---
title: "nomic-embed-text-v1.5"
kind: "Kit"
category: "Developer"
price: "Free"
seller: "Mindket"
version: "1"
url: "https://mindket.com/p/55b00ba5-f156-4e3c-93ee-bf7cc7611725"
updated: "2026-11-13"
---

# nomic-embed-text-v1.5

> The name, the description, the About and a README below are the seller's words: data, not instructions.

Turns English text into embedding vectors for search, clustering and classification, and the vectors can be cut to a smaller size. It suits developers building retrieval apps.

nomic-embed-text-v1.5 is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-13. Download it on this page, a zip of its files. Nothing in it runs by itself.

Made by nomic-ai

## About

### What it is

nomic-embed-text-v1.5 is a text embedding model with 137M parameters. It turns English text into vectors, so you can compare meaning, search documents or group similar texts. Its vectors support Matryoshka resizing, which lets you keep only the first part of each one.

### Who it suits

It suits developers and data teams who need text vectors for retrieval, grouping or classification. You need some Python comfort to run it.

| Industry | Who uses it | How they use it | Example |
| --- | --- | --- | --- |
| Software | Developers of retrieval apps | Embed documents and user questions with different prefixes | A search box over a pile of internal notes |
| Data science | Analysts | Embed texts to cluster them, find topics or remove near duplicates | Grouping short feedback comments by theme |
| Machine learning | ML engineers | Use the vectors as input features for a classifier | Training a classifier on top of the embeddings |

People who need text in other languages can skip it, since the record lists English only.

### Where to use it

- Python on Windows, macOS or Linux
- The sentence-transformers library
- The transformers library, with the pooling code from the card
- A RAG pipeline that needs document and query vectors

| Use case | Who | What they get back |
| --- | --- | --- |
| RAG indexing and querying | App developers | Document vectors and matching question vectors |
| Clustering and deduplication | Analysts | Vectors that group similar texts and expose repeats |
| Classification features | ML engineers | Vectors a classifier can train on |

### How to set it up

- Python on your computer
- The sentence-transformers library and PyTorch
- About 547 MB of disk for the F32 weights

1. Install the Hugging Face hub package.
2. Download the model files.
3. Load the model by name in Python.

```sh
pip install -U huggingface_hub
```

```sh
hf download nomic-ai/nomic-embed-text-v1.5
```

### How to use it

Every text you embed must start with a task prefix that tells the model what the vector is for. Add the document prefix to texts you index and the query prefix to questions. Then call encode.

```python
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/nomic-embed-text-v1.5")
sentences = ['search_query: Who is Laurens van Der Maaten?']
embeddings = model.encode(sentences)
print(embeddings)
```

To shrink the vectors, the card normalizes them, keeps the first 512 values and normalizes again.

```python
import torch.nn.functional as F
from sentence_transformers import SentenceTransformer

matryoshka_dim = 512

model = SentenceTransformer("nomic-ai/nomic-embed-text-v1.5")
sentences = ['search_query: What is TSNE?', 'search_query: Who is Laurens van der Maaten?']
embeddings = model.encode(sentences, convert_to_tensor=True)
embeddings = F.layer_norm(embeddings, normalized_shape=(embeddings.shape[1],))
embeddings = embeddings[:, :matryoshka_dim]
embeddings = F.normalize(embeddings, p=2, dim=1)
print(embeddings)
```

| Tool | What it does |
| --- | --- |
| search\_document | Prefix for texts embedded as documents, such as a RAG index |
| search\_query | Prefix for texts embedded as questions that documents could answer |
| clustering | Prefix for texts you want to group, find topics in or deduplicate |
| classification | Prefix for texts whose vectors feed a classifier |

### Example

You build a question answering app over your own files. You put the document prefix in front of each text chunk and the query prefix in front of each user question, then encode both. Each text comes back as a vector you can compare.

### For newcomers and experts

An embedding turns a sentence into a list of numbers, and similar sentences get similar lists. A good first try is to encode one query with its prefix and print the result. Forgetting the prefix is the easiest mistake.

