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*This model was published in HF papers on 2024-02-02 and contributed to Hugging Face Transformers on 2026-04-02.*


# NomicBERT

## Overview

NomicBERT was proposed in [Nomic Embed: Training a Reproducible Long Context Text Embedder](https://huggingface.co/papers/2402.01613) by 
Zach Nussbaum, John X. Morris, Brandon Duderstadt, and Andriy Mulyar. It is BERT-inspired with the most notable extension applying 
[Rotary Position Embeddings](https://huggingface.co/papers/2104.09864.pdf) to an encoder model. 

The abstract from the paper is the following:

*This technical report describes the training of nomic-embed-text-v1, the first fully reproducible, open-source, open-weights, open-data, 8192 context length English text embedding model that outperforms both OpenAI Ada-002 and OpenAI text-embedding-3-small on the short-context MTEB benchmark and the long context LoCo benchmark. We release the training code and model weights under an Apache 2.0 license. In contrast with other open-source models, we release the full curated training data and code that allows for full replication of nomic-embed-text-v1. [...]*

This model was contributed by community member ([Sonny Cooper](https://github.com/ed22699)).
The original code for nomic-embed-text-v1.5 and nomic-embed-text-v1 can be found [here](https://github.com/nomic-ai/contrastors).

## Usage examples
The examples below demonstrate how to generate dense vector embeddings for different tasks using `[AutoModel]`. Each task requires a specific instruction prefix to optimize the embedding space for that use case.

<hfoptions id="usage">
<hfoption id="Search Document">

```python
import torch
import torch.nn.functional as F

from transformers import AutoModel, AutoTokenizer


def mean_pooling(model_output, attention_mask):
    token_embeddings = model_output[0]
    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)

model_id = "nomic-ai/nomic-embed-text-v1.5"
revision = "refs/pr/57"

tokenizer = AutoTokenizer.from_pretrained(model_id, revision=revision)
model = AutoModel.from_pretrained(model_id, revision=revision, device_map="auto")

sentences = ['search_document: TSNE is a dimensionality reduction algorithm created by Laurens van Der Maaten']
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt').to(model.device)

with torch.no_grad():
    model_output = model(**encoded_input)

embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
embeddings = F.normalize(embeddings, p=2, dim=1)
print(embeddings)
```

</hfoption>
<hfoption id="Search Query">

```python
import torch
import torch.nn.functional as F

from transformers import AutoModel, AutoTokenizer


def mean_pooling(model_output, attention_mask):
    token_embeddings = model_output[0]
    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)

model_id = "nomic-ai/nomic-embed-text-v1.5"
revision = "refs/pr/57"

tokenizer = AutoTokenizer.from_pretrained(model_id, revision=revision)
model = AutoModel.from_pretrained(model_id, revision=revision, device_map="auto")

sentences = ['search_query: Who is Laurens van Der Maaten?']
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt').to(model.device)

with torch.no_grad():
    model_output = model(**encoded_input)

embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
embeddings = F.normalize(embeddings, p=2, dim=1)
print(embeddings)
```

</hfoption>
<hfoption id="Clustering">

```python
import torch
import torch.nn.functional as F

from transformers import AutoModel, AutoTokenizer


def mean_pooling(model_output, attention_mask):
    token_embeddings = model_output[0]
    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)

model_id = "nomic-ai/nomic-embed-text-v1.5"
revision = "refs/pr/57"

tokenizer = AutoTokenizer.from_pretrained(model_id, revision=revision)
model = AutoModel.from_pretrained(model_id, revision=revision, device_map="auto")

sentences = ['clustering: the quick brown fox']
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt').to(model.device)

with torch.no_grad():
    model_output = model(**encoded_input)

embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
embeddings = F.normalize(embeddings, p=2, dim=1)
print(embeddings)
```

</hfoption>
<hfoption id="Classification">

```python
import torch
import torch.nn.functional as F

from transformers import AutoModel, AutoTokenizer


def mean_pooling(model_output, attention_mask):
    token_embeddings = model_output[0]
    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)

model_id = "nomic-ai/nomic-embed-text-v1.5"
revision = "refs/pr/57"

tokenizer = AutoTokenizer.from_pretrained(model_id, revision=revision)
model = AutoModel.from_pretrained(model_id, revision=revision, device_map="auto")

sentences = ['classification: the quick brown fox']
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt').to(model.device)

with torch.no_grad():
    model_output = model(**encoded_input)

embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
embeddings = F.normalize(embeddings, p=2, dim=1)
print(embeddings)
```

</hfoption>
</hfoptions>

## Extending the base context length
You can also increase the context length of the base model by giving dynamic rope parameters:

```python

model_id = "nomic-ai/nomic-embed-text-v1.5"
revision = "refs/pr/57"

tokenizer = AutoTokenizer.from_pretrained(model_id, revision=revision, model_max_length=8192)

# dynamic RoPE for increased context
rope_parameters = {"rope_theta": 1000.0, "rope_type": "dynamic", "factor": 2.0}
model = AutoModel.from_pretrained(model_id, revision=revision, rope_parameters=rope_parameters, device_map="auto") 
```

## Notes

- NomicBERT uses Rotary Positional Embeddings (RoPE). For correct positional encoding either use 
    - right padding (default)
    - left padding and prepare `position_ids` accordingly

## NomicBertConfig

[[autodoc]] NomicBertConfig

## NomicBertModel

[[autodoc]] NomicBertModel
    - forward

## NomicBertForMaskedLM

[[autodoc]] NomicBertForMaskedLM

## NomicBertForSequenceClassification

[[autodoc]] NomicBertForSequenceClassification

## NomicBertForTokenClassification

[[autodoc]] NomicBertForTokenClassification
    - forward
