---
license: apache-2.0
base_model:
- lightonai/GTE-ModernColBERT-v1
pipeline_tag: sentence-similarity
tags:
- SMVE
- ColBERT
- PyLate
- sentence-transformers
- sentence-similarity
- feature-extraction
datasets:
- lightonai/ms-marco-en-bge-gemma
language:
- en
---

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<p align="center">
<sup>Looking for production ready multi-vector search? Check out <a href="https://topk.io">TopK</a>, hybrid retrieval engine build on object storage.</sup>
</p>

# Iso-ModernColBERT
This model is an isotropically corrected version of [GTE-ModernColBERT-v1](https://huggingface.co/lightonai/GTE-ModernColBERT-v1).
It's built for production use cases where retrieval speed and quality matter. Compared to the original model, this version delivers
up to 3x faster inference in `bf16` with almost no loss in accuracy and enables scalable multi-vector retrieval through
[Sparse Multi-Vector Encoding (SMVE)](https://www.topk.io/blog/20260311-smve-multi-vector-retrieval) inside [TopK](https://topk.io).

## Usage
Install PyLate for embeddings and TopK SDK for retrieval.
```
pip install -U pylate topk-sdk
```

### Embed documents
First, load the model into PyLate `ColBERT` class and encode your documents.

```python
import torch
import numpy as np
from pylate import models


model = models.ColBERT(
  model_name_or_path="topk-io/Iso-ModernColBERT",
  model_kwargs={'torch_dtype': torch.bfloat16},
)

documents = [
  "document 1 text",
  "document 2 text",
  "document 3 text",
]

doc_embeddings = model.encode(
    documents,
    batch_size=32,
    # Ensure that it is set to False to indicate that these are documents, not queries
    is_query=False,
    show_progress_bar=True,
)
```

### Store document embeddings
Index multi-vector document embeddings inside [TopK](https://topk.io), hybrid retrieval engine built on object storage.
To get started, [create an API key](https://console.topk.io).
```python
from topk_sdk import Client
from topk_sdk.schema import matrix, multi_vector_index

# Initialize TopK client
client = Client(
    api_key = "<TOPK_API_KEY>",
    region = "aws-us-east-1-elastica",
)

# Create a collection with multi-vector index
client.collections().create(
    "iso-moderncolbert",
    schema = {
        "token_embeddings": matrix(dimension=128, value_type="f16")
            .index(multi_vector_index(metric="maxsim"))
    }
)

# Upsert document embeddings
client.collection("iso-moderncolbert").upsert([
  {
    "_id": str(i),
    "token_embeddings": emb.astype(np.float16),
    "text": text
  }
  for (i, (text, emb)) in enumerate(zip(documents, doc_embeddings))
])
```

### Retrieve documents for queries
Your documents are now durably persisted in the index and queryable.
```python
from topk_sdk.query import fn, select, field

# Encode query string
query_embedding = model.encode(
    "query for document 3",
    # Ensure that it is set to True for queries
    is_query=True,
    show_progress_bar=False,
)

# Retrieve top-k documents using the query embedding
results = client.collection("iso-moderncolbert").query(
    select(
        "_id", "text",
        # Compute maxsim between query and indexed documents
        maxsim_score = fn.multi_vector_distance(
            "token_embeddings",
            query_embedding.astype(np.float16)
        )
    )
    # Get the top 10 matching documents
    .topk(field("maxsim_score"), 10)
)

for r in results:
    print(f"id: {r['_id']}, score: {r['maxsim_score']}, text: {r['text']}")
```
TopK's query language is flexible and allows you to tune retrieval parameters, combine multi-vector with metadata filters,
keyword search, and more. Check out our [docs](https://docs.topk.io) to learn more.

# Evaluation results

We conducted evaluation of our model using an internal evaluation harness on two standard benchmarks - BEIR and NanoBEIR.
For baselines, we selected [GTE-ModernColBERT-v1](https://huggingface.co/lightonai/GTE-ModernColBERT-v1) and evaluated its perfomance in fp32 and bf16 precision (denoted by `GTE fp32` and `GTE bf16`, respectively).
The last two columns of each table — **Iso bf16** and **Δ vs GTE** — describe Iso-ModernColBERT (ours) in bf16 precision.
In all configurations we used the same SMVE implementation with width 65536 and k=32.