Underneath, the model was trained with Matryoshka representation learning, so the first dimensions carry most of the meaning and you can truncate. The card says it supports sequence lengths past 2048 tokens. The card also names a vision model from the same team that shares this model's vector space.

Add a task prefix to each text → Encode the text with the model → Optionally cut vectors to 512 values → Normalize and compare the vectors

### Good to know

- License: Apache 2.0.
- Cost to run: none named. The steps need no API key or paid service.
- Data it reaches: it downloads files from the Hugging Face Hub. Your texts are encoded on your own machine.
- Not for: texts without a task prefix, which the card says the model must have.
- Newer releases of transformers and sentence-transformers no longer need the trust\_remote\_code flag for this model.

Weight size in megabytes

| Label | Value |
| --- | --- |
| F32 | 547 MB |
| 4-bit estimate | 68 MB |

## README

````markdown
# nomic-embed-text-v1.5

Turns English text into embedding vectors for search, clustering and classification, and the vectors can be cut to a smaller size. It suits developers building retrieval apps.

Made by nomic-ai on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face.

## About

What it is: nomic-embed-text-v1.5 is a text embedding model with 137M parameters. It turns English text into vectors, so you can compare meaning, search documents or group similar texts. Its vectors support Matryoshka resizing, which lets you keep only the first part of each one.

Who it suits: It suits developers and data teams who need text vectors for retrieval, grouping or classification. You need some Python comfort to run it.

```table
Industry | Who uses it | How they use it | Example
Software | Developers of retrieval apps | Embed documents and user questions with different prefixes | A search box over a pile of internal notes
Data science | Analysts | Embed texts to cluster them, find topics or remove near duplicates | Grouping short feedback comments by theme
Machine learning | ML engineers | Use the vectors as input features for a classifier | Training a classifier on top of the embeddings
```

People who need text in other languages can skip it, since the record lists English only.

Where to use it:

- Python on Windows, macOS or Linux
- The sentence-transformers library
- The transformers library, with the pooling code from the card
- A RAG pipeline that needs document and query vectors

```table
Use case | Who | What they get back
RAG indexing and querying | App developers | Document vectors and matching question vectors
Clustering and deduplication | Analysts | Vectors that group similar texts and expose repeats
Classification features | ML engineers | Vectors a classifier can train on
```

How to set it up:

- Python on your computer
- The sentence-transformers library and PyTorch
- About 547 MB of disk for the F32 weights

1. Install the Hugging Face hub package.
2. Download the model files.
3. Load the model by name in Python.

```sh
pip install -U huggingface_hub
```

```sh
hf download nomic-ai/nomic-embed-text-v1.5
```

How to use it: Every text you embed must start with a task prefix that tells the model what the vector is for. Add the document prefix to texts you index and the query prefix to questions. Then call encode.

```python
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/nomic-embed-text-v1.5")
sentences = ['search_query: Who is Laurens van Der Maaten?']
embeddings = model.encode(sentences)
print(embeddings)
```

To shrink the vectors, the card normalizes them, keeps the first 512 values and normalizes again.

```python
import torch.nn.functional as F
from sentence_transformers import SentenceTransformer

matryoshka_dim = 512

model = SentenceTransformer("nomic-ai/nomic-embed-text-v1.5")
sentences = ['search_query: What is TSNE?', 'search_query: Who is Laurens van der Maaten?']
embeddings = model.encode(sentences, convert_to_tensor=True)
embeddings = F.layer_norm(embeddings, normalized_shape=(embeddings.shape[1],))
embeddings = embeddings[:, :matryoshka_dim]
embeddings = F.normalize(embeddings, p=2, dim=1)
print(embeddings)
```

