---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
widget:
- source_sentence: "The up-regulation of miR-146a was also detected in cervical cancer tissues."
  sentences: ["The expression of miR-146a has been found to be up-regulated in cervical cancer.", "Only concomitant ablation of ERK1 and ERK2 impairs tumor growth."]
  example_title: "BioNLP Example"
---

# kamalkraj/BioSimCSE-BioLinkBERT-BASE

This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 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('kamalkraj/BioSimCSE-BioLinkBERT-BASE')
embeddings = model.encode(sentences)
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('kamalkraj/BioSimCSE-BioLinkBERT-BASE')
model = AutoModel.from_pretrained('kamalkraj/BioSimCSE-BioLinkBERT-BASE')

# 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=kamalkraj/BioSimCSE-BioLinkBERT-BASE)


## Training
The model was trained with the parameters:

**DataLoader**:

`torch.utils.data.dataloader.DataLoader` of length 7708 with parameters:
```
{'batch_size': 128, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```

**Loss**:

`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
  ```
  {'scale': 20.0, 'similarity_fct': 'cos_sim'}
  ```

Parameters of the fit()-Method:
```
{
    "epochs": 1,
    "evaluation_steps": 0,
    "evaluator": "NoneType",
    "max_grad_norm": 1,
    "optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
    "optimizer_params": {
        "lr": 5e-05
    },
    "scheduler": "WarmupLinear",
    "steps_per_epoch": null,
    "warmup_steps": 771,
    "weight_decay": 0.01
}
```


## Full Model Architecture
```
SentenceTransformer(
  (0): Transformer({'max_seq_length': 128, '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})
)
```

## Citing & Authors

<!--- Describe where people can find more information -->
```bibtex
@inproceedings{kanakarajan-etal-2022-biosimcse,
    title = "{B}io{S}im{CSE}: {B}io{M}edical Sentence Embeddings using Contrastive learning",
    author = "Kanakarajan, Kamal raj  and
      Kundumani, Bhuvana  and
      Abraham, Abhijith  and
      Sankarasubbu, Malaikannan",
    booktitle = "Proceedings of the 13th International Workshop on Health Text Mining and Information Analysis (LOUHI)",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, United Arab Emirates (Hybrid)",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.louhi-1.10",
    pages = "81--86",
    abstract = "Sentence embeddings in the form of fixed-size vectors that capture the information in the sentence as well as the context are critical components of Natural Language Processing systems. With transformer model based sentence encoders outperforming the other sentence embedding methods in the general domain, we explore the transformer based architectures to generate dense sentence embeddings in the biomedical domain. In this work, we present BioSimCSE, where we train sentence embeddings with domain specific transformer based models with biomedical texts. We assess our model{'}s performance with zero-shot and fine-tuned settings on Semantic Textual Similarity (STS) and Recognizing Question Entailment (RQE) tasks. Our BioSimCSE model using BioLinkBERT achieves state of the art (SOTA) performance on both tasks.",
}
```