
# nthakur/contriever-base-msmarco

This is a port of the [Contriever Model](https://huggingface.co/facebook/contriever) 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('nishimoto/contriever-sentencetransformer')
embeddings = model.encode(sentences)
print(embeddings)
```


## Full Model Architecture
```
SentenceTransformer(
  (0): Transformer({'max_seq_length': 509, '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

Have a look at: [Contriever Model](https://github.com/facebookresearch/contriever).

{"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"}
sentence-transformers pytorch bert feature-extraction sentence-similarity transformers endpoints_compatible region:us