Creating Custom Models

Structure of Sentence Transformer Models

A Sentence Transformer model consists of a collection of modules (docs) that are executed sequentially. The most common architecture is a combination of a Transformer module, a Pooling module, and optionally, a Dense module and/or a Normalize module.

  • Transformer: This module is responsible for processing the input text and generating contextualized embeddings.

  • Pooling: This module reduces the dimensionality of the output from the Transformer module by aggregating the embeddings. Common pooling strategies include mean pooling and CLS pooling.

  • Dense: This module contains a linear layer that post-processes the embedding output from the Pooling module.

  • Normalize: This module normalizes the embedding from the previous layer.

For example, the popular all-MiniLM-L6-v2 model can also be loaded by initializing the 3 specific modules that make up that model:

from sentence_transformers import models, SentenceTransformer

transformer = models.Transformer("sentence-transformers/all-MiniLM-L6-v2", max_seq_length=256)
pooling = models.Pooling(transformer.get_word_embedding_dimension(), pooling_mode="mean")
normalize = models.Normalize()

model = SentenceTransformer(modules=[transformer, pooling, normalize])

Saving Sentence Transformer Models

Whenever a Sentence Transformer model is saved, three types of files are generated:

  • modules.json: This file contains a list of module names, paths, and types that are used to reconstruct the model.

  • config_sentence_transformers.json: This file contains some configuration options of the Sentence Transformer model, including saved prompts, the model its similarity function, and the Sentence Transformer package version used by the model author.

  • Module-specific files: Each module is saved in a separate folder, with the first module saved in the root folder and all subsequent modules saved in subfolders named after the module index and the model name (e.g., 1_Pooling, 2_Normalize). Most module folders contain a config.json (or sentence_bert_config.json for the Transformer module) file that stores default values for keyword arguments passed to that Module. So, a sentence_bert_config.json of:

    {
  "max_seq_length": 4096,
  "do_lower_case": false
}

    means that the Transformer module will be initialized with max_seq_length=4096 and do_lower_case=False.

As a result, if I call SentenceTransformer.save_pretrained("local-all-MiniLM-L6-v2") on the model from the previous snippet, the following files are generated:

local-all-MiniLM-L6-v2/
├── 1_Pooling
│   └── config.json
├── 2_Normalize
├── README.md
├── config.json
├── config_sentence_transformers.json
├── model.safetensors
├── modules.json
├── sentence_bert_config.json
├── special_tokens_map.json
├── tokenizer.json
├── tokenizer_config.json
└── vocab.txt

This contains a modules.json with these contents:

[
  {
    "idx": 0,
    "name": "0",
    "path": "",
    "type": "sentence_transformers.models.Transformer"
  },
  {
    "idx": 1,
    "name": "1",
    "path": "1_Pooling",
    "type": "sentence_transformers.models.Pooling"
  },
  {
    "idx": 2,
    "name": "2",
    "path": "2_Normalize",
    "type": "sentence_transformers.models.Normalize"
  }
]

And a config_sentence_transformers.json with these contents:

{
  "__version__": {
    "sentence_transformers": "3.0.1",
    "transformers": "4.43.4",
    "pytorch": "2.5.0"
  },
  "prompts": {},
  "default_prompt_name": null,
  "similarity_fn_name": null
}

Additionally, the 1_Pooling directory contains the configuration file for the Pooling module, while the 2_Normalize directory is empty because the Normalize module does not require any configuration. The sentence_bert_config.json file contains the configuration of the Transformer module, and this module also saved a lot of files related to the tokenizer and the model itself in the root directory.

Loading Sentence Transformer Models

To load a Sentence Transformer model from a saved model directory, the modules.json is read to determine the modules that make up the model. Each module is initialized with the configuration stored in the corresponding module directory, after which the SentenceTransformer class is instantiated with the loaded modules.

