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pubmedbert-base-embeddings-matryoshka

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metadata
pipeline_tag: sentence-similarity
tags:
  - sentence-transformers
  - feature-extraction
  - sentence-similarity
  - transformers
language: en
license: apache-2.0

PubMedBERT Embeddings Matryoshka

This is a version of PubMedBERT Embeddings with Matryoshka Representation Learning applied. This enables dynamic embeddings sizes of 64, 128, 256, 384, 512 and the full size of 768. It's important to note while this method saves space, the same computational resources are used regardless of the dimension size.

Sentence Transformers 2.4 added support for Matryoshka Embeddings. More can be read in this blog post.

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).

import txtai

# New embeddings with requested dimensionality
embeddings = txtai.Embeddings(
  path="neuml/pubmedbert-base-embeddings-matryoshka",
  content=True,
  dimensionality=256
)
embeddings.index(documents())

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

Usage (Sentence-Transformers)

Alternatively, the model can be loaded with sentence-transformers.

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

model = SentenceTransformer("neuml/pubmedbert-base-embeddings-matryoshka")
embeddings = model.encode(sentences)

# Requested dimensionality
dimensionality = 256

print(embeddings[:, :dimensionality])

Usage (Hugging Face Transformers)

The model can also be used directly with Transformers. 

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/pubmedbert-base-embeddings-matryoshka")
model = AutoModel.from_pretrained("neuml/pubmedbert-base-embeddings-matryoshka")

# 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'])

# Requested dimensionality
dimensionality = 256

print("Sentence embeddings:")
print(embeddings[:, :dimensionality])

Evaluation Results

Performance of this model compared to the top base models on the MTEB leaderboard is shown below. A popular smaller model was also evaluated along with the most downloaded PubMed similarity model on the Hugging Face Hub.

The following datasets were used to evaluate model performance.

  • PubMed QA
    • Subset: pqa_labeled, Split: train, Pair: (question, long_answer)
  • PubMed Subset
    • Split: test, Pair: (title, text)
  • PubMed Summary
    • Subset: pubmed, Split: validation, Pair: (article, abstract)

Evaluation results from the original model are shown below for reference. The Pearson correlation coefficient is used as the evaluation metric.

Model PubMed QA PubMed Subset PubMed Summary Average
all-MiniLM-L6-v2 90.40 95.86 94.07 93.44
bge-base-en-v1.5 91.02 95.60 94.49 93.70
gte-base 92.97 96.83 96.24 95.35
pubmedbert-base-embeddings 93.27 97.07 96.58 95.64
S-PubMedBert-MS-MARCO 90.86 93.33 93.54 92.58

See the table below for evaluation results per dimension for pubmedbert-base-embeddings-matryoshka.

Model PubMed QA PubMed Subset PubMed Summary Average
Dimensions = 64 92.16 95.85 95.67 94.56
Dimensions = 128 92.80 96.44 96.22 95.15
Dimensions = 256 93.11 96.68 96.53 95.44
Dimensions = 384 93.42 96.79 96.61 95.61
Dimensions = 512 93.37 96.87 96.61 95.62
Dimensions = 768 93.53 96.95 96.70 95.73

This model performs slightly better overall compared to the original model.

The bigger takeaway is how competitive it is at lower dimensions. For example, Dimensions = 256 performs better than all the other models originally tested above. Even Dimensions = 64 performs better than all-MiniLM-L6-v2 and bge-base-en-v1.5.

Training

The model was trained with the parameters:

DataLoader:

torch.utils.data.dataloader.DataLoader of length 20191 with parameters:

{'batch_size': 24, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}

Loss:

sentence_transformers.losses.MatryoshkaLoss.MatryoshkaLoss with parameters:

{'loss': 'MultipleNegativesRankingLoss', 'matryoshka_dims': [768, 512, 384, 256, 128, 64], 'matryoshka_weights': [1, 1, 1, 1, 1, 1]}

Parameters of the fit()-Method:

{
    "epochs": 1,
    "evaluation_steps": 500,
    "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
    "max_grad_norm": 1,
    "optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
    "optimizer_params": {
        "lr": 2e-05
    },
    "scheduler": "WarmupLinear",
    "steps_per_epoch": null,
    "warmup_steps": 10000,
    "weight_decay": 0.01
}

Full Model Architecture 

SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 768, '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})
)