Row 4188

Row ID: 4188 | Dataset Entry | Axioma AXP Content Repository

Content Data

This page contains data entry 4188 from the Axioma AXP content repository. The structured data below represents the complete record for this entry.

Hi everyone, I want to create an **embedding model** for my own dataset of recipes. The goal is to create a neural network that takes each recipe, represented by a list of its ingredients, a list of recipe tags, and its title, as input.

The network should use **deep metric learning** to create an embedding model that represents recipes in a way that distinguishes them well. I have considered using **a triplet network** with the triplet loss function, trained on a dataset of triplets. Each triplet would consist of an anchor (the recipe itself), a positive sample (a similar recipe), and a negative sample (a very different recipe).

The objective would be to minimize the distances between the anchor and positive samples while maximizing the distances between the anchor and negative samples. Since I'm relatively new to neural networks and natural language processing, I would greatly appreciate your thoughts and insights on this approach.

Do you think this method is suitable for my task? Are there any potential pitfalls or challenges that I should be aware of? Additionally, I would be grateful if you could provide me with a guideline on how I can successfully execute this project. Thank you.

FieldValue
text Hi everyone, I want to create an **embedding model** for my own dataset of recipes. The goal is to create a neural network that takes each recipe, represented by a list of its ingredients, a list of recipe tags, and its title, as input. The network should use **deep metric learning** to create an embedding model that represents recipes in a way that distinguishes them well. I have considered using **a triplet network** with the triplet loss function, trained on a dataset of triplets. Each tr…
label r/neuralnetworks
dataType post
communityName r/neuralnetworks
datetime 2024-04-03
username_encoded Z0FBQUFBQm5LakwxT2FEdjhCMjBJTlBuaTN1UjdBTGdhcnVIZHVNOXA1VWVtUm0yUENIRmREd3k0RE9YLXNnQlcxT2VOeENlV05EampPT3Z2ckNDMEx1T3FZQ3p5UGx1N21nRW9fMFhlZ3JHQWNKYjA1U3hBd1U9
url_encoded Z0FBQUFBQm5Lak9GeVY2WkhBaEZZbVZpNjZjNEl1a0lrUjh2UFNHWS1WclRpWHhUbW5KMWZrckV6UGRzTEo5OGlPYlNnSUZ6MUFuTklFLWh3RGdUVkNyekZqdm9idUV5M0pHZzNuVmFuM0E5eFRhWHpoRXROZEw2Q0o5dG55MXFWaHRxMjZRU2dmN0tzTzRrNEd2RG9nOUJLX3ZibEdNUjU4S3RLWXBobHlLOFQzRXV6aldtU1Z5SUg0VTFFeXdYTjA4Vk80WjVNOERXSkhJVFJRc2Z5Z3hyeDBVbUpGOGc0dz09

Raw Record

{
  "text": "Hi everyone,  I want to create an **embedding model** for my own dataset of recipes. The goal is to create a neural network that takes each recipe, represented by a list of its ingredients, a list of recipe tags, and its title, as input. \n\nThe network should use **deep metric learning** to create an embedding model that represents recipes in a way that distinguishes them well.  I have considered using **a triplet network** with the triplet loss function, trained on a dataset of triplets. Each triplet would consist of an anchor (the recipe itself), a positive sample (a similar recipe), and a negative sample (a very different recipe). \n\nThe objective would be to minimize the distances between the anchor and positive samples while maximizing the distances between the anchor and negative samples.  Since I'm relatively new to neural networks and natural language processing, I would greatly appreciate your thoughts and insights on this approach. \n\nDo you think this method is suitable for my task? Are there any potential pitfalls or challenges that I should be aware of? Additionally, I would be grateful if you could provide me with a guideline on how I can successfully execute this project.    \nThank you.  ",
  "label": "r/neuralnetworks",
  "dataType": "post",
  "communityName": "r/neuralnetworks",
  "datetime": "2024-04-03",
  "username_encoded": "Z0FBQUFBQm5LakwxT2FEdjhCMjBJTlBuaTN1UjdBTGdhcnVIZHVNOXA1VWVtUm0yUENIRmREd3k0RE9YLXNnQlcxT2VOeENlV05EampPT3Z2ckNDMEx1T3FZQ3p5UGx1N21nRW9fMFhlZ3JHQWNKYjA1U3hBd1U9",
  "url_encoded": "Z0FBQUFBQm5Lak9GeVY2WkhBaEZZbVZpNjZjNEl1a0lrUjh2UFNHWS1WclRpWHhUbW5KMWZrckV6UGRzTEo5OGlPYlNnSUZ6MUFuTklFLWh3RGdUVkNyekZqdm9idUV5M0pHZzNuVmFuM0E5eFRhWHpoRXROZEw2Q0o5dG55MXFWaHRxMjZRU2dmN0tzTzRrNEd2RG9nOUJLX3ZibEdNUjU4S3RLWXBobHlLOFQzRXV6aldtU1Z5SUg0VTFFeXdYTjA4Vk80WjVNOERXSkhJVFJRc2Z5Z3hyeDBVbUpGOGc0dz09"
}
Explore Dataset Explore Row

Entry Information