monsoon-nlp
/

protein-matryoshka-embeddings

@@ -3,31 +3,40 @@ library_name: sentence-transformers
 pipeline_tag: sentence-similarity
 tags:
 - sentence-transformers
-- feature-extraction
 - sentence-similarity
 - transformers
 ---
-# monsoon-nlp/protein-matryoshka-embeddings
-This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search.
-<!--- Describe your model here -->
-## Usage (Sentence-Transformers)
-Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
 ```
-pip install -U sentence-transformers
 ```
-Then you can use the model like this:
 ```python
 from sentence_transformers import SentenceTransformer
-sentences = ["This is an example sentence", "Each sentence is converted"]
 model = SentenceTransformer('monsoon-nlp/protein-matryoshka-embeddings')
 embeddings = model.encode(sentences)
@@ -35,96 +44,29 @@ print(embeddings)
 ```
-## Usage (HuggingFace Transformers)
-Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
-```python
-from transformers import AutoTokenizer, AutoModel
-import torch
-#Mean Pooling - Take attention mask into account for correct averaging
-def mean_pooling(model_output, attention_mask):
-    token_embeddings = model_output[0] #First element of model_output contains all token embeddings
-    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
-    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.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('monsoon-nlp/protein-matryoshka-embeddings')
-model = AutoModel.from_pretrained('monsoon-nlp/protein-matryoshka-embeddings')
-# Tokenize sentences
-encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
-# Compute token embeddings
-with torch.no_grad():
-    model_output = model(**encoded_input)
-# Perform pooling. In this case, mean pooling.
-sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
-print("Sentence embeddings:")
-print(sentence_embeddings)
-```
-## Evaluation Results
-<!--- Describe how your model was evaluated -->
-For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=monsoon-nlp/protein-matryoshka-embeddings)
-## Training
-The model was trained with the parameters:
-**DataLoader**:
-`sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 30000 with parameters:
 ```
-{'batch_size': 10}
 ```
-**Loss**:
-`sentence_transformers.losses.MatryoshkaLoss.MatryoshkaLoss` with parameters:
-  ```
-  {'loss': 'CoSENTLoss', 'matryoshka_dims': [768, 256, 128, 64], 'matryoshka_weights': [1, 1, 1, 1], 'n_dims_per_step': -1}
-  ```
-Parameters of the fit()-Method:
-```
-{
-    "epochs": 1,
-    "evaluation_steps": 3000,
-    "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": 3000,
-    "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': 1024, '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})
-)
-```
-## Citing & Authors
-<!--- Describe where people can find more information -->

 pipeline_tag: sentence-similarity
 tags:
 - sentence-transformers
 - sentence-similarity
 - transformers
+- biology
 ---
+# Protein Matryoshka Embeddings
+The model generates an embedding for input proteins. It was trained using [Matryoshka loss](https://huggingface.co/blog/matryoshka),
+so shortened embeddings can be used for faster search and other tasks.
+Inputs use [IUPAC-IUB codes](https://en.wikipedia.org/wiki/FASTA_format#Sequence_representation) where letters A-Z map to amino acids. For example:
+"M A R N W S F R V"
+The base model was [Rostlab/prot_bert_bfd](https://huggingface.co/Rostlab/prot_bert_bfd).
+A [sentence-transformers](https://github.com/UKPLab/sentence-transformers) model was trained on cosine-similarity of embeddings
+from [UniProt](https://www.uniprot.org/help/downloads#embeddings).
+For train/test/validation datasets of embeddings and distances, see: https://huggingface.co/datasets/monsoon-nlp/protein-pairs-uniprot-swissprot
+## Usage
+Install these dependencies:
 ```
+pip install -U sentence-transformers datasets
 ```
+Generating embeddings:
 ```python
 from sentence_transformers import SentenceTransformer
+sequences = ["M S L E Q K...", "M A R N W S F R V..."]
 model = SentenceTransformer('monsoon-nlp/protein-matryoshka-embeddings')
 embeddings = model.encode(sentences)
 ```
+## Validation Results
+On 1,000 protein pairs from the validation dataset:
 ```
+|steps|cosine_pearson|cosine_spearman|
+|-----|--------------|---------------|
+|3000|0.8598688660086558|0.8666855900999677|
+|6000|0.8692703523988448|0.8615673651584274|
+|9000|0.8779733537629968|0.8754158959780602|
+|12000|0.8877422045031667|0.8881492475969834|
+|15000|0.9027359688395733|0.899106724739699|
+|18000|0.9046675789738002|0.9044183600191271|
+|21000|0.9165801536390973|0.9061381997421003|
+|24000|0.9128046401341833|0.9076748537082228|
+|27000|0.918547416546341|0.9127677526055185|
+|30000|0.9239429677657788|0.9187051589781693|
 ```
+## Future
+This model will be updated when I have examples using it on protein classification tasks.
+I'm interested in whether [embedding quantization](https://huggingface.co/blog/embedding-quantization) could improve these better.
+If you want to collaborate on future projects / have resources to train longer on more embeddings, please get in touch.