---
tags:
- ColBERT
- PyLate
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- loss:Distillation
base_model: NeuML/biomedbert-small
pipeline_tag: sentence-similarity
library_name: PyLate
language: en
license: apache-2.0
---

# BiomedBERT Small ColBERT

This is a [PyLate](https://github.com/lightonai/pylate) model finetuned from [neuml/biomedbert-small](https://hf.co/neuml/biomedbert-small). It maps sentences & paragraphs to sequences of 128-dimensional dense vectors and can be used for semantic textual similarity using the MaxSim operator.

## Usage (txtai)

This model can be used to build embeddings databases with [txtai](https://github.com/neuml/txtai) for semantic search and/or as a knowledge source for retrieval augmented generation (RAG). Note that since this is a custom architecture, `trust_remote_code` is required to be enabled.

```python
import txtai

embeddings = txtai.Embeddings(
  path="neuml/biomedbert-small-colbert",
  content=True
)
embeddings.index(documents())

# Run a query
embeddings.search("query to run")
```

Late interaction models excel as reranker pipelines.

```python
from txtai.pipeline import Reranker, Similarity

similarity = Similarity(path="neuml/biomedbert-small-colbert", lateencode=True)
ranker = Reranker(embeddings, similarity)
ranker("query to run")
```

## Usage (PyLate)

Alternatively, the model can be loaded with [PyLate](https://github.com/lightonai/pylate).

```python
from pylate import rank, models

queries = [
    "query A",
    "query B",
]

documents = [
    ["document A", "document B"],
    ["document 1", "document C", "document B"],
]

documents_ids = [
    [1, 2],
    [1, 3, 2],
]

model = models.ColBERT(
    model_name_or_path="neuml/biomedbert-small-colbert", trust_remote_code=True
)

queries_embeddings = model.encode(
    queries,
    is_query=True,
)

documents_embeddings = model.encode(
    documents,
    is_query=False,
)

reranked_documents = rank.rerank(
    documents_ids=documents_ids,
    queries_embeddings=queries_embeddings,
    documents_embeddings=documents_embeddings,
)
```

## Evaluation Results

Performance of these models are compared to previously released models trained on medical literature. The most commonly used small embeddings model is also included for comparison.

The following datasets were used to evaluate model performance.

- [PubMed QA](https://huggingface.co/datasets/qiaojin/PubMedQA)
  - Subset: pqa_labeled, Split: train, Pair: (question, long_answer)
- [PubMed Subset](https://huggingface.co/datasets/awinml/pubmed_abstract_3_1k)
  - Split: test, Pair: (title, text)
- [PubMed Summary](https://huggingface.co/datasets/armanc/scientific_papers)
  - Subset: pubmed, Split: validation, Pair: (article, abstract)

Evaluation results are shown below. The [Pearson correlation coefficient](https://en.wikipedia.org/wiki/Pearson_correlation_coefficient) is used as the evaluation metric.

| Model                                                 | PubMed QA | PubMed Subset | PubMed Summary | Average   |
| ----------------------------------------------------- | --------- | ------------- | -------------- | --------- |
| [all-MiniLM-L6-v2](https://hf.co/sentence-transformers/all-MiniLM-L6-v2) | 90.40  | 95.92 | 94.07 | 93.46 |
| [biomedbert-base-colbert](https://hf.co/neuml/biomedbert-base-colbert)  | 94.59 | 97.18 | 96.21  | 95.99 |
| [biomedbert-base-embeddings](https://hf.co/neuml/biomedbert-base-embeddings)  | 94.60 | 98.39  | 97.61 | 96.87 |
| [biomedbert-base-reranker](https://hf.co/neuml/biomedbert-base-reranker)  | 97.66 | 99.76  | 98.81 | 98.74 |
| [**biomedbert-small-colbert**](https://hf.co/neuml/biomedbert-small-colbert)  | **93.51** | **97.20** | **95.85** | **95.52** |
| [biomedbert-small-embeddings](https://hf.co/neuml/biomedbert-small-embeddings)  | 93.25 | 97.93  | 96.65 | 95.94 |
| [biomedbert-hash-nano-colbert](https://hf.co/neuml/biomedbert-hash-nano-colbert) | 90.45 | 96.81 | 92.00 | 93.09 |
| [biomedbert-hash-nano-embeddings](https://hf.co/neuml/biomedbert-hash-nano-embeddings)  | 90.39 | 96.29 | 95.32 | 94.00 |
| [pubmedbert-base-embeddings](https://hf.co/neuml/pubmedbert-base-embeddings)       | 93.27  | 97.00 | 96.58 | 95.62 |

As with other ColBERT models on this dataset, it tends to score lower with longer form queries. But note how this model outperforms it's equivalent small model on the PubMed QA dataset. For traditional user queries, this model will likely get better results in production.

## Full Model Architecture

```
ColBERT(
  (0): Transformer({'max_seq_length': 511, 'do_lower_case': False, 'architecture': 'BertModel'})
  (1): Dense({'in_features': 384, 'out_features': 128, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity', 'use_residual': False})
)
```

## More Information

Read more about the model in [this article](https://huggingface.co/blog/NeuML/biomedbert-small).