Upload folder using huggingface_hub

.argilla/dataset.json

@@ -0,0 +1,16 @@
+{
+  "id": "88840a37-5d1c-4967-9c89-a9a16d4bfad9",
+  "name": "blog_posts_classified",
+  "guidelines": "Pre-annotated blog posts with manual labels. Please verify and adjust the classifications as needed.",
+  "allow_extra_metadata": false,
+  "status": "ready",
+  "distribution": {
+    "strategy": "overlap",
+    "min_submitted": 1
+  },
+  "metadata": null,
+  "workspace_id": "c19af2a7-2281-4c1d-8d77-236ae33465d6",
+  "last_activity_at": "2025-01-19T19:16:16.880298",
+  "inserted_at": "2025-01-16T03:08:33.075762",
+  "updated_at": "2025-01-16T03:08:35.204326"
+}

.argilla/settings.json

@@ -0,0 +1,208 @@
+{
+  "guidelines": "Pre-annotated blog posts with manual labels. Please verify and adjust the classifications as needed.",
+  "allow_extra_metadata": false,
+  "distribution": {
+    "strategy": "overlap",
+    "min_submitted": 1
+  },
+  "fields": [
+    {
+      "id": "1f0e84c5-2514-4163-a1e6-304201da98e1",
+      "name": "title",
+      "title": "Blog Post Title",
+      "required": true,
+      "settings": {
+        "type": "text",
+        "use_markdown": false
+      },
+      "dataset_id": "88840a37-5d1c-4967-9c89-a9a16d4bfad9",
+      "inserted_at": "2025-01-16T03:08:33.869561",
+      "updated_at": "2025-01-16T03:08:33.869561"
+    },
+    {
+      "id": "ec24600e-c201-485e-93c1-f2879f308d97",
+      "name": "authors",
+      "title": "Authors",
+      "required": true,
+      "settings": {
+        "type": "text",
+        "use_markdown": false
+      },
+      "dataset_id": "88840a37-5d1c-4967-9c89-a9a16d4bfad9",
+      "inserted_at": "2025-01-16T03:08:34.116371",
+      "updated_at": "2025-01-16T03:08:34.116371"
+    },
+    {
+      "id": "d7bfcc44-0dcb-434a-b899-6e6d2e86aebf",
+      "name": "filename",
+      "title": "Source Filename",
+      "required": true,
+      "settings": {
+        "type": "text",
+        "use_markdown": false
+      },
+      "dataset_id": "88840a37-5d1c-4967-9c89-a9a16d4bfad9",
+      "inserted_at": "2025-01-16T03:08:34.373369",
+      "updated_at": "2025-01-16T03:08:34.373369"
+    },
+    {
+      "id": "eae3ed0a-e907-49d4-917c-c6f4bda39cbe",
+      "name": "content",
+      "title": "Blog Content",
+      "required": true,
+      "settings": {
+        "type": "text",
+        "use_markdown": false
+      },
+      "dataset_id": "88840a37-5d1c-4967-9c89-a9a16d4bfad9",
+      "inserted_at": "2025-01-16T03:08:34.597688",
+      "updated_at": "2025-01-16T03:08:34.597688"
+    }
+  ],
+  "questions": [
+    {
+      "id": "a8e2b25b-fe8b-467c-ab90-416124d9a8e4",
+      "name": "content_class",
+      "title": "What topics does this blog post cover?",
+      "description": "Select all topics that apply to this blog post",
+      "required": true,
+      "settings": {
+        "type": "multi_label_selection",
+        "options": [
+          {
+            "value": "llm",
+            "text": "LLM",
+            "description": null
+          },
+          {
+            "value": "computer_vision",
+            "text": "Computer-Vision",
+            "description": null
+          },
+          {
+            "value": "audio",
+            "text": "Audio",
+            "description": null
+          },
+          {
+            "value": "transformers",
+            "text": "Transformers",
+            "description": null
+          },
+          {
+            "value": "data",
+            "text": "Data",
+            "description": null
+          },
+          {
+            "value": "mlops",
+            "text": "MLOps",
+            "description": null
+          },
+          {
+            "value": "research",
+            "text": "Research",
+            "description": null
+          },
+          {
+            "value": "implementation",
+            "text": "Implementation",
+            "description": null
+          },
+          {
+            "value": "benchmarks",
+            "text": "Benchmarks",
+            "description": null
+          },
+          {
+            "value": "tutorial",
+            "text": "Tutorial",
+            "description": null
+          },
+          {
+            "value": "community",
+            "text": "Community",
+            "description": null
+          },
+          {
+            "value": "security",
+            "text": "Security",
+            "description": null
+          },
+          {
+            "value": "optimization",
+            "text": "Optimization",
+            "description": null
+          },
+          {
+            "value": "deployment",
+            "text": "Deployment",
+            "description": null
+          },
+          {
+            "value": "tools",
+            "text": "Tools",
+            "description": null
+          },
+          {
+            "value": "text_generation",
+            "text": "Text-Generation",
+            "description": null
+          },
+          {
+            "value": "text_classification",
+            "text": "Text-Classification",
+            "description": null
+          },
+          {
+            "value": "translation",
+            "text": "Translation",
+            "description": null
+          },
+          {
+            "value": "image_generation",
+            "text": "Image-Generation",
+            "description": null
+          },
+          {
+            "value": "multi_modal",
+            "text": "Multi-Modal",
+            "description": null
+          },
+          {
+            "value": "quantization",
+            "text": "Quantization",
+            "description": null
+          },
+          {
+            "value": "fine_tuning",
+            "text": "Fine-Tuning",
+            "description": null
+          },
+          {
+            "value": "integration",
+            "text": "Integration",
+            "description": null
+          },
+          {
+            "value": "efficient_computing",
+            "text": "Efficient-Computing",
+            "description": null
+          },
+          {
+            "value": "robotics",
+            "text": "Robotics",
+            "description": null
+          }
+        ],
+        "visible_options": 8,
+        "options_order": "natural"
+      },
+      "dataset_id": "88840a37-5d1c-4967-9c89-a9a16d4bfad9",
+      "inserted_at": "2025-01-16T03:08:34.877040",
+      "updated_at": "2025-01-16T03:08:34.877040"
+    }
+  ],
+  "metadata": [],
+  "vectors": []
+}