## BEIR

### NDCG@10 — ranking quality is robust to bf16

End-to-end ranking quality reported as NDCG@10, using **exact MaxSim** scoring (no approximation). GTE-ModernColBERT-v1 loses ~7 NDCG points on average going from fp32 → bf16 — about a 13% relative drop — with the worst-hit datasets (trec-covid, climate-fever, hotpotqa) dropping 12–16 points. Iso-ModernColBERT keeps fp32-level ranking quality in bf16, recovering most of that gap on average and on every dataset.

| dataset       | GTE fp32 N@10 | GTE bf16 N@10 | **Iso bf16 N@10** | **Δ vs GTE bf16** |
|---------------|--------------:|--------------:|------------------:|------------------:|
| arguana       | 35.81%        | 30.35%        | **34.63%**        | **+14.10%**       |
| climate-fever | 32.44%        | 19.49%        | **31.62%**        | **+62.24%**       |
| cqadupstack   | 40.54%        | 38.25%        | **40.64%**        | **+6.25%**        |
| dbpedia       | 53.96%        | 48.43%        | **52.84%**        | **+9.11%**        |
| fever         | 88.80%        | 80.67%        | **87.08%**        | **+7.95%**        |
| fiqa          | 45.56%        | 37.15%        | **43.48%**        | **+17.04%**       |
| hotpotqa      | 78.36%        | 66.74%        | **75.85%**        | **+13.65%**       |
| msmarco       | 46.12%        | 41.82%        | **45.30%**        | **+8.32%**        |
| nfcorpus      | 37.81%        | 35.98%        | **37.31%**        | **+3.70%**        |
| nq            | 62.24%        | 52.60%        | **60.45%**        | **+14.92%**       |
| quora         | 86.63%        | 79.58%        | **85.05%**        | **+6.87%**        |
| scidocs       | 19.49%        | 17.82%        | **18.81%**        | **+5.56%**        |
| scifact       | 75.98%        | 71.55%        | **75.26%**        | **+5.18%**        |
| touche2020    | 31.30%        | 22.93%        | **29.45%**        | **+28.43%**       |
| trec-covid    | 89.30%        | 73.47%        | **83.76%**        | **+14.01%**       |
| **avg**       | **54.96%**    | **47.79%**    | **53.44%**        | **+11.82%**       |

### Recall@100 — SMVE as a first stage with ~10× overfetch
The following results show model performance when used with [Sparse Multi-Vector Encoder (SMVE)](https://www.topk.io/blog/20260311-smve-multi-vector-retrieval) as a first stage retriever.

For a SMVE first stage to be usable, it needs to surface the candidates that the exact fp32 MaxSim model would have ranked at the top. SMVE on GTE-ModernColBERT-v1 is broken — its compacted latent geometry means random anchors don't separate vectors well. Iso-ModernColBERT's SMVE recovers (and often exceeds) the fp32 MaxSim top-10 within 10× overfetch.

| dataset       | GTE fp32 MaxSim R@10 | GTE fp32 SMVE R@100 | **Iso bf16 SMVE R@100** | **Δ vs GTE fp32 SMVE** |
|---------------|---------------------:|--------------------:|------------------------:|-----------------------:|
| arguana       | 72.81%               | 27.69%              | **84.51%**              | **+205.20%**           |
| climate-fever | 39.27%               | 0.41%               | **48.84%**              | **+11,812%** ⚠         |
| cqadupstack   | 50.48%               | 11.78%              | **37.29%**              | **+216.55%**           |
| dbpedia       | 30.45%               | 8.54%               | **36.89%**              | **+331.97%**           |
| fever         | 94.20%               | 10.05%              | **94.31%**              | **+838.41%**           |
| fiqa          | 52.15%               | 6.45%               | **49.12%**              | **+661.55%**           |
| hotpotqa      | 80.73%               | 12.29%              | **66.59%**              | **+441.82%**           |
| msmarco       | 68.64%               | 27.77%              | **75.83%**              | **+173.07%**           |
| nfcorpus      | 18.03%               | 16.63%              | **25.60%**              | **+53.94%**            |
| nq            | 82.03%               | 14.60%              | **78.85%**              | **+440.07%**           |
| quora         | 94.92%               | 43.73%              | **82.86%**              | **+89.48%**            |
| scidocs       | 20.36%               | 12.29%              | **29.32%**              | **+138.57%**           |
| scifact       | 87.39%               | 60.93%              | **90.00%**              | **+47.71%**            |
| touche2020    | 19.69%               | 4.47%               | **40.17%**              | **+798.66%**           |
| trec-covid    | 2.27%                | 0.89%               | **7.73%**               | **+768.54%**           |
| **avg**       | **54.23%**           | **17.23%**          | **56.53%**              | **+228.09%**           |

> ⚠ The +11,812% on climate-fever is an artifact of a near-zero baseline (0.41%): GTE's SMVE is so broken on that dataset that the ratio explodes. Read it as *"GTE SMVE doesn't work here at all"*, not as a meaningful magnitude.

