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README.md

@@ -4,140 +4,8 @@ tags:
 - rlfh
 - argilla
 - human-feedback
-dataset_info:
-  features:
-  - name: title
-    dtype: string
-    id: field
-  - name: question
-    dtype: string
-    id: field
-  - name: answer
-    dtype: string
-    id: field
-  - name: title_question_fit
-    list:
-    - name: user_id
-      dtype: string
-      id: question
-    - name: value
-      dtype: string
-      id: suggestion
-    - name: status
-      dtype: string
-      id: question
-  - name: title_question_fit-suggestion
-    dtype: string
-    id: suggestion
-  - name: title_question_fit-suggestion-metadata
-    struct:
-    - name: type
-      dtype: string
-      id: suggestion-metadata
-    - name: score
-      dtype: float32
-      id: suggestion-metadata
-    - name: agent
-      dtype: string
-      id: suggestion-metadata
-  - name: tags
-    list:
-    - name: user_id
-      dtype: string
-      id: question
-    - name: value
-      sequence: string
-      id: suggestion
-    - name: status
-      dtype: string
-      id: question
-  - name: tags-suggestion
-    sequence: string
-    id: suggestion
-  - name: tags-suggestion-metadata
-    struct:
-    - name: type
-      dtype: string
-      id: suggestion-metadata
-    - name: score
-      dtype: float32
-      id: suggestion-metadata
-    - name: agent
-      dtype: string
-      id: suggestion-metadata
-  - name: answer_quality
-    list:
-    - name: user_id
-      dtype: string
-      id: question
-    - name: value
-      dtype: int32
-      id: suggestion
-    - name: status
-      dtype: string
-      id: question
-  - name: answer_quality-suggestion
-    dtype: int32
-    id: suggestion
-  - name: answer_quality-suggestion-metadata
-    struct:
-    - name: type
-      dtype: string
-      id: suggestion-metadata
-    - name: score
-      dtype: float32
-      id: suggestion-metadata
-    - name: agent
-      dtype: string
-      id: suggestion-metadata
-  - name: new_answer
-    list:
-    - name: user_id
-      dtype: string
-      id: question
-    - name: value
-      dtype: string
-      id: suggestion
-    - name: status
-      dtype: string
-      id: question
-  - name: new_answer-suggestion
-    dtype: string
-    id: suggestion
-  - name: new_answer-suggestion-metadata
-    struct:
-    - name: type
-      dtype: string
-      id: suggestion-metadata
-    - name: score
-      dtype: float32
-      id: suggestion-metadata
-    - name: agent
-      dtype: string
-      id: suggestion-metadata
-  - name: external_id
-    dtype: string
-    id: external_id
-  - name: metadata
-    dtype: string
-    id: metadata
-  splits:
-  - name: train
-    num_bytes: 351704
-    num_examples: 200
-  download_size: 208405
-  dataset_size: 351704
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
 ---
 
-# FAKE RESPONSES NOTIFICATION
-
-This dataset contains fake responses and is used for demo purposes only.
-
 # Dataset Card for stackoverflow_feedback_demo
 
 This dataset has been created with [Argilla](https://docs.argilla.io).
@@ -156,7 +24,7 @@ As shown in the sections below, this dataset can be loaded into Argilla as expla
 
 This dataset contains:
 
-* A dataset configuration file conforming to the Argilla dataset format named `argilla.cfg`. This configuration file will be used to configure the dataset when using the `FeedbackDataset.from_huggingface` method in Argilla.
+* A dataset configuration file conforming to the Argilla dataset format named `argilla.yaml`. This configuration file will be used to configure the dataset when using the `FeedbackDataset.from_huggingface` method in Argilla.
 
 * Dataset records in a format compatible with HuggingFace `datasets`. These records will be loaded automatically when using `FeedbackDataset.from_huggingface` and can be loaded independently using the `datasets` library via `load_dataset`.
 
@@ -184,7 +52,7 @@ ds = load_dataset("argilla/stackoverflow_feedback_demo")
 
 ### Supported Tasks and Leaderboards
 
-This dataset can contain [multiple fields, questions and responses](https://docs.argilla.io/en/latest/guides/llms/conceptual_guides/data_model.html) so it can be used for different NLP tasks, depending on the configuration. The dataset structure is described in the [Dataset Structure section](#dataset-structure).
+This dataset can contain [multiple fields, questions and responses](https://docs.argilla.io/en/latest/conceptual_guides/data_model.html#feedback-dataset) so it can be used for different NLP tasks, depending on the configuration. The dataset structure is described in the [Dataset Structure section](#dataset-structure).
 
