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raul3820/
bert-hash-nano-embeddings

Name: bert-hash-nano-embeddings
Availability: InStock

sentence-transformers

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BERT Hash Nano Embeddings

This is a BERT Hash Nano model fined-tuned using sentence-transformers. 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 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. The training workflow was a two step distillation process as follows.

Distill embeddings from the larger mixedbread-ai/mxbai-embed-xsmall-v1 model using this model distillation script from Sentence Transformers.
Build a distilled dataset of teacher scores using the 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.

Usage (txtai)

This model can be used to build embeddings databases with txtai for semantic search and/or as a knowledge source for retrieval augmented generation (RAG).

python

import txtai

embeddings = txtai.Embeddings(
  path="raul3820/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.

python

from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]

model = SentenceTransformer("raul3820/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("raul3820/bert-hash-nano-embeddings", trust_remote_code=True)
model = AutoModel.from_pretrained("raul3820/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.

This evaluation is compared against the ColBERT MUVERA 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	0.9M	0.2562	0.1179	0.5032	0.2924
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	0.9M	0.3101	0.1347	0.6327	0.3592
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)	0.9M	0.3180	0.1262	0.6576	0.3673
all-MiniLM-L6-v2	22.7M	0.3089	0.2164	0.6527	0.3927
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.