---
license: apache-2.0
language:
  - en
library_name: transformers
pipeline_tag: feature-extraction
base_model: nomic-ai/nomic-embed-text-v1.5
tags:
  - onnx
  - teradata
  - byom
  - embeddings
  - feature-extraction
---



> Read the disclaimer below before using this model.

----

# nomic-embed-text-v1.5 -- ONNX for Teradata BYOM

This repository hosts an **ONNX-converted** version of the upstream
model [`nomic-ai/nomic-embed-text-v1.5`](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5),
packaged for the Teradata Vantage `mldb.ONNXEmbeddings` BYOM
function. It is **not** the original PyTorch model -- only the
inference graph and tokenizer needed for in-database embedding
generation.

What's different from upstream:

- **Format**: ONNX (opset 14, IR version 8 -- BYOM 6+ compatible),
  produced from the upstream weights with architecture-aware
  post-processing baked in.
- **Precision**: dynamic int8 quantization. See the variants table
  below for what is shipped for this model.
- **Pooling and post-processing**: this graph emits the raw
  `sentence_embedding` tensor. Pooling rule is
  **mean** and the model expects
  a query-time instruction prefix (see "Instruction prefix" below).
- **Verification**: every variant's cosine fidelity vs. the
  upstream PyTorch reference is recorded on a fixed
  FLORES-200 sample. Numbers may not generalize
  to your data.

## Model details

| | |
|---|---|
| Upstream repo | [`nomic-ai/nomic-embed-text-v1.5`](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5) |
| Architecture | `NomicBertModel` (encoder) |
| Parameters | 136,731,648 |
| Output dimensions | 768 |
| Pooling | `mean` |
| Instruction prefix | yes |
| Max input tokens (native / advertised) | 2048 / 8192 |
| Languages | 1 |
| License | apache-2.0 |
| ONNX opset | 14 |
| ONNX IR version | 8 (BYOM 6+ compatible) |

<details>
<summary>Full language list (1)</summary>

- `en`

</details>

### Instruction prefix

This model was trained with two **fixed literal prefixes** that must
be prepended to the raw text before encoding. Unlike free-form
instruction-tuned models, the prefix wording is not customisable --
the model only understands these specific tokens. The ONNX graph
itself is prefix-agnostic; downstream BYOM SQL is responsible for
prepending the prefix to each input row (typically with a CTE that
concatenates the prefix string with the input text).

Use the following prefixes (snapshot at publish time -- see the
upstream model card for any updates):

- `search_query: ` -- for query-side text
- `search_document: ` -- for document / passage-side text

**Both sides of a retrieval pair must be prefixed**: prepend
`search_query: ` to user queries and `search_document: ` to the
indexed passages. Omitting the prefix degrades retrieval quality
materially. See
[`nomic-ai/nomic-embed-text-v1.5`](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5) for the
canonical guidance.

Example SQL (prepend the prefix at query time via a CTE):

```sql
WITH prefixed_queries AS (
  SELECT id,
         'search_query: ' || query_text AS text
  FROM   my_query_table
)
SELECT *
FROM   mldb.ONNXEmbeddings(
         ON prefixed_queries
         ON onnx_models    AS ModelTable DIMENSION
         ON tokenizers     AS TokenizerTable DIMENSION
         USING
           Accumulate('id')
           ModelOutputTensor('sentence_embedding')
       ) AS s;
```

## Quantization variants

This repository ships the following variants. Quality numbers are
measured against the upstream PyTorch reference on a fixed
FLORES-200 sample. The **Size** column is the
on-disk size of the ONNX weight file in megabytes (MB, 10^6 bytes).

| Variant | Size (MB) | p50 cosine | R@1 |
|---|---|---|---|
| `fp32` | 547.8 | 1.000000 | — |
| `ffn_skip` | 414.2 | 0.991608 | 0.851 |


How to read the quality columns:

- **p50 cosine** is the median cosine similarity between this
  variant's embeddings and the fp32 ONNX reference, computed over
  a fixed evaluation set. Higher means closer to the unquantized
  model; **1.0** is identical.
- **R@1** is top-1 retrieval consistency: if you use this variant
  as a search index, R@1 is the fraction of queries that get the
  same nearest neighbor as the fp32 reference would. Higher is
  better.

Notes:
- **fp32**: full-precision reference. Useful for an accuracy ceiling,
  but BYOM users almost always want one of the int8 variants for
  in-database scoring -- they are 3-4x smaller and load much faster.
- **ffn_skip**: dynamic int8 with the feed-forward (FFN) MatMul
  layers kept in **fp32**, while attention and projection MatMuls
  stay quantized. The FFN layers are where most of the quantization
  error in transformer blocks concentrates; leaving them in fp32
  recovers most of the quality loss for a modest size increase.
  The artifact is roughly **3x smaller than fp32** (larger than the
  per_channel int8 sibling).

## Quickstart: using this model with Teradata BYOM

Requires Teradata Vantage with **BYOM 6+** (`mldb.ONNXEmbeddings`).

```python
import getpass
import teradataml as tdml
from huggingface_hub import hf_hub_download

repo_id   = "Teradata/nomic-embed-text-v1.5"
model_id  = "nomic-embed-text-v1.5"        # arbitrary, used as the BYOM model_id
onnx_file = "onnx/model-ffn_skip.onnx"

# 1. Download the ONNX + tokenizer for the chosen variant.
hf_hub_download(repo_id=repo_id, filename=onnx_file,       local_dir="./")
hf_hub_download(repo_id=repo_id, filename="tokenizer.json", local_dir="./")

# 2. Connect to Vantage.
tdml.create_context(
    host=input("host: "),
    username=input("user: "),
    password=getpass.getpass("password: "),
)

# 3. Load model + tokenizer into BYOM tables (one-time per model_id).
tdml.save_byom(model_id=model_id, model_file=onnx_file,
               table_name="embeddings_models")
tdml.save_byom(model_id=model_id, model_file="tokenizer.json",
               table_name="embeddings_tokenizers")
```

Then call `mldb.ONNXEmbeddings` against an input table whose
`txt` column carries the strings to embed:

```sql
SELECT *
FROM mldb.ONNXEmbeddings(
    ON (SELECT id, txt FROM your_input_table) AS InputTable
    ON (SELECT model_id, model FROM embeddings_models
         WHERE model_id = 'nomic-embed-text-v1.5') AS ModelTable DIMENSION
    ON (SELECT model_id, tokenizer FROM embeddings_tokenizers
         WHERE model_id = 'nomic-embed-text-v1.5') AS TokenizerTable DIMENSION
    USING
        Accumulate('id')
        ModelOutputTensor('sentence_embedding')
        OutputFormat('FLOAT32(768)')
        OverwriteCachedModel('*')
) AS t
ORDER BY id;
```

Pooling rule **`mean`** is applied **inside** the converted
ONNX graph -- the output tensor named above already contains the
pooled, post-processed embedding vector. For instruction-prefix models, prepend
the recommended instruction text to each input `txt` before calling
`ONNXEmbeddings`; the prefix is plain text that the tokenizer handles
unchanged.

## Original model attribution

The original weights and training methodology belong to
**Nomic AI**. Please cite their work, not this
repository, in academic contexts. The canonical upstream model card
is at
[`nomic-ai/nomic-embed-text-v1.5`](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5);
refer to it for benchmarks, training details, intended use, and
citation information.

## Reporting issues

For ONNX-conversion or BYOM-compatibility issues specific to this
Teradata-converted artifact, please open a **Discussion** on this
model's Hugging Face page. Questions about the underlying model
quality, training, or intended use should go to the upstream
maintainer's model card.

----

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