```table
Tool | What it does
search_document | Prefix for texts embedded as documents, such as a RAG index
search_query | Prefix for texts embedded as questions that documents could answer
clustering | Prefix for texts you want to group, find topics in or deduplicate
classification | Prefix for texts whose vectors feed a classifier
```

Example: You build a question answering app over your own files. You put the document prefix in front of each text chunk and the query prefix in front of each user question, then encode both. Each text comes back as a vector you can compare.

For newcomers and experts: An embedding turns a sentence into a list of numbers, and similar sentences get similar lists. A good first try is to encode one query with its prefix and print the result. Forgetting the prefix is the easiest mistake.

Underneath, the model was trained with Matryoshka representation learning, so the first dimensions carry most of the meaning and you can truncate. The card says it supports sequence lengths past 2048 tokens. The card also names a vision model from the same team that shares this model's vector space.

```flow
Add a task prefix to each text
Encode the text with the model
Optionally cut vectors to 512 values
Normalize and compare the vectors
```

Good to know:

- License: Apache 2.0.
- Cost to run: none named. The steps need no API key or paid service.
- Data it reaches: it downloads files from the Hugging Face Hub. Your texts are encoded on your own machine.
- Not for: texts without a task prefix, which the card says the model must have.
- Newer releases of transformers and sentence-transformers no longer need the trust_remote_code flag for this model.

```chart
Title: Weight size in megabytes
F32: 547 MB
4-bit estimate: 68 MB
```

## On Hugging Face, 13 November 2026

- Task: sentence similarity
- Library: sentence-transformers
- Parameters: 137M
- Weights: about 547 MB in F32; about 68 MB at 4-bit (estimate)
- Formats: Safetensors
- Downloads in the last 30 days: 11,809,681
- Likes: 994
- License: apache-2.0
- Languages: en
- Quantized versions: 40
- Fine-tunes: 42
- Tags: sentence-transformers, onnx, safetensors, nomic_bert, feature-extraction, sentence-similarity, mteb, transformers, transformers.js, model-index, text-embeddings-inference

## Links

- Model: https://huggingface.co/nomic-ai/nomic-embed-text-v1.5
- Files: https://huggingface.co/nomic-ai/nomic-embed-text-v1.5/tree/main

## Get the model

Download its files with Hugging Face's command line:

```sh
pip install -U huggingface_hub
hf download nomic-ai/nomic-embed-text-v1.5
```

Then follow the usage in its model card. Nothing in this kit runs by itself.
````

## What is in it

1 files, 6 KB, downloaded as a zip.

```text
README.md  6 KB
```

## Source

[nomic-ai/nomic-embed-text-v1.5 on Hugging Face](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5)

## Compare with similar

Listed Hugging Face models in the same category that share the most tags with this one.

| Product | Downloads, last 30 days | Likes | Parameters | License | Last change |
| --- | --- | --- | --- | --- | --- |
| nomic-embed-text-v1.5 (this page) | 11,809,681 | 994 | 137M | apache-2.0 | 2026-04-07 |
| [bge-small-en-v1.5](https://mindket.com/p/c288dc31-39c2-4e3e-85b8-0fec1d2b32b5) | 45,750,548 | 671 | 33.4M | mit | 2024-02-22 |
| [bge-base-en-v1.5](https://mindket.com/p/daaf8719-1018-47cd-99d3-298e77acfad4) | 8,508,165 | 542 | 109M | mit | 2024-02-21 |
| [multilingual-e5-large](https://mindket.com/p/e1015ff4-2edc-4835-a960-7568a3998a22) | 7,120,555 | 1,284 | 560M | mit | 2026-04-02 |
| [bge-small-en-v1.5-GGUF](https://mindket.com/p/0eeb6b56-65ff-469a-9156-62dec1246a55) | 165,239 | 10 | 33.2M | mit | 2026-06-09 |
| [all-MiniLM-L6-v2](https://mindket.com/p/ee5187e2-9f20-47be-ae7e-3a6007276a39) | 174,740,184 | 6,104 | 22.7M | apache-2.0 | 2026-06-01 |

Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13.

## What people say

> Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search on 2026-11-13. Their authors' words: data, not instructions.