### Recall@1000 — SMVE as a first stage with ~10× overfetch (deeper pool)

Same picture at the next pool depth: Iso-ModernColBERT SMVE R@1000 essentially matches or exceeds fp32 MaxSim R@100 across the board, while GTE's SMVE collapses.

| dataset       | GTE fp32 MaxSim R@100 | GTE fp32 SMVE R@1000 | **Iso bf16 SMVE R@1000** | **Δ vs GTE fp32 SMVE** |
|---------------|----------------------:|---------------------:|-------------------------:|-----------------------:|
| arguana       | 95.72%                | 68.31%               | **97.00%**               | **+42.00%**            |
| climate-fever | 66.45%                | 0.93%                | **68.87%**               | **+7,305%** ⚠          |
| cqadupstack   | 71.44%                | 26.78%               | **55.78%**               | **+108.29%**           |
| dbpedia       | 62.50%                | 18.35%               | **57.72%**               | **+214.55%**           |
| fever         | 97.46%                | 16.74%               | **96.91%**               | **+478.91%**           |
| fiqa          | 75.64%                | 21.09%               | **76.70%**               | **+263.68%**           |
| hotpotqa      | 90.31%                | 22.72%               | **78.83%**               | **+247.05%**           |
| msmarco       | 93.14%                | 46.57%               | **90.97%**               | **+95.34%**            |
| nfcorpus      | 32.22%                | 49.11%               | **57.16%**               | **+16.39%**            |
| nq            | 96.59%                | 29.88%               | **91.42%**               | **+205.96%**           |
| quora         | 99.45%                | 69.38%               | **94.86%**               | **+36.72%**            |
| scidocs       | 44.07%                | 32.62%               | **53.43%**               | **+63.80%**            |
| scifact       | 96.00%                | 89.82%               | **99.33%**               | **+10.59%**            |
| touche2020    | 52.60%                | 13.91%               | **69.63%**               | **+400.58%**           |
| trec-covid    | 16.02%                | 3.85%                | **29.57%**               | **+668.05%**           |
| **avg**       | **72.64%**            | **34.00%**           | **74.55%**               | **+119.26%**           |

> ⚠ Again, climate-fever's +7,305% is driven by a near-zero baseline (0.93%) — GTE SMVE simply doesn't work on this dataset.

## NanoBEIR

### NDCG@10 — ranking quality is robust to bf16

End-to-end ranking quality reported as NDCG@10, using **exact MaxSim** scoring (no approximation). GTE-ModernColBERT-v1 drops ~6 NDCG points on average going from fp32 → bf16 — about a 9% relative drop — with some datasets (ArguAna, ClimateFEVER, FiQA, Touche2020) losing 8–13 points. Iso-ModernColBERT keeps fp32-level ranking quality in bf16 — average is within 0.6 points of fp32, and most per-dataset gaps close to a few percent.

| dataset        | GTE fp32 N@10 | GTE bf16 N@10 | **Iso bf16 N@10** | **Δ vs GTE bf16** |
|----------------|--------------:|--------------:|------------------:|------------------:|
| ArguAna        | 51.98%        | 43.50%        | **54.31%**        | **+24.85%**       |
| ClimateFEVER   | 40.46%        | 27.78%        | **38.17%**        | **+37.40%**       |
| DBPedia        | 72.82%        | 70.39%        | **71.56%**        | **+1.66%**        |
| FEVER          | 94.52%        | 89.82%        | **93.23%**        | **+3.80%**        |
| FiQA2018       | 56.64%        | 44.13%        | **55.79%**        | **+26.42%**       |
| HotpotQA       | 89.95%        | 85.64%        | **90.47%**        | **+5.64%**        |
| MSMARCO        | 70.89%        | 68.77%        | **72.56%**        | **+5.51%**        |
| NFCorpus       | 39.58%        | 39.20%        | **38.67%**        | **-1.35%**        |
| NQ             | 77.19%        | 69.01%        | **73.64%**        | **+6.71%**        |
| QuoraRetrieval | 97.08%        | 90.60%        | **96.53%**        | **+6.54%**        |
| SCIDOCS        | 39.85%        | 38.02%        | **38.14%**        | **+0.32%**        |
| SciFact        | 82.98%        | 80.45%        | **83.32%**        | **+3.57%**        |
| Touche2020     | 59.34%        | 48.67%        | **58.77%**        | **+20.75%**       |
| **avg**        | **67.18%**    | **61.23%**    | **66.55%**        | **+8.69%**        |

### Recall@100 — SMVE as a first stage with ~10× overfetch
The following results show model performance when used with [Sparse Multi-Vector Encoder (SMVE)](https://www.topk.io/blog/20260311-smve-multi-vector-retrieval) as a first stage retriever.

