Datasets:

MongoDB
/

tech-news-embeddings

Tasks:

Question Answering

Text Generation

Modalities:

Formats:

Languages:

Size:

Tags:

semantic search

retrieval augmented generation

Libraries:

License:

Dataset card Data Studio Files Files and versions

RichmondMongo commited on Feb 23, 2024

Commit

d2a532d

·

verified ·

1 Parent(s): 6a5bd9b

Update README.md

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@@ -36,7 +36,7 @@ Each record in the dataset represents a news article about technology companies
 ## Data Ingestion
-[Create a free 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=richmond.alake) and conduct the Data Ingestion process below.
 ```python
 import os
@@ -61,6 +61,7 @@ dataset = load_dataset("AIatMongoDB/tech-news-embeddings")
 insert_data = []
 # Iterate through the dataset and prepare the documents for insertion
 for item in dataset['train']:
     # Convert the dataset item to MongoDB document format
     doc_item = json_util.loads(json_util.dumps(item))

 ## Data Ingestion
+[Create a free 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=richmond.alake)
 ```python
 import os
 insert_data = []
 # Iterate through the dataset and prepare the documents for insertion
+# The script below ingests 1000 records into the database at a time
 for item in dataset['train']:
     # Convert the dataset item to MongoDB document format
     doc_item = json_util.loads(json_util.dumps(item))