.argilla/version.json

@@ -0,0 +1,3 @@
+{
+  "argilla": "2.6.0"
+}

README.md

@@ -1,46 +1,147 @@
 ---
-dataset_info:
-  features:
-  - name: id
-    dtype: string
-  - name: status
-    dtype: string
-  - name: inserted_at
-    dtype: timestamp[us]
-  - name: updated_at
-    dtype: timestamp[us]
-  - name: _server_id
-    dtype: string
-  - name: title
-    dtype: string
-  - name: authors
-    dtype: string
-  - name: filename
-    dtype: string
-  - name: content
-    dtype: string
-  - name: content_class.responses
-    sequence:
-      sequence: string
-  - name: content_class.responses.users
-    sequence: string
-  - name: content_class.responses.status
-    sequence: string
-  - name: content_class.suggestion
-    sequence: string
-  - name: content_class.suggestion.agent
-    dtype: 'null'
-  - name: content_class.suggestion.score
-    dtype: 'null'
-  splits:
-  - name: train
-    num_bytes: 5680715
-    num_examples: 507
-  download_size: 2923635
-  dataset_size: 5680715
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
+tags:
+- rlfh
+- argilla
+- human-feedback
 ---
+
+# Dataset Card for blog_posts_classified
+
+
+
+
+
+
+
+This dataset has been created with [Argilla](https://github.com/argilla-io/argilla). As shown in the sections below, this dataset can be loaded into your Argilla server as explained in [Load with Argilla](#load-with-argilla), or used directly with the `datasets` library in [Load with `datasets`](#load-with-datasets).
+
+
+## Using this dataset with Argilla
+
+To load with Argilla, you'll just need to install Argilla as `pip install argilla --upgrade` and then use the following code:
+
+```python
+import argilla as rg
+
+ds = rg.Dataset.from_hub("fdaudens/blog_posts_classified", settings="auto")
+```
+
+This will load the settings and records from the dataset repository and push them to you Argilla server for exploration and annotation.
+
+## Using this dataset with `datasets`
+
+To load the records of this dataset with `datasets`, you'll just need to install `datasets` as `pip install datasets --upgrade` and then use the following code:
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("fdaudens/blog_posts_classified")
+```
+
+This will only load the records of the dataset, but not the Argilla settings.
+
+## Dataset Structure
+
+This dataset repo contains:
+
+* Dataset records in a format compatible with HuggingFace `datasets`. These records will be loaded automatically when using `rg.Dataset.from_hub` and can be loaded independently using the `datasets` library via `load_dataset`.
+* The [annotation guidelines](#annotation-guidelines) that have been used for building and curating the dataset, if they've been defined in Argilla.
+* A dataset configuration folder conforming to the Argilla dataset format in `.argilla`.
+
+The dataset is created in Argilla with: **fields**, **questions**, **suggestions**, **metadata**, **vectors**, and **guidelines**.
+
+### Fields
+
+The **fields** are the features or text of a dataset's records. For example, the 'text' column of a text classification dataset of the 'prompt' column of an instruction following dataset.
+
+| Field Name | Title | Type | Required |
+| ---------- | ----- | ---- | -------- |
+| title | Blog Post Title | text | True |
+| authors | Authors | text | True |
+| filename | Source Filename | text | True |
+| content | Blog Content | text | True |
+
+
+### Questions
+
+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 |
+| ------------- | ----- | ---- | -------- | ----------- | ------------- |
+| content_class | What topics does this blog post cover? | multi_label_selection | True | Select all topics that apply to this blog post | ['llm', 'computer_vision', 'audio', 'transformers', 'data', 'mlops', 'research', 'implementation', 'benchmarks', 'tutorial', 'community', 'security', 'optimization', 'deployment', 'tools', 'text_generation', 'text_classification', 'translation', 'image_generation', 'multi_modal', 'quantization', 'fine_tuning', 'integration', 'efficient_computing', 'robotics'] |
+
+
+<!-- check length of metadata properties -->
+
+
+
+
+### Data Splits
+
+The dataset contains a single split, which is `train`.
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed]
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+#### Annotation guidelines
+
+Pre-annotated blog posts with manual labels. Please verify and adjust the classifications as needed.
+
+#### Annotation process
+
+[More Information Needed]
+
+#### Who are the annotators?
+
+[More Information Needed]
+
+### Personal and Sensitive Information
+
+[More Information Needed]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed]
+
+### Licensing Information
+
+[More Information Needed]
+
+### Citation Information
+
+[More Information Needed]
+
+### Contributions
+
+[More Information Needed]