sentence-transformers pytorch bert feature-extraction sentence-similarity transformers dataset:wikipedia dataset:bookcorpus dataset:ms_marco dataset:BeIR/fiqa dataset:BeIR/trec-covid dataset:BeIR/scifact dataset:BeIR/nfcorpus dataset:BeIR/nq dataset:BeIR/hotpotqa dataset:BeIR/arguana dataset:BeIR/webis-touche2020 dataset:BeIR/quora dataset:BeIR/dbpedia-entity dataset:BeIR/scidocs dataset:BeIR/fever dataset:BeIR/climate-fever autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
datasets:
- wikipedia
- bookcorpus
- ms_marco
- BeIR/fiqa
- BeIR/trec-covid
- BeIR/scifact
- BeIR/nfcorpus
- BeIR/nq
- BeIR/hotpotqa
- BeIR/arguana
- BeIR/webis-touche2020
- BeIR/quora
- BeIR/dbpedia-entity
- BeIR/scidocs
- BeIR/fever
- BeIR/climate-fever
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---

# nthakur/RetroMAE_BEIR

This is a port of the [RetroMAE_BEIR](https://huggingface.co/Shitao/RetroMAE_BEIR) Model to [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('nthakur/RetroMAE_BEIR')
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


def cls_pooling(model_output, attention_mask):
    return model_output[0][:,0]


# 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('nthakur/RetroMAE_BEIR')
model = AutoModel.from_pretrained('nthakur/RetroMAE_BEIR')

# 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, cls pooling.
sentence_embeddings = cls_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=nthakur/RetroMAE_BEIR)



## Full Model Architecture
```
SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```

## Citing & Authors
Have a look at [RetroMAE](https://github.com/staoxiao/RetroMAE/).
<!--- Describe where people can find more information -->