Minor edits to data card

README.md

@@ -194,11 +194,11 @@ configs:
 
 # Dataset Summary
 
-`Aya Evaluation Suite` contains a total of 25,750 open-ended conversation-style prompts to evaluate multilingual open-ended generation quality.\
+`Aya Evaluation Suite` contains a total of 26,750 open-ended conversation-style prompts to evaluate multilingual open-ended generation quality.\
 To strike a balance between language coverage and the quality that comes with human curation, we create an evaluation suite that includes:
-1) human-curated examples in 7 languages (`tur,eng,yor,arb,zho,por,tel`) → `aya-human-annotated`.
+1) human-curated examples in 7 languages (`tur, eng, yor, arb, zho, por, tel`) → `aya-human-annotated`.
 2) machine-translations of handpicked examples into 101 languages → `dolly-machine-translated`.
-3) human-post-edited translations into 6 languages (`hin,srp,rus,fra,arb,spa`) → `dolly-human-edited`.
+3) human-post-edited translations into 6 languages (`hin, srp, rus, fra, arb, spa`) → `dolly-human-edited`.
 
 ---
 
@@ -224,7 +224,7 @@ The `Aya Evaluation Suite` includes the following subsets:
 
 
 ## Load with Datasets
-To load this dataset consisting of both prompt-completions and demographics data with `datasets`, you'll just need to install Datasets as `pip install datasets --upgrade` and then use the following code:
+To load this dataset consisting of prompt-completions with `datasets`, you just need to install Datasets as `pip install datasets --upgrade` and then use the following code:
 
 ```python
 from datasets import load_dataset
@@ -262,8 +262,8 @@ Example data instances from the `Aya Evaluation Suite` subsets are listed in the
 
 <b>Dolly-machine-translated and dolly-human-edited</b>
 
-- These two subsets are parallel datasets (data instances can be mapped using the `id`).
-- Note that in the `dolly-machine-translated` subset, we also include the original English subset (`id 1-200`) that is translated into 101 languages. Furthermore, the field `id` can be used to filter out the translation of the same data instance across the languages. 
+- These two subsets are parallel datasets (data instances can be mapped using their `id` column).
+- Note that in the `dolly-machine-translated` subset, we also include the original English subset (`id 1-200`), which is translated into 101 languages. Furthermore, the field `id` can be used to match the translations of the same data instance across languages. 
 - The `source_id` field contains the corresponding original row index from the [databricks-dolly-15k](https://huggingface.co/datasets/databricks/databricks-dolly-15k) dataset.
   <details>
   <summary> <b>dolly-machine-translated</b> </summary>  
@@ -421,7 +421,7 @@ The toggled table below lists the breakdown of languages in each subset.
   |	`xho`  |	Xhosa	|	Low	|
   |	`yid` (`ydd`)  |	Yiddish (Eastern)	|	Low	|
   |	`yor`  |	Yoruba	|	Low	|
-  |	`zho` & `yue`  |	Chinese	 (Simplified & Yue) |	High	|
+  |	`zho` (+ `yue`)  |	Chinese	 (Simplified & Cantonese) |	High	|
   |	`zul`  |	Zulu	|	Low	|
 </details>
 
@@ -444,12 +444,12 @@ The toggled table below lists the breakdown of languages in each subset.
 # Motivations & Intentions
 
 - **Curation Rationale:** This evaluation suite is tailored to test the generation quality of multilingual models, with the aim of balancing language coverage and human-sourced quality.
-It covers prompts originally written in each language, as well as English-centric translated and manually curated or edited prompts for a linguistically broad but rich testbed.
-The list of languages was established from mT5 and aligned with the annotators’ language list and the NLLB translation model.
+It covers prompts originally written in each language, as well as English-centric translated, and manually curated or edited prompts for a linguistically broad, but rich testbed.
+The list of languages was initially established from mT5 and aligned with the annotators’ language list and the NLLB translation model.
 
 # Known Limitations
 
-- **Translation Quality:** Note that the expressiveness of the `dolly-machine-translated` subset is limited by the quality of the translation model and may adversely impact an estimate of ability in languages where translations are not adequate. If this subset is used for testing, we recommend it be paired and reported with the professionally post-edited `dolly-human-edited` subset or the `aya-human-annotated` set, which also only covers 7 languages but is entirely created by proficient target language speakers.
+- **Translation Quality:** Note that the expressiveness of the `dolly-machine-translated` subset is limited by the quality of the translation model and may adversely impact an estimate of ability in languages where translations are not adequate. If this subset is used for testing, we recommend it be paired and reported with the professionally post-edited `dolly-human-edited` subset or the `aya-human-annotated` set, which, while covering only 7 languages, is entirely created by proficient target language speakers.
 ---
 
 # Additional Information