---
base_model: BEE-spoke-data/bert-plus-L8-v1.0-allNLI_matryoshka
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
- 4k
- '4096'
- document embedding
- synthetic data
license: apache-2.0
datasets:
- pszemraj/synthetic-text-similarity
language:
- en
---

# BEE-spoke-data/bert-plus-L8-v1.0-syntheticSTS-4k

<a href="https://colab.research.google.com/gist/pszemraj/492e96baa289ba2f8326369153f3fd34/inference_bert_synthsts.ipynb">
  <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a>

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.

- Continued-tune of [BEE-spoke-data/bert-plus-L8-v1.0-allNLI_matryoshka](https://hf.co/BEE-spoke-data/bert-plus-L8-v1.0-allNLI_matryoshka) 
- ctx 4096 on synthetic text similarity dataset of `text1`, `text2`, `label`
- Matryoshka dims: [768, 512, 256, 128, 64]

<!--- 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('BEE-spoke-data/bert-plus-L8-v1.0-syntheticSTS-4k')
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('BEE-spoke-data/bert-plus-L8-v1.0-syntheticSTS-4k')
model = AutoModel.from_pretrained('BEE-spoke-data/bert-plus-L8-v1.0-syntheticSTS-4k')

# 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)
```

## Training

The model was trained with the parameters:

**Loss**:

`sentence_transformers.losses.MatryoshkaLoss.MatryoshkaLoss` with parameters:
  ```
  {'loss': 'CosineSimilarityLoss', 'matryoshka_dims': [768, 512, 256, 128, 64], 'matryoshka_weights': [1, 1, 1, 1, 1], 'n_dims_per_step': -1}
  ```

See more details at [the training run on wandb](https://wandb.ai/pszemraj/test-sbert-v3-api/runs/suv4fd2p)

---