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pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
base_model: NeuML/bert-hash-nano
language: en
license: apache-2.0
---
# BERT Hash Nano Embeddings
This is a [BERT Hash Nano](https://hf.co/neuml/bert-hash-nano) model fined-tuned using [sentence-transformers](https://www.SBERT.net). It maps sentences & paragraphs to a 128-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
This model is an alternative to [MUVERA fixed-dimensional encoding](https://arxiv.org/abs/2405.19504) with ColBERT models. MUVERA encoding enables encoding the multi-vector outputs of ColBERT into single dense vector outputs. While this is a great step, the main issue with MUVERA is that it tends to need wide vectors to be effective (5K - 10K dimensional vectors). `bert-hash-nano-embeddings` outputs 128-dimensional vectors.
The training dataset is a subset of [this embedding training collection](https://huggingface.co/collections/sentence-transformers/embedding-model-datasets). The training workflow was a two step distillation process as follows.
- Distill embeddings from the larger [mixedbread-ai/mxbai-embed-xsmall-v1](https://huggingface.co/mixedbread-ai/mxbai-embed-xsmall-v1) model using this [model distillation script](https://github.com/huggingface/sentence-transformers/blob/main/examples/sentence_transformer/training/distillation/model_distillation.py) from Sentence Transformers.
- Build a distilled dataset of teacher scores using the [mixedbread-ai/mxbai-rerank-xsmall-v1](https://hf.co/mixedbread-ai/mxbai-rerank-xsmall-v1) cross-encoder for a random sample of the training dataset mentioned above.
- Further fine-tune the model on the distilled dataset using [KLDivLoss](https://github.com/huggingface/sentence-transformers/blob/main/sentence_transformers/losses/DistillKLDivLoss.py).
## 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).
```python
import txtai
embeddings = txtai.Embeddings(
path="neuml/bert-hash-nano-embeddings",
content=True,
vectors={"trust_remote_code": True}
)
embeddings.index(documents())
# Run a query
embeddings.search("query to run")
```
## Usage (Sentence-Transformers)
Alternatively, the model can be loaded with [sentence-transformers](https://www.SBERT.net).
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer("neuml/bert-hash-nano-embeddings", trust_remote_code=True)
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (Hugging Face Transformers)
The model can also be used directly with Transformers.
```python
from transformers import AutoTokenizer, AutoModel
import torch
# Mean Pooling - Take attention mask into account for correct averaging
def meanpooling(output, mask):
embeddings = output[0] # First element of model_output contains all token embeddings
mask = mask.unsqueeze(-1).expand(embeddings.size()).float()
return torch.sum(embeddings * mask, 1) / torch.clamp(mask.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained("neuml/bert-hash-nano-embeddings", trust_remote_code=True)
model = AutoModel.from_pretrained("neuml/bert-hash-nano-embeddings", trust_remote_code=True)
# Tokenize sentences
inputs = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
output = model(**inputs)
# Perform pooling. In this case, mean pooling.
embeddings = meanpooling(output, inputs['attention_mask'])
print("Sentence embeddings:")
print(embeddings)
```
## Evaluation
The following table shows a subset of BEIR scored with the [txtai benchmarks script](https://github.com/neuml/txtai/blob/master/examples/benchmarks.py).
This evaluation is compared against the [ColBERT MUVERA](https://huggingface.co/collections/NeuML/colbert) series of models.
Scores reported are `ndcg@10` and grouped into the following three categories.
### BERT Hash Embeddings vs MUVERA
| Model | Parameters | NFCorpus | SciDocs | SciFact | Average |
|:------------------|:-----------|:---------|:---------|:--------|:--------|
| [**BERT Hash Nano Embeddings**](https://huggingface.co/neuml/bert-hash-nano-embeddings) | **0.9M** | **0.2562** | **0.1179** | **0.5032** | **0.2924** |
| [ColBERT MUVERA Nano](https://huggingface.co/neuml/colbert-muvera-nano) | 0.9M | 0.2355 | 0.0807 | 0.4904 | 0.2689 |
### BERT Hash Embeddings vs MUVERA with maxsim re-ranking of the top 100 results per MUVERA paper
| Model | Parameters | NFCorpus | SciDocs | SciFact | Average |
|:------------------|:-----------|:---------|:---------|:--------|:--------|
| [**BERT Hash Nano Embeddings**](https://huggingface.co/neuml/bert-hash-nano-embeddings) | **0.9M** | **0.3101** | **0.1347** | **0.6327** | **0.3592** |
| [ColBERT MUVERA Nano](https://huggingface.co/neuml/colbert-muvera-nano) | 0.9M | 0.2996 | 0.1201 | 0.6249 | 0.3482 |
### Compare to other models
| Model | Parameters | NFCorpus | SciDocs | SciFact | Average |
|:------------------|:-----------|:---------|:---------|:--------|:--------|
| [ColBERT MUVERA Nano (full multi-vector maxsim)](https://huggingface.co/neuml/colbert-muvera-nano) | 0.9M | 0.3180 | 0.1262 | 0.6576 | 0.3673 |
| [all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) | 22.7M | 0.3089 | 0.2164 | 0.6527 | 0.3927 |
| [mxbai-embed-xsmall-v1](https://huggingface.co/mixedbread-ai/mxbai-embed-xsmall-v1) | 24.1M | 0.3186 | 0.2155 | 0.6598 | 0.3980 |
In analyzing the results, `bert-hash-nano-embeddings` is better across the board vs MUVERA with `colbert-muvera-nano`. It keeps `98%` of the performance of full multi-vector maxsim vs `95%` for MUVERA. Comparing the standard MUVERA output of `10240` vs `128` dimensions, `10K` standard F32 vectors needs `400 MB` of storage vs `5 MB`
By itself for a `970K` parameter model, the scores are really good. When paired with re-ranking with a `970K` ColBERT model, the scores are even better. Competitive with common small models as shown above at only `~4%` of the number of parameters.
While this isn't a state of the art model, it's an extremely competitive method for building vectors on edge and low resource devices.
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False, 'architecture': 'BertHashModel'})
(1): Pooling({'word_embedding_dimension': 128, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```
## More Information
Read more about this model and how it was built in [this article](https://hf.co/blog/neuml/bert-hash-embeddings).
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