README.md

---
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
language:
- pt
---

# mteb-pt/average_pt_nilc_wang2vec_skip_s1000

This is an adaptation of pre-trained Portuguese Wang2Vec Word Embeddings to a [sentence-transformers](https://www.SBERT.net) model.  

The original pre-trained word embeddings can be found at: [http://nilc.icmc.usp.br/nilc/index.php/repositorio-de-word-embeddings-do-nilc](http://nilc.icmc.usp.br/nilc/index.php/repositorio-de-word-embeddings-do-nilc).  

This model maps sentences & paragraphs to a 1000 dimensional dense vector space and can be used for tasks like clustering or semantic search.

## 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('mteb-pt/average_pt_nilc_wang2vec_skip_s1000')
embeddings = model.encode(sentences)
print(embeddings)
```

## Evaluation Results

For an automated evaluation of this model, see the *Portuguese MTEB Leaderboard*: [mteb-pt/leaderboard](https://huggingface.co/spaces/mteb-pt/leaderboard)

## Full Model Architecture
```
SentenceTransformer(
  (0): WordEmbeddings(
    (emb_layer): Embedding(929607, 1000)
  )
  (1): Pooling({'word_embedding_dimension': 1000, '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})
)
```

## Citing & Authors

```bibtex
@inproceedings{hartmann2017portuguese,
    title  = {Portuguese Word Embeddings: Evaluating on Word Analogies and Natural Language Tasks},
    author = {Hartmann, Nathan S and
              Fonseca, Erick R and
              Shulby, Christopher D and
              Treviso, Marcos V and
              Rodrigues, J{'{e}}ssica S and
              Alu{'{\i}}sio, Sandra Maria},
    year = {2017},
    publisher = {SBC},
    booktitle = {Brazilian Symposium in Information and Human Language Technology - STIL},
    url = {https://sol.sbc.org.br/index.php/stil/article/view/4008}
}
```