Add information for the dataset

README.md

@@ -30,6 +30,10 @@ This dataset contains:
 
 * The [annotation guidelines](#annotation-guidelines) that have been used for building and curating the dataset, if they've been defined in Argilla.
 
+It contains the raw version of [go_emotions](https://huggingface.co/datasets/go_emotions) as a `FeedbackDataset`. Each of the original questions are defined a single
+`FeedbackRecord` and contain the `responses` from each annotator. The final labels in the *simplified* version of the dataset have been used as `suggestions`, so that we
+can use this dataset to showcase the metrics related to the agreement between annotators as well as the `responses` vs `suggestions` metrics.
+
 ### Load with Argilla
 
 To load with Argilla, you'll just need to install Argilla as `pip install argilla --upgrade` and then use the following code:
@@ -265,6 +269,119 @@ The dataset contains a single split, which is `train`.
 
 ## Dataset Creation
 
+### Script used for the generation
+
+```python
+import argilla as rg
+from datasets import load_dataset
+import uuid
+from datasets import concatenate_datasets
+
+ds = load_dataset("go_emotions", "raw", split="train")
+ds_prepared = load_dataset("go_emotions")
+
+_CLASS_NAMES = [
+    "admiration",
+    "amusement",
+    "anger",
+    "annoyance",
+    "approval",
+    "caring",
+    "confusion",
+    "curiosity",
+    "desire",
+    "disappointment",
+    "disapproval",
+    "disgust",
+    "embarrassment",
+    "excitement",
+    "fear",
+    "gratitude",
+    "grief",
+    "joy",
+    "love",
+    "nervousness",
+    "optimism",
+    "pride",
+    "realization",
+    "relief",
+    "remorse",
+    "sadness",
+    "surprise",
+    "neutral",
+]
+label_to_id = {label: i for i, label in enumerate(_CLASS_NAMES)}
+id_to_label = {i: label for i, label in enumerate(_CLASS_NAMES)}
+
+# Concatenate the datasets and transform to pd.DataFrame
+
+ds_prepared = concatenate_datasets([ds_prepared["train"], ds_prepared["validation"], ds_prepared["test"]])
+df_prepared = ds_prepared.to_pandas()
+
+# Obtain the final labels as a dict, to later include these as suggestions
+
+labels_prepared = {}
+for idx in df_prepared.index:
+    labels = [id_to_label[label_id] for label_id in df_prepared['labels'][idx]]
+    labels_prepared[df_prepared['id'][idx]] = labels
+
+# Add labels to the dataset and keep only the relevant columns
+
+def add_labels(ex):
+    labels = []
+    for label in _CLASS_NAMES:
+        if ex[label] == 1:
+            labels.append(label)
+    ex["labels"] = labels
+    
+    return ex
+    
+ds = ds.map(add_labels)
+df = ds.select_columns(["text", "labels", "rater_id", "id"]).to_pandas()
+
+# Create a FeedbackDataset for text classification
+
+feedback_dataset = rg.FeedbackDataset.for_text_classification(labels=_CLASS_NAMES, multi_label=True)
+
+# Create the records with the original responses, and use as suggestions
+# the final labels in the "simplified" go_emotions dataset.
+
+records = []
+for text, df_text in df.groupby("text"):
+    responses = []
+    for rater_id, df_raters in df_text.groupby("rater_id"):
+        responses.append(
+            {
+                "values": {"label": {"value": df_raters["labels"].iloc[0].tolist()}},
+                "status": "submitted",
+                "user_id": uuid.UUID(int=rater_id),
+            }
+        )
+    suggested_labels = labels_prepared.get(df_raters["id"].iloc[0], None)
+    if not suggested_labels:
+        continue
+    suggestion = [
+        {
+            "question_name": "label",
+            "value": suggested_labels,
+            "type": "human",
+        }
+    ]
+    records.append(
+        rg.FeedbackRecord(
+            fields={"text": df_raters["text"].iloc[0]},
+            responses=responses,
+            suggestions=suggestion
+        )
+    )
+
+
+feedback_dataset.add_records(records)
+
+# Push to the hub
+feedback_dataset.push_to_huggingface("plaguss/go_emotions_raw")
+```
+
 ### Curation Rationale
 
 [More Information Needed]