Sentence Transformer Model from a Transformers Model

When you initialize a Sentence Transformer model with a pure Transformers model (e.g., BERT, RoBERTa, DistilBERT, T5), Sentence Transformers creates a Transformer module and a Mean Pooling module by default. This provides a simple way to leverage pre-trained language models for sentence embeddings.

To be specific, these two snippets are identical:

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("bert-base-uncased")

from sentence_transformers import models, SentenceTransformer

transformer = models.Transformer("bert-base-uncased")
pooling = models.Pooling(transformer.get_word_embedding_dimension(), pooling_mode="mean")
model = SentenceTransformer(modules=[transformer, pooling])

Advanced: Custom Modules

To create custom Sentence Transformer models, you can implement your own modules by subclassing PyTorch’s torch.nn.Module class and implementing these methods:

  • A torch.nn.Module.forward() method that accepts a features dictionary with keys like input_ids, attention_mask, token_type_ids, token_embeddings, and sentence_embedding, depending on where the module is in the model pipeline.

  • A save method that accepts a save_dir argument and saves the module’s configuration to that directory.

  • A load static method that accepts a load_dir argument and initializes the Module given the module’s configuration from that directory.

  • (If 1st module) A get_max_seq_length method that returns the maximum sequence length the module can process. Only required if the module processes input text.

  • (If 1st module) A tokenize method that accepts a list of inputs and returns a dictionary with keys like input_ids, attention_mask, token_type_ids, pixel_values, etc. This dictionary will be passed along to the module’s forward method.

  • (Optional) A get_sentence_embedding_dimension method that returns the dimensionality of the sentence embeddings produced by the module. Only required if the module generated the embeddings or updates the embeddings’ dimensionality.

  • (Optional) A get_config_dict method that returns a dictionary with the module’s configuration. This method can be used to save the module’s configuration to disk and to save the module config in a model card.

For example, we can create a custom pooling method by implementing a custom Module.

# decay_pooling.py

import json
import os
import torch
import torch.nn as nn

class DecayMeanPooling(nn.Module):
    def __init__(self, dimension: int, decay: float = 0.95) -> None:
        super(DecayMeanPooling, self).__init__()
        self.dimension = dimension
        self.decay = decay

    def forward(self, features: dict[str, torch.Tensor], **kwargs) -> dict   [str, torch.Tensor]:
        token_embeddings = features["token_embeddings"]
        attention_mask = features["attention_mask"].unsqueeze(-1)

        # Apply the attention mask to filter away padding tokens
        token_embeddings = token_embeddings * attention_mask
        # Calculate mean of token embeddings
        sentence_embeddings = token_embeddings.sum(1) / attention_mask.sum(1)
        # Apply exponential decay
        importance_per_dim = self.decay ** torch.arange(sentence_embeddings.   size(1), device=sentence_embeddings.device)
        features["sentence_embedding"] = sentence_embeddings *    importance_per_dim
        return features

    def get_config_dict(self) -> dict[str, float]:
        return {"dimension": self.dimension, "decay": self.decay}

    def get_sentence_embedding_dimension(self) -> int:
        return self.dimension

    def save(self, save_dir: str, **kwargs) -> None:
        with open(os.path.join(save_dir, "config.json"), "w") as fOut:
            json.dump(self.get_config_dict(), fOut, indent=4)

    def load(load_dir: str, **kwargs) -> "DecayMeanPooling":
        with open(os.path.join(load_dir, "config.json")) as fIn:
            config = json.load(fIn)

        return DecayMeanPooling(**config)

Note

Adding **kwargs to the __init__, forward, save, load, and tokenize methods is recommended to ensure that the methods are compatible with future updates to the Sentence Transformers library.

