Update README.md

README.md

@@ -1,4 +1,5 @@
 ---
+---
 license: cc-by-2.0
 task_categories:
 - translation
@@ -9,67 +10,65 @@ size_categories:
 - 10K<n<100K
 ---
 
+
 # Dataset Card for Dataset Name
 
 ## Dataset Description
 
-- **Homepage:** 
-- **Repository:**
+- **Repository:**[link](https://github.com/Digital-Umuganda/twb_nllb_project_tourism_education) to the GitHub repository containing the code for training the model on this data, and the code for the collection of the monolingual data.
 - **Data Format:** TSV
-- **Data Source:** [Source of the data, e.g., web scraping, surveys]
-- **Point of Contact:** 
-### Dataset Summary
+- **Model:** huggingface [model link](mbazaNLP/Nllb_finetuned_education_en_kin).
 
-This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
+  
+### Dataset Summary
 
 
 
 ### Data Instances
 
-[More Information Needed]
+```
+118347	103384	And their ideas was that the teachers just didn't care and had no time for them.	Kandi igitekerezo cyabo nuko abarimu batabitayeho gusa kandi ntibabone umwanya.	2023-06-25 09:40:28	 223	1	3	education	coursera	72-93
+```
 
 ### Data Fields
 
-[More Information Needed]
+- id
+- source_id
+- source
+- phrase
+- timestamp
+- user_id
+- validation_state
+- validation_score
+- domain
+- source_files
+- str_ranges
 
 ### Data Splits
 
-- **Training Data:** [Description of the training data split]
-- **Validation Data:** [Description of the validation data split]
-- **Test Data:** [Description of the test data split]
-[More Information Needed]
+- **Training Data:** 58251
+- **Validation Data:** 2456
+- **Test Data:** 1060
 
 ## Data Preprocessing
 
-- **Data Cleaning:** [Description of any data cleaning or preprocessing steps]
-- **Data Transformation:** [Details on how the data was transformed, if applicable]
-- **Data Splitting:** [Information about how the data was split into training, validation, and test sets]
+- **Data Splitting:** To create a test set; all data sources are equally represented in terms of the number of sentences contributed to the test dataset. In terms of sentence length, the test set distribution is similar to the sentence length distribution of the whole dataset. After picking the test set, from the remaining data the train and validation data are split using sklearn's [train_test_split](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html).
 
 ## Data Collection
 
-- **Data Collection Process:** [Description of how the data was collected]
-- **Data Sources:** [List of sources used to collect the data]
-- **Data Annotations:** [Details about any annotations or labels in the dataset]
-
-## Dataset Creation
-
-### Curation Rationale
-
-[More Information Needed]
+- **Data Collection Process:** The monolingual source sentences were obtained through web-scraping of several websites containing English sentences. 
 
-### Source Data
+- **Data Sources:**
+  -  Coursera
+  -  Atingi
+  -  Wikipedia
 
-#### Initial Data Collection and Normalization
 
 
-[More Information Needed]
 
-### Annotations
-
-#### Annotation process
+## Dataset Creation
 
-[More Information Needed]
+After collecting the monolingual dataset, human translators were employed to produce translations for the collected sentences. To ensure quality, each sentence was translated more than once, and each generated translation was assigned **validation_score** that was used to pick the best translation. The test dataset was further revised to remove or correct sentences with faulty translations.
 
-#### Who are the annotators?