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README.md

@@ -4,136 +4,6 @@ tags:
 - rlfh
 - argilla
 - human-feedback
-dataset_info:
-  features:
-  - name: prompt
-    dtype: string
-    id: field
-  - name: response
-    dtype: string
-    id: field
-  - name: relevant
-    list:
-    - name: user_id
-      dtype: string
-      id: question
-    - name: value
-      dtype: string
-      id: suggestion
-    - name: status
-      dtype: string
-      id: question
-  - name: relevant-suggestion
-    dtype: string
-    id: suggestion
-  - name: relevant-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: content_class
-    list:
-    - name: user_id
-      dtype: string
-      id: question
-    - name: value
-      sequence: string
-      id: suggestion
-    - name: status
-      dtype: string
-      id: question
-  - name: content_class-suggestion
-    sequence: string
-    id: suggestion
-  - name: content_class-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: rating
-    list:
-    - name: user_id
-      dtype: string
-      id: question
-    - name: value
-      dtype: int32
-      id: suggestion
-    - name: status
-      dtype: string
-      id: question
-  - name: rating-suggestion
-    dtype: int32
-    id: suggestion
-  - name: rating-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: corrected-text
-    list:
-    - name: user_id
-      dtype: string
-      id: question
-    - name: value
-      dtype: string
-      id: suggestion
-    - name: status
-      dtype: string
-      id: question
-  - name: corrected-text-suggestion
-    dtype: string
-    id: suggestion
-  - name: corrected-text-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
-  - name: vectors
-    struct:
-    - name: prompt
-      sequence: float32
-      id: vectors
-  splits:
-  - name: train
-    num_bytes: 6458850
-    num_examples: 5590
-  download_size: 3574600
-  dataset_size: 6458850
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
 ---
 
 # Dataset Card for oasst_response_quality
@@ -219,6 +89,13 @@ The **suggestions** are human or machine generated recommendations for each ques
 The **metadata** is a dictionary that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the `metadata_properties` defined in the dataset configuration file in `argilla.yaml`.
 
 
+**✨ NEW** The **vectors** are different columns that contain a vector in floating point, which is constraint to the pre-defined dimensions in the **vectors_settings** when configuring the vectors within the dataset itself, also the dimensions will always be 1-dimensional. The **vectors** are optional and identified by the pre-defined vector name in the dataset configuration file in `argilla.yaml`.
+
+| Vector Name | Title | Dimensions |
+|-------------|-------|------------|
+| prompt | Prompt | [1, 2] |
+
+
 
 | Metadata Name | Title | Type | Values | Visible for Annotators |
 | ------------- | ----- | ---- | ------ | ---------------------- |
@@ -240,7 +117,12 @@ An example of a dataset instance in Argilla looks as follows:
     "metadata": {},
     "responses": [],
     "suggestions": [],
-    "vectors": {}
+    "vectors": {
+        "prompt": [
+            1,
+            2
+        ]
+    }
 }
 ```
 
@@ -279,7 +161,13 @@ While the same record in HuggingFace `datasets` looks as follows:
         "score": null,
         "type": null
     },
-    "response": "Sure! Let\u0027s say you want to build a model which can distinguish between images of cats and dogs. You gather your dataset, consisting of many cat and dog pictures. Then you put them through a neural net of your choice, which produces some representation for each image, a sequence of numbers like [0.123, 0.045, 0.334, ...]. The problem is, if your model is unfamiliar with cat and dog images, these representations will be quite random. At one time a cat and a dog picture could have very similar representations (their numbers would be close to each other), while at others two cat images may be represented far apart. In simple terms, the model wouldn\u0027t be able to tell cats and dogs apart. This is where contrastive learning comes in.\n\nThe point of contrastive learning is to take pairs of samples (in this case images of cats and dogs), then train the model to \"pull\" representations of similar pairs (cat-cat or dog-dog) closer to each other and \"push\" representations of different pairs (cat-dog) apart. After doing this for a sufficient number of steps, your model will be able to produce unique, reliable representations for cats and dogs, in essence tell them apart.\n\nThis method is not limited to images, you can typically use it with any dataset that has similar and dissimilar data points."
+    "response": "Sure! Let\u0027s say you want to build a model which can distinguish between images of cats and dogs. You gather your dataset, consisting of many cat and dog pictures. Then you put them through a neural net of your choice, which produces some representation for each image, a sequence of numbers like [0.123, 0.045, 0.334, ...]. The problem is, if your model is unfamiliar with cat and dog images, these representations will be quite random. At one time a cat and a dog picture could have very similar representations (their numbers would be close to each other), while at others two cat images may be represented far apart. In simple terms, the model wouldn\u0027t be able to tell cats and dogs apart. This is where contrastive learning comes in.\n\nThe point of contrastive learning is to take pairs of samples (in this case images of cats and dogs), then train the model to \"pull\" representations of similar pairs (cat-cat or dog-dog) closer to each other and \"push\" representations of different pairs (cat-dog) apart. After doing this for a sufficient number of steps, your model will be able to produce unique, reliable representations for cats and dogs, in essence tell them apart.\n\nThis method is not limited to images, you can typically use it with any dataset that has similar and dissimilar data points.",
+    "vectors": {
+        "prompt": [
+            1.0,
+            2.0
+        ]
+    }
 }
 ```
 
@@ -307,6 +195,10 @@ Among the dataset fields, we differentiate between the following:
     * (optional) **corrected-text-suggestion** is of type `QuestionTypes.text`.
 
 
+* **✨ NEW** **Vectors**: As of Argilla 1.19.0, the vectors have been included in order to add support for similarity search to explore similar records based on vector search powered by the search engine defined. The vectors are optional and cannot be seen within the UI, those are uploaded and internally used. Also the vectors will always be optional, and only the dimensions previously defined in their settings.
+    
+    * (optional) **prompt** is of type `float32` and has a dimension of (1, `2`).
+
 
 Additionally, we also have two more fields that are optional and are the following: