Update README.md

README.md

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-**Pralekha** is a large-scale parallel document dataset for **Cross-Lingual Document Alignment (CLDA)** and **Machine Translation (MT)** across **11 Indic languages** and **English**. It comprises over **3 million** document pairs, with **1.5 million** being English-centric.  
+**Pralekha** is a large-scale parallel document dataset spanning across **11 Indic languages** and **English**. It comprises over **3 million** document pairs, with **1.5 million** being English-centric. This dataset serves both as a benchmark for evaluating **Cross-Lingual Document Alignment (CLDA)** techniques and as a domain-specific parallel corpus for training document-level **Machine Translation (MT)** models in Indic Languages.
 
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 ## Dataset Description
 
-**Pralekha** covers 12 languages—Bengali (`ben`), Gujarati (`guj`), Hindi (`hin`), Kannada (`kan`), Malayalam (`mal`), Marathi (`mar`), Odia (`ori`), Punjabi (`pan`), Tamil (`tam`), Telugu (`tel`), Urdu (`urd`), and English (`eng`). It includes a mixture of high- and medium-resource languages, covering 11 different scripts. The dataset spans two broad domains: **News Bulletins** ([Indian Press Information Bureau (PIB)](https://pib.gov.in)) and **Podcast Scripts** ([Mann Ki Baat](https://www.pmindia.gov.in/en/mann-ki-baat)), offering both written and spoken forms of data. All the data is human-written or human-verified, ensuring high quality.
+**Pralekha** covers 12 languages—Bengali (`ben`), Gujarati (`guj`), Hindi (`hin`), Kannada (`kan`), Malayalam (`mal`), Marathi (`mar`), Odia (`ori`), Punjabi (`pan`), Tamil (`tam`), Telugu (`tel`), Urdu (`urd`), and English (`eng`). It includes a mixture of high- and medium-resource languages, covering 11 different scripts. The dataset spans two broad domains: **News Bulletins** ([Indian Press Information Bureau (PIB)](https://pib.gov.in)) and **Podcast Scripts** ([Mann Ki Baat (MKB)](https://www.pmindia.gov.in/en/mann-ki-baat)), offering both written and spoken forms of data. All the data is human-written or human-verified, ensuring high quality.
 
 While this accounts for `alignable` (parallel) documents, In real-world scenarios, multilingual corpora often include `unalignable` documents. To simulate this for CLDA evaluation, we sample `unalignable` documents from [Sangraha Unverified](https://huggingface.co/datasets/ai4bharat/sangraha/viewer/unverified), selecting 50% of Pralekha’s size to maintain a 1:2 ratio of `unalignable` to `alignable` documents. 
 
-For Machine Translation (MT) tasks, we first randomly sample 1,000 documents per English-Indic language pair, ensuring a good distribution of varying document lengths. After excluding these sampled documents, we use the remaining documents for training document-level machine translation models.
+For Machine Translation (MT) tasks, we first randomly sample 1,000 documents from the `alignable` subset per English-Indic language pair for each development (dev) and test set, ensuring a good distribution of varying document lengths. After excluding these sampled documents, we use the remaining documents as the training set for training document-level machine translation models.
 
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 - **`src_lang`:** Source Language (eng)
 - **`src_text`:** Source Language Text
-- **`tgt_lang`:** Target Language ((ISO 639-3 code)
+- **`tgt_lang`:** Target Language (ISO 639-3 code)
 - **`tgt_text`:** Target Language Text
 
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