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--- |
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pipeline_tag: sentence-similarity |
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tags: |
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- sentence-transformers |
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- feature-extraction |
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- sentence-similarity |
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- transformers |
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language: en |
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license: apache-2.0 |
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--- |
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# PubMedBERT Embeddings Matryoshka |
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This is a version of [PubMedBERT Embeddings](https://huggingface.co/NeuML/pubmedbert-base-embeddings) with [Matryoshka Representation Learning](https://arxiv.org/abs/2205.13147) 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. |
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Sentence Transformers 2.4 added support for Matryoshka Embeddings. More can be read in [this blog post](https://huggingface.co/blog/matryoshka). |
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## Usage (txtai) |
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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). |
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```python |
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import txtai |
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# New embeddings with requested dimensionality |
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embeddings = txtai.Embeddings( |
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path="neuml/pubmedbert-base-embeddings-matryoshka", |
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content=True, |
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dimensionality=256 |
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) |
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embeddings.index(documents()) |
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# Run a query |
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embeddings.search("query to run") |
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``` |
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## Usage (Sentence-Transformers) |
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Alternatively, the model can be loaded with [sentence-transformers](https://www.SBERT.net). |
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```python |
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from sentence_transformers import SentenceTransformer |
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sentences = ["This is an example sentence", "Each sentence is converted"] |
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model = SentenceTransformer("neuml/pubmedbert-base-embeddings-matryoshka") |
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embeddings = model.encode(sentences) |
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# Requested dimensionality |
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dimensionality = 256 |
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print(embeddings[:, :dimensionality]) |
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``` |
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## Usage (Hugging Face Transformers) |
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The model can also be used directly with Transformers. |
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```python |
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from transformers import AutoTokenizer, AutoModel |
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import torch |
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# Mean Pooling - Take attention mask into account for correct averaging |
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def meanpooling(output, mask): |
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embeddings = output[0] # First element of model_output contains all token embeddings |
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mask = mask.unsqueeze(-1).expand(embeddings.size()).float() |
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return torch.sum(embeddings * mask, 1) / torch.clamp(mask.sum(1), min=1e-9) |
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# Sentences we want sentence embeddings for |
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sentences = ['This is an example sentence', 'Each sentence is converted'] |
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# Load model from HuggingFace Hub |
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tokenizer = AutoTokenizer.from_pretrained("neuml/pubmedbert-base-embeddings-matryoshka") |
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model = AutoModel.from_pretrained("neuml/pubmedbert-base-embeddings-matryoshka") |
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# Tokenize sentences |
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inputs = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') |
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# Compute token embeddings |
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with torch.no_grad(): |
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output = model(**inputs) |
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# Perform pooling. In this case, mean pooling. |
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embeddings = meanpooling(output, inputs['attention_mask']) |
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# Requested dimensionality |
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dimensionality = 256 |
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print("Sentence embeddings:") |
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print(embeddings[:, :dimensionality]) |
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``` |
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## Evaluation Results |
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Performance of this model compared to the top base models on the [MTEB leaderboard](https://huggingface.co/spaces/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. |
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The following datasets were used to evaluate model performance. |
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- [PubMed QA](https://huggingface.co/datasets/pubmed_qa) |
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- Subset: pqa_labeled, Split: train, Pair: (question, long_answer) |
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- [PubMed Subset](https://huggingface.co/datasets/zxvix/pubmed_subset_new) |
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- Split: test, Pair: (title, text) |
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- [PubMed Summary](https://huggingface.co/datasets/scientific_papers) |
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- Subset: pubmed, Split: validation, Pair: (article, abstract) |
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Evaluation results from the original model are shown below for reference. The [Pearson correlation coefficient](https://en.wikipedia.org/wiki/Pearson_correlation_coefficient) is used as the evaluation metric. |
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| Model | PubMed QA | PubMed Subset | PubMed Summary | Average | |
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| ----------------------------------------------------------------------------- | --------- | ------------- | -------------- | --------- | |
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| [all-MiniLM-L6-v2](https://hf.co/sentence-transformers/all-MiniLM-L6-v2) | 90.40 | 95.86 | 94.07 | 93.44 | |
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| [bge-base-en-v1.5](https://hf.co/BAAI/bge-large-en-v1.5) | 91.02 | 95.60 | 94.49 | 93.70 | |
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| [gte-base](https://hf.co/thenlper/gte-base) | 92.97 | 96.83 | 96.24 | 95.35 | |
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| [**pubmedbert-base-embeddings**](https://hf.co/neuml/pubmedbert-base-embeddings) | **93.27** | **97.07** | **96.58** | **95.64** | |
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| [S-PubMedBert-MS-MARCO](https://hf.co/pritamdeka/S-PubMedBert-MS-MARCO) | 90.86 | 93.33 | 93.54 | 92.58 | |
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See the table below for evaluation results per dimension for `pubmedbert-base-embeddings-matryoshka`. |
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| Model | PubMed QA | PubMed Subset | PubMed Summary | Average | |
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| --------------------| --------- | ------------- | -------------- | --------- | |
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| Dimensions = 64 | 92.16 | 95.85 | 95.67 | 94.56 | |
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| Dimensions = 128 | 92.80 | 96.44 | 96.22 | 95.15 | |
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| Dimensions = 256 | 93.11 | 96.68 | 96.53 | 95.44 | |
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| Dimensions = 384 | 93.42 | 96.79 | 96.61 | 95.61 | |
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| Dimensions = 512 | 93.37 | 96.87 | 96.61 | 95.62 | |
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| **Dimensions = 768** | **93.53** | **96.95** | **96.70** | **95.73** | |
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This model performs slightly better overall compared to the original model. |
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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`. |
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## Training |
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The model was trained with the parameters: |
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**DataLoader**: |
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`torch.utils.data.dataloader.DataLoader` of length 20191 with parameters: |
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``` |
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{'batch_size': 24, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} |
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``` |
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**Loss**: |
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`sentence_transformers.losses.MatryoshkaLoss.MatryoshkaLoss` with parameters: |
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``` |
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{'loss': 'MultipleNegativesRankingLoss', 'matryoshka_dims': [768, 512, 384, 256, 128, 64], 'matryoshka_weights': [1, 1, 1, 1, 1, 1]} |
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``` |
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Parameters of the fit()-Method: |
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``` |
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{ |
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"epochs": 1, |
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"evaluation_steps": 500, |
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"evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator", |
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"max_grad_norm": 1, |
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"optimizer_class": "<class 'torch.optim.adamw.AdamW'>", |
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"optimizer_params": { |
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"lr": 2e-05 |
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}, |
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"scheduler": "WarmupLinear", |
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"steps_per_epoch": null, |
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"warmup_steps": 10000, |
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"weight_decay": 0.01 |
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} |
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``` |
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## Full Model Architecture |
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``` |
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SentenceTransformer( |
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(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel |
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(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}) |
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) |
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``` |
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