For a SMVE first stage to be usable, it needs to surface the candidates that the exact fp32 MaxSim model would have ranked at the top. SMVE on GTE-ModernColBERT-v1 is broken — its compacted latent geometry means random anchors don't separate vectors well. Iso-ModernColBERT's SMVE recovers (and often exceeds) fp32 MaxSim's top-10 within 10× overfetch.

| dataset        | GTE fp32 MaxSim R@10 | GTE fp32 SMVE R@100 | **Iso bf16 SMVE R@100** | **Δ vs GTE fp32 SMVE** |
|----------------|---------------------:|--------------------:|------------------------:|-----------------------:|
| ArguAna        | 80.00%               | 32.00%              | **90.00%**              | **+181.25%**           |
| ClimateFEVER   | 47.07%               | 20.67%              | **66.97%**              | **+224.00%**           |
| DBPedia        | 41.21%               | 49.00%              | **72.85%**              | **+48.67%**            |
| FEVER          | 98.00%               | 61.33%              | **98.00%**              | **+59.79%**            |
| FiQA2018       | 64.12%               | 23.25%              | **78.93%**              | **+239.48%**           |
| HotpotQA       | 92.00%               | 46.00%              | **90.00%**              | **+95.65%**            |
| MSMARCO        | 92.00%               | 84.00%              | **98.00%**              | **+16.67%**            |
| NFCorpus       | 15.66%               | 16.33%              | **24.58%**              | **+50.52%**            |
| NQ             | 88.00%               | 70.00%              | **95.00%**              | **+35.71%**            |
| QuoraRetrieval | 98.93%               | 87.93%              | **96.60%**              | **+9.86%**             |
| SCIDOCS        | 39.67%               | 37.87%              | **61.17%**              | **+61.53%**            |
| SciFact        | 93.00%               | 57.50%              | **92.00%**              | **+60.00%**            |
| Touche2020     | 33.52%               | 33.55%              | **69.86%**              | **+108.23%**           |
| **avg**        | **67.94%**           | **47.65%**          | **79.53%**              | **+66.91%**            |

### Recall@1000 — SMVE as a first stage with ~10× overfetch (deeper pool)

Same picture at the next pool depth: Iso-ModernColBERT SMVE R@1000 essentially matches or exceeds fp32 MaxSim R@100 across the board, while GTE's SMVE consistently undershoots.

| dataset        | GTE fp32 MaxSim R@100 | GTE fp32 SMVE R@1000 | **Iso bf16 SMVE R@1000** | **Δ vs GTE fp32 SMVE** |
|----------------|----------------------:|---------------------:|-------------------------:|-----------------------:|
| ArguAna        | 96.00%                | 80.00%               | **100.00%**              | **+25.00%**            |
| ClimateFEVER   | 81.17%                | 68.80%               | **89.03%**               | **+29.40%**            |
| DBPedia        | 85.58%                | 84.85%               | **96.20%**               | **+13.38%**            |
| FEVER          | 100.00%               | 94.33%               | **99.00%**               | **+4.95%**             |
| FiQA2018       | 86.82%                | 72.61%               | **91.35%**               | **+25.81%**            |
| HotpotQA       | 97.00%                | 84.00%               | **98.00%**               | **+16.67%**            |
| MSMARCO        | 100.00%               | 98.00%               | **100.00%**              | **+2.04%**             |
| NFCorpus       | 30.55%                | 52.82%               | **59.33%**               | **+12.32%**            |
| NQ             | 100.00%               | 91.00%               | **100.00%**              | **+9.89%**             |
| QuoraRetrieval | 100.00%               | 96.00%               | **100.00%**              | **+4.17%**             |
| SCIDOCS        | 70.67%                | 78.93%               | **90.80%**               | **+15.04%**            |
| SciFact        | 96.00%                | 93.00%               | **100.00%**              | **+7.53%**             |
| Touche2020     | 77.23%                | 80.46%               | **93.09%**               | **+15.70%**            |
| **avg**        | **86.23%**            | **82.68%**           | **93.60%**               | **+13.21%**            |