 There are no leaderboards associated with this dataset.
 
@@ -196,27 +64,29 @@ There are no leaderboards associated with this dataset.
 
 ### Data in Argilla
 
-The dataset is created in Argilla with: **fields**, **questions**, and **guidelines**.
+The dataset is created in Argilla with: **fields**, **questions**, **suggestions**, and **guidelines**.
 
 The **fields** are the dataset records themselves, for the moment just text fields are suppported. These are the ones that will be used to provide responses to the questions.
 
 | Field Name | Title | Type | Required | Markdown |
 | ---------- | ----- | ---- | -------- | -------- |
-| title | Title | TextField | True | False |
-| question | Question | TextField | True | True |
-| answer | Answer | TextField | True | True |
+| title | Title | FieldTypes.text | True | False |
+| question | Question | FieldTypes.text | True | True |
+| answer | Answer | FieldTypes.text | True | True |
 
 
-The **questions** are the questions that will be asked to the annotators. They can be of different types, such as rating, text, single choice, or multiple choice.
+The **questions** are the questions that will be asked to the annotators. They can be of different types, such as rating, text, label_selection, multi_label_selection, or ranking.
 
 | Question Name | Title | Type | Required | Description | Values/Labels |
 | ------------- | ----- | ---- | -------- | ----------- | ------------- |
-| title_question_fit | Does the title match the question? | LabelQuestion | True | N/A |  N/A  |
-| tags | What are the topics mentioned in this question? | MultiLabelQuestion | True | Select all that apply. |  N/A  |
-| answer_quality | Rate the quality of the answer: | RatingQuestion | True | N/A | [1, 2, 3, 4, 5] |
-| new_answer | If needed, correct the answer | TextQuestion | True | If the rating is below 4, please provide a corrected answer |  N/A  |
+| title_question_fit | Does the title match the question? | QuestionTypes.label_selection | True | N/A | ['yes', 'no'] |
+| tags | What are the topics mentioned in this question? | QuestionTypes.multi_label_selection | True | Select all that apply. | ['python', 'django', 'python-2.7', 'list', 'python-3.x', 'numpy', 'pandas', 'regex', 'dictionary', 'string', 'matplotlib', 'arrays', 'google-app-engine', 'csv', 'tkinter', 'flask', 'json', 'linux', 'mysql', 'html', 'function', 'file', 'class', 'algorithm', 'windows', 'scipy', 'loops', 'multithreading', 'beautifulsoup', 'django-models', 'for-loop', 'javascript', 'xml', 'sqlalchemy', 'parsing', 'performance', 'datetime', 'osx', 'sorting', 'unicode', 'c++', 'dataframe', 'selenium', 'subprocess', 'pygame', 'java', 'pyqt', 'pip', 'tuples', 'scrapy'] |
+| answer_quality | Rate the quality of the answer: | QuestionTypes.rating | True | N/A | [1, 2, 3, 4, 5] |
+| new_answer | If needed, correct the answer | QuestionTypes.text | False | If the rating is below 4, please provide a corrected answer | N/A |
 
 
+**✨ NEW** Additionally, we also have **suggestions**, which are linked to the existing questions, and so on, named appending "-suggestion" and "-suggestion-metadata" to those, containing the value/s of the suggestion and its metadata, respectively. So on, the possible values are the same as in the table above.
+
 Finally, the **guidelines** are just a plain string that can be used to provide instructions to the annotators. Find those in the [annotation guidelines](#annotation-guidelines) section.
 
