formatting for readability

README.md

@@ -19,23 +19,23 @@ A Speech to Intent dataset for Indian English (`en-IN`)
   <p>This Datasheet is inspired from the <a href="https://arxiv.org/pdf/1803.09010.pdf" target="_blank">Datasheets for datasets</a> paper.</p>
 </div>
 
-## Motivation
+# Motivation
 
-Q1) For what purpose was the dataset created ? Was there a specific task in mind ? Was there a specific gap that needed to be filled ?
+**Q1) For what purpose was the dataset created ? Was there a specific task in mind ? Was there a specific gap that needed to be filled ?**
 
 Ans. This is a dataset for Intent classification from (Indian English) speech, and covers 14 coarse-grained intents from the Banking domain. While there are other datasets that have approached this task, here we provide a much largee training dataset (`>650` samples per intent) to train models in an end-to-end fashion. We also provide anonymised speaker information to help answer questions around model robustness and bias.
 
-Q2) Who created the dataset and on behalf of which entity ?
+**Q2) Who created the dataset and on behalf of which entity ?**
 
 Ans. The (internal) Operations team at Skit was involved in the generation of the dataset, and provided their information for (anonymous) release. [Unnati Senani](https://unnu.so/about/) was involved in the curation of utterance templates, and [Kriti Anandan](https://github.com/kritianandan98) and [Kumarmanas Nethil](https://huggingface.co/janaab) were involved in the planning and collection of utterances - using an internal tool called [sandbox](https://github.com/skit-ai/sandbox). These contributors worked on this dataset as part of the Conversational UX and ML teams at Skit.
 
-Q3) Who funded the creation of the dataset ?
+**Q3) Who funded the creation of the dataset ?**
 
 Ans. Skit funded the creation of this dataset.
 
-## Composition
+# Composition
 
-Q4) What do the instances that comprise the dataset consist of ?
+**Q4) What do the instances that comprise the dataset consist of ?**
 
 Ans. The intent dataset is split across `train.csv` and `test.csv`. In both, individual instances consist of the following fields:
 - `id`
@@ -57,7 +57,7 @@ You can trace more information on the speakers, using the shared `speaker_id` fi
 - `gender`
 
 
-Q5) How many instances are there in total (of each type, if appropriate) ?
+**Q5) How many instances are there in total (of each type, if appropriate) ?**
 
 Ans. In all there are `11845` samples, across the train and test splits:
 
@@ -73,60 +73,60 @@ Some statistics on the speakers are provided below. More granular information ca
 - Languages spoken: `Hindi`, `English`, `Bengali`, `Odia`, `Kannada`, `Punjabi`, `Malayalam`, `Bihari`, `Marathi`
 - Indian states lived in: `Bihar`, `Odisha`, `Karnataka`, `West Bengal`, `Punjab`, `Kerala`, `Jharkhand`, `Maharashtra`
 
-Q6) Does the dataset contain all possible instances or is it a sample (not necessarily random) of instances from a larger set ?
+**Q6) Does the dataset contain all possible instances or is it a sample (not necessarily random) of instances from a larger set ?**
 
 Ans. For each intent, our Conversational UX team generated a list of templates. These are meant to be a (satisfactory) representation of all the variations in utterances, seen in human speech. These templates were used as a guide by the speakers when generating data. So, this dataset is limited by the templates and the variations that speakers added (spontaneously).
 
-Q7) Are there recommended data splits (e.g., training, development/validation, testing) ?
+**Q7) Are there recommended data splits (e.g., training, development/validation, testing) ?**
 
 Ans. The recommended split into train and test sets is provided as `train.csv` and `test.csv` respectively.
 
-Q8) Are there any errors, sources of noise, or redundancies in the dataset?
+**Q8) Are there any errors, sources of noise, or redundancies in the dataset?**
 
 Ans. There could be channel noise present in the dataset, because the data was generated through telephone calls. However, background noise will not be as prevalent as in real-world scenarios, since these telephone calls were made in a semi-controlled environment.
 
-Q9) Other comments.
+**Q9) Other comments.**
 
 Ans. Speakers were responsible for generating variations in utterances, using the `template` field as a guide. So, there could be some unintentional overlap across the content of utterances.
 
-## Collection Process
+# Collection Process
 
-Q10) How was the data associated with each instance acquired ?
+**Q10) How was the data associated with each instance acquired ?**
 
 Ans. Members of the (internal) Operation team generated each utterance - using the associated `template` field as a guide, and injecting their own variations into the speech utterance.
 
-Q11) Who was involved in the data collection process and how were they compensated ?
+**Q11) Who was involved in the data collection process and how were they compensated ?**
 
 Ans. The data was generated by the (internal) Operations team and they are/were full-time employees.
 
-Q12) Over what timeframe was the data collected ?
+**Q12) Over what timeframe was the data collected ?**
 
 Ans. This data was collected over a time period of 1 month.
 
-Q13) Was any preprocessing/cleaning/labelling of the data done ?
+**Q13) Was any preprocessing/cleaning/labelling of the data done ?**
 
 Ans. Audio instances in the dataset were *auto-labelled* with their associated `intent` and `template` fields. For more information on this, refer to the documentation of [sandbox](https://github.com/skit-ai/sandbox).
 
-## Recommended Uses
+# Recommended Uses
 
-Q14) Has the dataset been used for any tasks already ?
+**Q14) Has the dataset been used for any tasks already ?**
 
 Ans. It has been used to benchmark models for the task of intent classification from speech.
 
-Q15) What (other) tasks could the dataset be used for ?
+**Q15) What (other) tasks could the dataset be used for ?**
 
 Ans. We provide speaker characteristics. So, this dataset could be used for alternate classification tasks from speech - like, gender or native language.
 
-## Distribution and Maintenance
+# Distribution and Maintenance
 
-Q16) Will the dataset be distributed under a copyright or other intellectual property (IP) license ?
+**Q16) Will the dataset be distributed under a copyright or other intellectual property (IP) license ?**
 
 Ans. This dataset is being distributed under a [CC BY NC license](https://creativecommons.org/licenses/by-nc/4.0/).
 
-Q17) Who will be maintaining the dataset ?
+**Q17) Who will be maintaining the dataset ?**
 
 Ans. The research team at Skit will be maintaining the dataset. They can be contacted by sending an email to ml-research@skit.ai.
 
-Q18) Will the dataset be updated in the future (e.g., to correct labelling errors, add new instances, delete instances) ?
+**Q18) Will the dataset be updated in the future (e.g., to correct labelling errors, add new instances, delete instances) ?**
 
 Ans. Incase there are errors, we will try to collate and share an updated version every 3 months. We also plan to add more instances and variations to the dataset - to make it more robust.