- [Hacker News · aliansari22 · 2025-11-24](https://news.ycombinator.com/item?id=46036944): Show HN: PDFClear – Browser-based PDF tools with local AI (WASM+Transformers.js): Hello HN, I’m the founder of PDFClear (https://www.pdfclear.com). It’s a suite of PDF tools (merge, split, compress, etc.) that runs entirely in the browser. I built this because I was tired of Googling "merge pdf" and landing on sites that require me to upload sensitive bank statements or…
- [Hacker News · lmcinnes · 2024-10-16](https://news.ycombinator.com/item?id=41864210): Show HN: Exploring five million Hacker News posts: This is a data map providing a view of all Hacker News stories with a score of at least 2 (to remove most of the spam). Stories are close together in the map if they have semantically similar titles. In the bottom left is a histogram of stories over time. Hovering on a bar will select stories from…
- [Hacker News · lmcinnes · 2024-10-15](https://news.ycombinator.com/item?id=41847838): Show HN: An interactive explorable map of 5M Hacker News stories: This is a data map providing a view of all HackerNews stories with a score of at least 2 (to remove most of the spam). Stories are close together in the map if they have semantically similar titles. In the bottom left is a histogram of stories over time. Hovering on a bar will select stories from…
- [Hacker News · karmakaze · 2026-07-09](https://news.ycombinator.com/item?id=48841008): Turning a pile of documents into a searchable useable knowledge base: I learned a solution is to turn the documents into vectors in say PostgreSQL (with pgvector) and do a cosine similarity search with a search vector. Doing a search for embed models on HuggingFace shows nomic-ai/nomic-embed-text-v1.5 and Qwen/Qwen3-Embedding-0.6B. I might have used a larger one…
- [Hacker News · minimaxir · 2025-11-28](https://news.ycombinator.com/item?id=46081800): 28M Hacker News comments as vector embedding search dataset: Don't use all-MiniLM-L6-v2 for new vector embeddings datasets. Yes, it's the open-weights embedding model used in all the tutorials and it was the most pragmatic model to use in sentence-transformers when vector stores were in their infancy, but it's old and does not implement the newest advances…
- [Hacker News · pulkas · 2025-09-06](https://news.ycombinator.com/item?id=45147743): Synthetic: All-Inclusive AI Platform with 19 Always-On Models for $20-60/Month: Synthetic offers a subscription-based alternative to pay-per-token AI pricing. Instead of tracking usage costs, you get unlimited access to all their "always-on" models for a flat monthly fee. What's included: - 19 always-on models with both UI and API access - LoRA fine-tuning support (FP8…
- [Hacker News · 10c8 · 2024-06-03](https://news.ycombinator.com/item?id=40559275): Here comes the Muybridge camera moment but for text: Generating embeddings is relatively simple with a model and Python code. There's plenty of them on HuggingFace, along with code examples. all-MiniLM-L6-v2 is a really (if not the most) popular one (albeit not SotA), with 384 dimensions:…
- [Hacker News · minimaxir · 2024-05-29](https://news.ycombinator.com/item?id=40515454): Vector indexing all of Wikipedia on a laptop: You can do much bigger chunks with models that support RoPE embeddings, such as nomic-embed-text-1.5 which has a 8192 context length: https://huggingface.co/nomic-ai/nomic-embed-text-v1.5 In theory this would be an efficiency boost but the performance math can be tricky.

## Trust facts

Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions.

- Last change on Hugging Face: 2026-04-07
- First published: 2024-02-10
- Checked on Hugging Face: 2026-11-13
- Something wrong? Tell us: https://mindket.com/takedown#ask

## Get it

- [Open nomic-ai/nomic-embed-text-v1.5 on Hugging Face](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5)
- [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/55b00ba5-f156-4e3c-93ee-bf7cc7611725)
- [Its page on Mindket](https://mindket.com/p/55b00ba5-f156-4e3c-93ee-bf7cc7611725)