contains the text of the document.
+* `timestamp`: field contains the date and time when the document was crawled.
+* `url`: field contains the URL of the document.
+
+**Data splits**
+
+The dataset is split into two parts: train and validation. The train split contains 90% of the data, the validation split contains 5% of the data, and the test split contains 5% of the data.
+
+**Usage**
+
+To use the dataset, you can load it into a Hugging Face Dataset object using the following code:
+
+```python
+import datasets
+
+dataset = datasets.load_dataset("zicsx/mC4-hindi")
+```
+
+Once you have loaded the dataset, you can access the train and validation splits using the following code:
+
+```python
+train_dataset = dataset["train"]
+validation_dataset = dataset["validation"]
+```
+
+You can then use the dataset to train and evaluate your natural language processing model.