README.md

---
base_model: BEE-spoke-data/mega-encoder-small-16k-v1
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
- 16k
- efficient attention
license: artistic-2.0
datasets:
- pszemraj/synthetic-text-similarity
language:
- en
---

# mega-small-embed-synthSTS-16384: v1

<img src="https://cdn-uploads.huggingface.co/production/uploads/60bccec062080d33f875cd0c/38Yc1IgU4bH92Wyb43J2I.png" alt="image/png" style="max-width: 75%;">

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.

- this model's primary use case is meant to be long-document similarity, i.e. computing embeddings of long documents and comparing those.
  - check out the training dataset `pszemraj/synthetic-text-similarity` for details
- pretrained & finetuned at context length 16384
- This model is a "v1" and we may make improved versions in the future. Or, we may not.


## Usage


Regardless of method, you will need to have this specific fork of transformers installed unless you want to get [errors related to padding](https://github.com/UKPLab/sentence-transformers/issues/2540):

```sh
pip install -U git+https://github.com/pszemraj/transformers.git@mega-upgrades --force-reinstall --no-deps
```

### 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/mega-small-embed-synthSTS-16384-v1')
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/mega-small-embed-synthSTS-16384-v1')
model = AutoModel.from_pretrained('BEE-spoke-data/mega-small-embed-synthSTS-16384-v1')

# 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}
  ```

**arch**

```
SentenceTransformer(
  (0): Transformer({'max_seq_length': 16384, 'do_lower_case': False}) with Transformer model: MegaModel 
  (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})
)
```