This can now be used as a module in a Sentence Transformer model:

from sentence_transformers import models, SentenceTransformer
from decay_pooling import DecayMeanPooling

transformer = models.Transformer("bert-base-uncased", max_seq_length=256)
decay_mean_pooling = DecayMeanPooling(transformer.get_word_embedding_dimension(), decay=0.99)
normalize = models.Normalize()

model = SentenceTransformer(modules=[transformer, decay_mean_pooling, normalize])
print(model)
"""
SentenceTransformer(
    (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel
    (1): DecayMeanPooling()
    (2): Normalize()
)
"""

texts = [
    "Hello, World!",
    "The quick brown fox jumps over the lazy dog.",
    "I am a sentence that is used for testing purposes.",
    "This is a test sentence.",
    "This is another test sentence.",
]
embeddings = model.encode(texts)
print(embeddings.shape)
# [5, 384]

You can save this model with SentenceTransformer.save_pretrained, resulting in a modules.json of:

[
  {
    "idx": 0,
    "name": "0",
    "path": "",
    "type": "sentence_transformers.models.Transformer"
  },
  {
    "idx": 1,
    "name": "1",
    "path": "1_DecayMeanPooling",
    "type": "decay_pooling.DecayMeanPooling"
  },
  {
    "idx": 2,
    "name": "2",
    "path": "2_Normalize",
    "type": "sentence_transformers.models.Normalize"
  }
]

To ensure that decay_pooling.DecayMeanPooling can be imported, you should copy over the decay_pooling.py file to the directory where you saved the model. If you push the model to the Hugging Face Hub, then you should also upload the decay_pooling.py file to the model’s repository. Then, everyone can use your custom module by calling SentenceTransformer("your-username/your-model-id", trust_remote_code=True).

Note

Using a custom module with remote code stored on the Hugging Face Hub requires that your users specify trust_remote_code as True when loading the model. This is a security measure to prevent remote code execution attacks.

If you have your models and custom modelling code on the Hugging Face Hub, then it might make sense to separate your custom modules into a separate repository. This way, you only have to maintain one implementation of your custom module, and you can reuse it across multiple models. You can do this by updating the type in modules.json file to include the path to the repository where the custom module is stored like {repository_id}--{dot_path_to_module}. For example, if the decay_pooling.py file is stored in a repository called my-user/my-model-implementation and the module is called DecayMeanPooling, then the modules.json file may look like this:

[
  {
    "idx": 0,
    "name": "0",
    "path": "",
    "type": "sentence_transformers.models.Transformer"
  },
  {
    "idx": 1,
    "name": "1",
    "path": "1_DecayMeanPooling",
    "type": "my-user/my-model-implementation--decay_pooling.DecayMeanPooling"
  },
  {
    "idx": 2,
    "name": "2",
    "path": "2_Normalize",
    "type": "sentence_transformers.models.Normalize"
  }
]

Advanced: Keyword argument passthrough in Custom Modules

If you want your users to be able to specify custom keyword arguments via the SentenceTransformer.encode method, then you can add their names to the modules.json file. For example, if my module should behave differently if your users specify a task_type keyword argument, then your modules.json might look like:

[
  {
    "idx": 0,
    "name": "0",
    "path": "",
    "type": "custom_transformer.CustomTransformer",
    "kwargs": ["task_type"]
  },
  {
    "idx": 1,
    "name": "1",
    "path": "1_Pooling",
    "type": "sentence_transformers.models.Pooling"
  },
  {
    "idx": 2,
    "name": "2",
    "path": "2_Normalize",
    "type": "sentence_transformers.models.Normalize"
  }
]

Then, you can access the task_type keyword argument in the forward method of your custom module:

from sentence_transformers.models import Transformer

class CustomTransformer(Transformer):
    def forward(self, features: dict[str, torch.Tensor], task_type: Optional[str] = None) -> dict[str, torch.Tensor]:
        if task_type == "default":
            # Do something
        else:
            # Do something else
        return features

This way, users can specify the task_type keyword argument when calling SentenceTransformer.encode:

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("your-username/your-model-id", trust_remote_code=True)
texts = [...]
model.encode(texts, task_type="default")