Update README.md

README.md

@@ -42,7 +42,7 @@ task_ids:
 
 - **Fine-Tuning script:** [research-projects/xtreme-s](https://github.com/huggingface/transformers/tree/main/examples/pytorch/audio-classification)
 - **Paper:** [Multilingual and Cross-Lingual Intent Detection from Spoken Data](https://arxiv.org/abs/2104.08524)
-- **Total amount of disk used:** ca. 5 GB 
+- **Total amount of disk used:** ca. 500 MB 
 
 MINDS-14 is training and evaluation resource for intent detection task with spoken data. It covers 14 
 intents extracted from a commercial system in the e-banking domain, associated with spoken examples in 14 diverse language varieties.
@@ -71,8 +71,21 @@ intent = minds_14["train"].features["intent_class"].names[intent_class]
 
 ## Dataset Structure
 
+We show detailed information the example configurations `fr-FR` of the dataset.
+All other configurations have the same structure.
+
+### Data Instances
+
+**fr-FR**
+
+- Size of downloaded dataset files: 471 MB
+- Size of the generated dataset: 300 KB
+- Total amount of disk used: 471 MB
+
+
 An example of a datainstance of the config `fr-FR` looks as follows:
 
+```
 {
     "path": "/home/patrick/.cache/huggingface/datasets/downloads/extracted/3ebe2265b2f102203be5e64fa8e533e0c6742e72268772c8ac1834c5a1a921e3/fr-FR~ADDRESS/response_4.wav",
     "audio": {
@@ -87,6 +100,20 @@ An example of a datainstance of the config `fr-FR` looks as follows:
     "intent_class": 1,
     "lang_id": 6,
 }
+```
+
+### Data Fields
+The data fields are the same among all splits.
+
+- **path** (str): Path to the audio file
+- **audio** (dict): Audio object including loaded audio array, sampling rate and path ot audio
+- **transcription** (str): Transcription of the audio file
+- **english_transcription** (str): English transcription of the audio file
+- **intent_class** (int): Class id of intent
+- **lang_id** (int): Id of language
+
+### Data Splits
+Every config only has the `"train"` split containing of *ca.* 600 examples.
 
 ## Dataset Creation