 ### Data Instances
@@ -231,48 +101,8 @@ An example of a dataset instance in Argilla looks as follows:
         "question": "\u003cp\u003eI am using the Photoshop\u0027s javascript API to find the fonts in a given PSD.\u003c/p\u003e\n\n\u003cp\u003eGiven a font name returned by the API, I want to find the actual physical font file that that font name corresponds to on the disc.\u003c/p\u003e\n\n\u003cp\u003eThis is all happening in a python program running on OSX so I guess I\u0027m looking for one of:\u003c/p\u003e\n\n\u003cul\u003e\n\u003cli\u003eSome Photoshop javascript\u003c/li\u003e\n\u003cli\u003eA Python function\u003c/li\u003e\n\u003cli\u003eAn OSX API that I can call from python\u003c/li\u003e\n\u003c/ul\u003e\n",
         "title": "How can I find the full path to a font from its display name on a Mac?"
     },
-    "metadata": null,
+    "metadata": {},
     "responses": [
-        {
-            "status": "submitted",
-            "user_id": null,
-            "values": {
-                "answer_quality": {
-                    "value": 1
-                },
-                "new_answer": {
-                    "value": "Sample answer"
-                },
-                "tags": {
-                    "value": [
-                        "arrays"
-                    ]
-                },
-                "title_question_fit": {
-                    "value": "yes"
-                }
-            }
-        },
-        {
-            "status": "submitted",
-            "user_id": null,
-            "values": {
-                "answer_quality": {
-                    "value": 5
-                },
-                "new_answer": {
-                    "value": "Sample answer"
-                },
-                "tags": {
-                    "value": [
-                        "linux"
-                    ]
-                },
-                "title_question_fit": {
-                    "value": "no"
-                }
-            }
-        },
         {
             "status": "submitted",
             "user_id": null,
@@ -293,7 +123,8 @@ An example of a dataset instance in Argilla looks as follows:
                 }
             }
         }
-    ]
+    ],
+    "suggestions": []
 }
 ```
 
@@ -302,83 +133,63 @@ While the same record in HuggingFace `datasets` looks as follows:
 ```json
 {
     "answer": "\u003cp\u003eUnfortunately the only API that isn\u0027t deprecated is located in the ApplicationServices framework, which doesn\u0027t have a bridge support file, and thus isn\u0027t available in the bridge. If you\u0027re wanting to use ctypes, you can use ATSFontGetFileReference after looking up the ATSFontRef.\u003c/p\u003e\r\n\r\n\u003cp\u003eCocoa doesn\u0027t have any native support, at least as of 10.5, for getting the location of a font.\u003c/p\u003e",
-    "answer_quality": {
-        "status": [
-            "submitted",
-            "submitted",
-            "submitted"
-        ],
-        "user_id": [
-            null,
-            null,
-            null
-        ],
-        "value": [
-            1,
-            5,
-            1
-        ]
+    "answer_quality": [
+        {
+            "status": "submitted",
+            "user_id": null,
+            "value": 1
+        }
+    ],
+    "answer_quality-suggestion": null,
+    "answer_quality-suggestion-metadata": {
+        "agent": null,
+        "score": null,
+        "type": null
     },
     "external_id": null,
-    "metadata": null,
-    "new_answer": {
-        "status": [
-            "submitted",
-            "submitted",
-            "submitted"
-        ],
-        "user_id": [
-            null,
-            null,
-            null
-        ],
-        "value": [
-            "Sample answer",
-            "Sample answer",
-            "Sample answer"
-        ]
+    "metadata": "{}",
+    "new_answer": [
+        {
+            "status": "submitted",
+            "user_id": null,
+            "value": "Sample answer"
+        }
+    ],
+    "new_answer-suggestion": null,
+    "new_answer-suggestion-metadata": {
+        "agent": null,
+        "score": null,
+        "type": null
     },
     "question": "\u003cp\u003eI am using the Photoshop\u0027s javascript API to find the fonts in a given PSD.\u003c/p\u003e\n\n\u003cp\u003eGiven a font name returned by the API, I want to find the actual physical font file that that font name corresponds to on the disc.\u003c/p\u003e\n\n\u003cp\u003eThis is all happening in a python program running on OSX so I guess I\u0027m looking for one of:\u003c/p\u003e\n\n\u003cul\u003e\n\u003cli\u003eSome Photoshop javascript\u003c/li\u003e\n\u003cli\u003eA Python function\u003c/li\u003e\n\u003cli\u003eAn OSX API that I can call from python\u003c/li\u003e\n\u003c/ul\u003e\n",
-    "tags": {
-        "status": [
-            "submitted",
-            "submitted",
-            "submitted"
-        ],
-        "user_id": [
-            null,
-            null,
-            null
-        ],
-        "value": [
-            [
