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

# mteb-pt/average_fasttext_cc.pt.300

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

The original pre-trained word embeddings can be found at: [https://fasttext.cc/docs/en/crawl-vectors.html](https://fasttext.cc/docs/en/crawl-vectors.html).  

This model maps sentences & paragraphs to a 300 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_fasttext_cc.pt.300')
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(2000001, 300)
  )
  (1): Pooling({'word_embedding_dimension': 300, '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{grave2018learning,
    title={Learning Word Vectors for 157 Languages},
    author={Grave, Edouard and Bojanowski, Piotr and Gupta, Prakhar and Joulin, Armand and Mikolov, Tomas},
    booktitle={Proceedings of the International Conference on Language Resources and Evaluation (LREC 2018)},
    year={2018}
}
```