Update README.md

README.md

@@ -128,38 +128,79 @@ SQuAD-TR is a machine translated version of the original [SQuAD2.0](https://rajp
 ### Data Instances
 
 Our data instances follow that of the original SQuAD2.0 dataset.
-Shared below is an example instance from the default train dataset.
+Shared below is an example instance from the default train dataset🍫
 
+Example from SQuAD2.0:
 ```
 {
-    "id": "56be85543aeaaa14008c9063",
-    "title": "Beyonce",
-    "context": "Beyoncé Giselle Knowles-Carter (d. 4 Eylül 1981), ABD'li şarkıcı, söz yazarı, prodüktör ve aktris. Houston, Teksas'ta doğup büyüdü, çocukken çeşitli şarkı ve dans yarışmalarında sahne aldı ve 1990'ların sonlarında R&B kız grubu Destiny's Child'ın solisti olarak ün kazandı. Babası Mathew Knowles tarafından yönetilen grup tüm zamanların en çok satan kız gruplarından biri oldu. Beyoncé'nin ilk albümü Dangerously in Love'ın (2003) yayınlanmasını izlemiştir ve beş Grammy Ödülü kazanmış ve Billboard Hot 100 bir numaralı single'ları “Crazy in Love” ve “Baby Boy\"un yer aldığı Beyoncé'nin ilk albümü Dangerously in Love (2003) yayınlandı.",
-    "question": "Beyonce ne zaman popüler olmaya başladı?",
-    "answers": [
-      {
-        "text": "1990'ların sonlarında",
-        "answer_start": 192
-      }
+    "context": "Chocolate is New York City's leading specialty-food export, with up to US$234 million worth of exports each year. Entrepreneurs were forming a \"Chocolate District\" in Brooklyn as of 2014, while Godiva, one of the world's largest chocolatiers, continues to be headquartered in Manhattan.",
+  "qas": [
+    {
+     "id": "56cff221234ae51400d9c140",
+      "question": "Which one of the world's largest chocolate makers is stationed in Manhattan?",
+      "is_impossible": false,
+      "answers": [
+        {
+          "text": "Godiva",
+          "answer_start": 194
+        }
+      ],
+    }
+  ]
+}
+```
+
+Turkish translation:
+
+```
+{
+    "context": "Çikolata, her yıl 234 milyon ABD dolarına varan ihracatı ile New York'un önde gelen özel gıda ihracatıdır. Girişimciler 2014 yılı itibariyle Brooklyn'de bir “Çikolata Bölgesi” kurarken, dünyanın en büyük çikolatacılarından biri olan Godiva merkezi Manhattan'da olmaya devam ediyor.",
+    "qas": [
+        {
+            "id": "56cff221234ae51400d9c140",
+            "question": "Dünyanın en büyük çikolata üreticilerinden hangisi Manhattan'da konuşlandırılmış?",
+            "is_impossible": false,
+            "answers": [
+                {
+                    "text": "Godiva",
+                    "answer_start": 233
+                }
+            ]
+        }
     ]
 }
 
 ```
-Notes:
+
+
+### Data Fields
+
+Below if the data model of the splits.  
+
+- `id`: a string feature.
+- `title`: a string feature.
+- `context`: a string feature.
+- `question`: a string feature.
+- `answers`: a dictionary feature containing:
+  - `text`: a string feature.
+  - `*answer_start`: a int32 feature.
+
+*Notes:
 - The training split we get by `openqa` parameter will not include `answer_start` field as it is not required for the training phase of the OpenQA formulation.
 - The split we get by `excluded` parameter is also missing `answer_start` field as we could not identify the starting index of the answers for these examples from the context after the translation.
 
-### Data Fields
+## Dataset Creation
 
-The data fields with `*` prefix are the same for all splits. The splits we get by `openqa` and `excluded` parameters are missing `answer_start` field. 
+We translated the titles, context paragraphs, questions and answer spans from the original SQuAD2.0 dataset using [Amazon Translate](https://aws.amazon.com/translate/) - requiring us to remap the starting positions of the answer spans, since their positions were changed due to the automatic translation.
 
-- `*id`: a string feature.
-- `*title`: a string feature.
-- `*context`: a string feature.
-- `*question`: a string feature.
-- `*answers`: a dictionary feature containing:
-  - `*text`: a string feature.
-  - `answer_start`: a int32 feature.
+We performed an automatic post-processing step to populate the start positions for the answer spans. To do so, we have first looked at whether there was an exact match for the translated answer span in the translated context paragraph and if so, we kept the answer text along with this start position found.
+If no exact match was found, we looked for approximate matches using a character-level edit distance algorithm.
+
+We have excluded the question-answer pairs from the original dataset where neither an exact nor an approximate match was found in the translated version. Our `default` configuration corresponds to this version. 
+
+We have put the excluded examples in our `excluded` configuration. 
+
+As a result, the datasets in these two configurations are mutually exclusive.  Below are the details for the corresponding dataset splits.
 