-                "arrays"
-            ],
-            [
-                "linux"
-            ],
-            [
+    "tags": [
+        {
+            "status": "submitted",
+            "user_id": null,
+            "value": [
                 "tkinter"
             ]
-        ]
+        }
+    ],
+    "tags-suggestion": null,
+    "tags-suggestion-metadata": {
+        "agent": null,
+        "score": null,
+        "type": null
     },
     "title": "How can I find the full path to a font from its display name on a Mac?",
-    "title_question_fit": {
-        "status": [
-            "submitted",
-            "submitted",
-            "submitted"
-        ],
-        "user_id": [
-            null,
-            null,
-            null
-        ],
-        "value": [
-            "yes",
-            "no",
-            "yes"
-        ]
+    "title_question_fit": [
+        {
+            "status": "submitted",
+            "user_id": null,
+            "value": "yes"
+        }
+    ],
+    "title_question_fit-suggestion": null,
+    "title_question_fit-suggestion-metadata": {
+        "agent": null,
+        "score": null,
+        "type": null
     }
 }
 ```
@@ -389,16 +200,23 @@ Among the dataset fields, we differentiate between the following:
 
 * **Fields:** These are the dataset records themselves, for the moment just text fields are suppported. These are the ones that will be used to provide responses to the questions.
     
-    * **title** is of type `TextField`.
-    * **question** is of type `TextField`.
-    * **answer** is of type `TextField`.
+    * **title** is of type `FieldTypes.text`.
+    * **question** is of type `FieldTypes.text`.
+    * **answer** is of type `FieldTypes.text`.
+
+* **Questions:** These are the questions that will be asked to the annotators. They can be of different types, such as `RatingQuestion`, `TextQuestion`, `LabelQuestion`, `MultiLabelQuestion`, and `RankingQuestion`.
+    
+    * **title_question_fit** is of type `QuestionTypes.label_selection` with the following allowed values ['yes', 'no'].
+    * **tags** is of type `QuestionTypes.multi_label_selection` with the following allowed values ['python', 'django', 'python-2.7', 'list', 'python-3.x', 'numpy', 'pandas', 'regex', 'dictionary', 'string', 'matplotlib', 'arrays', 'google-app-engine', 'csv', 'tkinter', 'flask', 'json', 'linux', 'mysql', 'html', 'function', 'file', 'class', 'algorithm', 'windows', 'scipy', 'loops', 'multithreading', 'beautifulsoup', 'django-models', 'for-loop', 'javascript', 'xml', 'sqlalchemy', 'parsing', 'performance', 'datetime', 'osx', 'sorting', 'unicode', 'c++', 'dataframe', 'selenium', 'subprocess', 'pygame', 'java', 'pyqt', 'pip', 'tuples', 'scrapy'], and description "Select all that apply.".
+    * **answer_quality** is of type `QuestionTypes.rating` with the following allowed values [1, 2, 3, 4, 5].
+    * (optional) **new_answer** is of type `QuestionTypes.text`, and description "If the rating is below 4, please provide a corrected answer".
 
-* **Questions:** These are the questions that will be asked to the annotators. They can be of different types, such as rating, text, single choice, or multiple choice.
+* **✨ NEW** **Suggestions:** As of Argilla 1.13.0, the suggestions have been included to provide the annotators with suggestions to ease or assist during the annotation process. Suggestions are linked to the existing questions, are always optional, and contain not just the suggestion itself, but also the metadata linked to it, if applicable.
     
-    * **title_question_fit** is of type `LabelQuestion`.
-    * **tags** is of type `MultiLabelQuestion`, and description "Select all that apply.".
-    * **answer_quality** is of type `RatingQuestion` with the following allowed values [1, 2, 3, 4, 5].
-    * (optional) **new_answer** is of type `TextQuestion`, and description "If the rating is below 4, please provide a corrected answer".
+    * (optional) **title_question_fit-suggestion** is of type `QuestionTypes.label_selection` with the following allowed values ['yes', 'no'].
+    * (optional) **tags-suggestion** is of type `QuestionTypes.multi_label_selection` with the following allowed values ['python', 'django', 'python-2.7', 'list', 'python-3.x', 'numpy', 'pandas', 'regex', 'dictionary', 'string', 'matplotlib', 'arrays', 'google-app-engine', 'csv', 'tkinter', 'flask', 'json', 'linux', 'mysql', 'html', 'function', 'file', 'class', 'algorithm', 'windows', 'scipy', 'loops', 'multithreading', 'beautifulsoup', 'django-models', 'for-loop', 'javascript', 'xml', 'sqlalchemy', 'parsing', 'performance', 'datetime', 'osx', 'sorting', 'unicode', 'c++', 'dataframe', 'selenium', 'subprocess', 'pygame', 'java', 'pyqt', 'pip', 'tuples', 'scrapy'].
+    * (optional) **answer_quality-suggestion** is of type `QuestionTypes.rating` with the following allowed values [1, 2, 3, 4, 5].
+    * (optional) **new_answer-suggestion** is of type `QuestionTypes.text`.
 
 Additionally, we also have one more field which is optional and is the following: