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--- |
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license: apache-2.0 |
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task_categories: |
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- question-answering |
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- text-retrieval |
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- text-to-image |
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language: |
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- en |
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tags: |
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- vector search |
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- multimodal |
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- retrieval augmented generation |
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size_categories: |
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- 1K<n<10K |
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--- |
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## Overview |
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This dataset consists of AirBnB listings with property descriptions, reviews, and other metadata. |
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It also contains text embeddings of the property descriptions as well as image embeddings of the listing image. The text embeddings were created using OpenAI's **text-embedding-3-small** model and the image embeddings using OpenAI's [**clip-vit-base-patch32**](https://huggingface.co/openai/clip-vit-base-patch32) model available on Hugging Face. |
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## Dataset Structure |
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Here is a full list of fields contained in the dataset. Some noteworthy fields have been highlighted: |
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- _id: Unique identifier for the listing |
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- listing_url: URL for the listing on AirBnB |
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- **name**: Title or name of the listing |
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- **summary**: Short overview of listing |
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- **space**: Short description of the space, amenities etc. |
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- **description**: Full listing description |
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- neighborhood_overview: Description of surrounding area |
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- notes: Special instructions or notes |
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- transit: Nearby public transportation options |
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- access: How to access the property. Door codes etc. |
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- interaction: Host's preferred interaction medium |
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- house_rules: Rules guests must follow |
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- **property_type**: Type of property |
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- room_type: Listing's room category |
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- bed_type: Type of bed provided |
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- minimum_nights: Minimum stay required |
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- maximum_nights: Maximum stay allowed |
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- cancellation_policy: Terms for cancelling booking |
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- first_review: Date of first review |
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- last_review: Date of latest review |
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- **accommodates**: Number of guests accommodated |
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- **bedrooms**: Number of bedrooms available |
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- **beds**: Number of beds available |
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- number_of_reviews: Total reviews received |
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- bathrooms: Number of bathrooms available |
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- **amenities**: List of amenities offered |
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- **price**: Nightly price for listing |
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- security_deposit: Required security deposit amount |
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- cleaning_fee: Additional cleaning fee charged |
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- extra_people: Fee for additional guests |
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- guests_included: Number of guests included in the base price |
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- **images**: Links to listing images |
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- host: Information about the host |
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- **address**: Physical address of listing |
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- **availability**: Availability dates for listing |
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- **review_scores**: Aggregate review scores |
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- reviews: Individual guest reviews |
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- weekly_price: Discounted price for week |
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- monthly_price: Discounted price for month |
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- **text_embeddings**: Embeddings of the property description in the `space` field |
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- **image_embeddings**: Embeddings of the `picture_url` in the `images` field |
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## Usage |
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This dataset can be useful for: |
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- Building Multimodal Search applications. Embed text queries using the CLIP model, and retrieve relevant images using the image embeddings provided. |
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- Building Hybrid Search applications. Use the embeddings provided for vector search and the metadata fields for pre-filtering and/or full-text search. |
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- Building RAG applications |
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## Ingest Data |
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To experiment with this dataset using MongoDB Atlas, first [create a MongoDB Atlas account](https://www.mongodb.com/cloud/atlas/register?utm_campaign=devrel&utm_source=community&utm_medium=organic_social&utm_content=Hugging%20Face%20Dataset&utm_term=apoorva.joshi). |
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You can then use the following script to load this dataset into your MongoDB Atlas cluster: |
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``` |
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import os |
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from pymongo import MongoClient |
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import datasets |
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from datasets import load_dataset |
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from bson import json_util |
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# MongoDB Atlas URI and client setup |
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uri = os.environ.get('MONGODB_ATLAS_URI') |
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client = MongoClient(uri) |
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# Change to the appropriate database and collection names |
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db_name = 'your_database_name' # Change this to your actual database name |
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collection_name = 'airbnb_embeddings' # Change this to your actual collection name |
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collection = client[db_name][collection_name] |
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# Load the "airbnb_embeddings" dataset from Hugging Face |
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dataset = load_dataset("MongoDB/airbnb_embeddings") |
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insert_data = [] |
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# Iterate through the dataset and prepare the documents for insertion |
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# The script below ingests 1000 records into the database at a time |
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for item in dataset['train']: |
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# Convert the dataset item to MongoDB document format |
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doc_item = json_util.loads(json_util.dumps(item)) |
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insert_data.append(doc_item) |
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# Insert in batches of 1000 documents |
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if len(insert_data) == 1000: |
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collection.insert_many(insert_data) |
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print("1000 records ingested") |
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insert_data = [] |
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# Insert any remaining documents |
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if len(insert_data) > 0: |
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collection.insert_many(insert_data) |
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print("{} records ingested".format(len(insert_data))) |
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print("All records ingested successfully!") |
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``` |