 ### Data Splits
 
@@ -170,43 +211,49 @@ The SQuAD2.0 TR dataset has 2 splits: _train_ and _validation_. Below are the st
 | train      | 442      | 18776      | 61293                | 43498                  | 104,791 |
 | validation | 35       | 1204       | 2346                 | 5945                   | 8291    |
 
-In addition to the default configuration, we also include a different view of train split specifically for openqa setting. In this setting, we only provide question-answer pairs along with their contexts.  
+
+
+| Split   | Articles | Paragraphs | Questions wo/ answers | Total   |
+| ------- | -------- | ---------- | --------------------- | ------- |
+| train-excluded   | 440        | 13490          | 25528                 | 25528   |
+| dev-excluded     | 35        | 924          | 3582                     | 3582       |
+
+
+In addition to the default configuration, we also a different view of train split can be obtained specifically for openqa setting by combining the `train` and `train-excluded` splits. In this view, we only have question-answer pairs (without `answer_start` field) along with their contexts.  
 
 | Split      | Articles | Paragraphs | Questions w/ answers |  Total   |
 | ---------- | -------- | ---------- | -------------------- |  ------- |
 | openqa     | 442      | 18776      | 86821                |  86821   |
 
+More information on our translation strategy can be found in our linked paper.
 
 
-## Dataset Creation
+### Source Data
 
-### Curation Rationale
+This dataset used the original SQuAD2.0 dataset as its source data.
 
-We translated the titles, context paragraphs, questions and answer spans from the original SQuAD2.0 dataset using "Amazon Translate" - requiring us to remap the starting positions of the answer spans, since their positions were changed due to the automatic translation.
-We performed an automatic post-processing step to populate the start positions for the answer spans.
-To do so, we have first looked at whether there was an exact match for the translated answer span in the translated context paragraph and if so, we kept the answer text along with this start position found.
-If no exact match was found, we looked for approximate matches using a character-level edit distance algorithm.
-We have excluded the question-answer pairs from the original dataset where neither an exact nor an approximate match was found in the translated version.
-Our "default" configuration corresponds to this version.
-We have put the "excluded" examples in our "excluded" configuration.
-As a result, the datasets in these two configurations are mutually exclusive.
+### Licensing Information
 
-| Split   | Articles | Paragraphs | Questions wo/ answers | Total   |
-| ------- | -------- | ---------- | --------------------- | ------- |
-| train   | ?        | ?          | 25528                 | 25528   |
-| dev     | ?        | ?          | ?                     | ?       |
+The SQuAD-TR is released under [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/) in accordance with the license of SQuAD-2.0. 
 
+#### 🤗 HuggingFace datasets
+```py
+from datasets import load_dataset
 
-More information on our translation strategy can be found in our linked paper.
+squad_tr_standard_qa = load_dataset("[TBD]", "default")
+squad_tr_open_qa = load_dataset("[TBD]", "openqa")
+squad_tr_excluded = load_dataset("[TBD]", "excluded")
+xquad_tr = load_dataset("xquad", "xquad.tr") # External resource
 
-### Source Data
+```
+* Demo application 👉 [Link TBD].
 
-This dataset used the original SQuAD2.0 dataset as its source data.
+### 🔬 Reproducibility 
 
-### 🏷 Licensing Information
+You can find all code, models and samples of the input data here [link TBD].  Please feel free to reach out to us if you have any specific questions. 
 
-The SQuAD-TR is released under [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/) in accordance with the license of SQuAD-2.0. 
 
+  
 ### ✍️ Citation
 
 ```
@@ -214,4 +261,6 @@ The SQuAD-TR is released under [CC BY-SA 4.0](https://creativecommons.org/licens
 ```
  
 ## ❤ Acknowledgment
-This research was supported by the _[AWS Cloud Credits for Research Program](https://aws.amazon.com/government-education/research-and-technical-computing/cloud-credit-for-research/) (formerly AWS Research Grants)_.
+This research was supported by the _[AWS Cloud Credits for Research Program](https://aws.amazon.com/government-education/research-and-technical-computing/cloud-credit-for-research/) (formerly AWS Research Grants)_.
+
+We thank Alara Dirik, Almira Bağlar, Berfu Büyüköz, Berna Erden, Gökçe Uludoğan,  Havva Yüksel, Melih Barsbey, Murat Karademir, Selen Parlar, Tuğçe Ulutuğ, Utku Yavuz for their support on our application for AWS Cloud Credits for Research Program and Fatih Mehmet Güler for the valuable advice, discussion and insightful comments.