zackproser/combined_writing · Datasets at Hugging Face

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**Pooling mechanism

**: Embeddings for each token are useful, but how do we roll those up into something more meaningful? To get a single vector representation of the

entire document or query

, a

pooling mechanism

is applied to the token

level embeddings.

### Pooling Mechanism Pooling mechanisms are used to get an embedding that represents an entire document or query. How can we condense the token-level embeddings into a single vector? There are several common approaches:

#### Mean Pooling Mean pooling involves averaging the embeddings of all tokens in the sequence. This method takes the mean of each dimension across all token embeddings, resulting in a single embedding vector that represents the average contextual information of the entire input.

This approach provides a smooth and balanced representation by considering all tokens equally. For example:

`

#### [CLS] Token Embedding In models like BERT, a special [CLS] token is added at the beginning of the input sequence. The embedding of this [CLS] token, produced by the final layer of the model, is often used as a representation of the entire sequence. The [CLS] token is designed to capture the aggregated information of the entire input sequence.

This approach provides a strong, contextually rich representation due to its position and function.

`

#### Max Pooling Max pooling selects the maximum value from each dimension across all token embeddings. This method highlights the most significant features in each dimension, providing a single vector representation that emphasizes the most prominent aspects of the input.

This method captures the most salient features, and can be useful in scenarios where the most significant feature in each dimension is important.

`

In summary:

**Mean Pooling

**: Averages all token embeddings to get a balanced representation.

**[CLS] Token Embedding

**: Uses the embedding of the [CLS] token, which is designed to capture the overall context of the sequence.

**Max Pooling

**: Selects the maximum value from each dimension to emphasize the most significant features.

These pooling mechanisms transform the token-level embeddings into a single vector that represents the entire input sequence, making it suitable for downstream tasks such as similarity comparisons and document retrieval.

## Loss Functions The training objective is to learn embeddings such that queries are close to their relevant documents in the vector space and far from irrelevant documents.

Common loss functions include:

**Contrastive loss

**: Measures the distance between positive pairs and minimizes it, while maximizing the distance between negative pairs. See also Geoffrey Hinton's paper on [Contrastive Divergence](http://www.cs.toronto.edu/~hinton/absps/nccd.pdf).

**Triplet loss

**: Involves a triplet of (query, positive document, negative document) and aims to ensure that the query is closer to the positive document than to the negative document by a certain margin. This [paper on FaceNet](https://arxiv.org/abs/1503.03832) describes using triplets, and [this repository](https://github.com/davidsandberg/facenet) has code samples.

**Cosine similarity loss

**: Maximizes the cosine similarity between the embeddings of positive pairs and minimizes it for negative pairs.

## Training Procedure

The training process involves feeding pairs of queries and documents through the model, obtaining their embeddings, and then computing the loss based on the similarity or dissimilarity of these embeddings.

**Input pairs

**: Query and document pairs are fed into the model.

**Embedding generation

**: The model generates embeddings for the query and document.

**Loss computation

**: The embeddings are used to compute the loss (e.g., contrastive loss, triplet loss).

**Backpropagation

**: The loss is backpropagated to update the model weights.

## Embedding Extraction After training, the model is often truncated to use only the layers up to the point where the desired embeddings are produced.

For instance:

**Final layer embeddings

**: In many cases, the embeddings from the final layer of the model are used.

**Intermediate layer embeddings

**: Sometimes, embeddings from an intermediate layer are used if they are found to be more useful for the specific task.

## Let's consider a real example

**Pooling mechanism

**: Embeddings for each token are useful, but how do we roll those up into something more meaningful? To get a single vector representation of the

entire document or query

, a

pooling mechanism

is applied to the token

level embeddings.

### Pooling Mechanism Pooling mechanisms are used to get an embedding that represents an entire document or query. How can we condense the token-level embeddings into a single vector? There are several common approaches:

#### Mean Pooling Mean pooling involves averaging the embeddings of all tokens in the sequence. This method takes the mean of each dimension across all token embeddings, resulting in a single embedding vector that represents the average contextual information of the entire input.

This approach provides a smooth and balanced representation by considering all tokens equally. For example:

`

#### [CLS] Token Embedding In models like BERT, a special [CLS] token is added at the beginning of the input sequence. The embedding of this [CLS] token, produced by the final layer of the model, is often used as a representation of the entire sequence. The [CLS] token is designed to capture the aggregated information of the entire input sequence.

This approach provides a strong, contextually rich representation due to its position and function.

`

#### Max Pooling Max pooling selects the maximum value from each dimension across all token embeddings. This method highlights the most significant features in each dimension, providing a single vector representation that emphasizes the most prominent aspects of the input.

This method captures the most salient features, and can be useful in scenarios where the most significant feature in each dimension is important.

`

In summary:

**Mean Pooling

**: Averages all token embeddings to get a balanced representation.

**[CLS] Token Embedding

**: Uses the embedding of the [CLS] token, which is designed to capture the overall context of the sequence.

**Max Pooling

**: Selects the maximum value from each dimension to emphasize the most significant features.

These pooling mechanisms transform the token-level embeddings into a single vector that represents the entire input sequence, making it suitable for downstream tasks such as similarity comparisons and document retrieval.

## Loss Functions The training objective is to learn embeddings such that queries are close to their relevant documents in the vector space and far from irrelevant documents.

Common loss functions include:

**Contrastive loss

**: Measures the distance between positive pairs and minimizes it, while maximizing the distance between negative pairs. See also Geoffrey Hinton's paper on [Contrastive Divergence](http://www.cs.toronto.edu/~hinton/absps/nccd.pdf).

**Triplet loss

**: Involves a triplet of (query, positive document, negative document) and aims to ensure that the query is closer to the positive document than to the negative document by a certain margin. This [paper on FaceNet](https://arxiv.org/abs/1503.03832) describes using triplets, and [this repository](https://github.com/davidsandberg/facenet) has code samples.

**Cosine similarity loss

**: Maximizes the cosine similarity between the embeddings of positive pairs and minimizes it for negative pairs.

## Training Procedure

The training process involves feeding pairs of queries and documents through the model, obtaining their embeddings, and then computing the loss based on the similarity or dissimilarity of these embeddings.

**Input pairs

**: Query and document pairs are fed into the model.

**Embedding generation

**: The model generates embeddings for the query and document.

**Loss computation

**: The embeddings are used to compute the loss (e.g., contrastive loss, triplet loss).

**Backpropagation

**: The loss is backpropagated to update the model weights.

## Embedding Extraction After training, the model is often truncated to use only the layers up to the point where the desired embeddings are produced.

For instance:

**Final layer embeddings

**: In many cases, the embeddings from the final layer of the model are used.

**Intermediate layer embeddings

**: Sometimes, embeddings from an intermediate layer are used if they are found to be more useful for the specific task.

## Let's consider a real example