mteb-pt/average_fasttext_wiki.pt.align.300
This is an adaptation of pre-trained Portuguese fastText Word Embeddings to a sentence-transformers model.
The original pre-trained word embeddings can be found at: https://fasttext.cc/docs/en/aligned-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 installed:
pip install -U sentence-transformers
Then you can use the model like this:
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('mteb-pt/average_fasttext_wiki.pt.align.300')
embeddings = model.encode(sentences)
print(embeddings)
Evaluation Results
For an automated evaluation of this model, see the Portuguese MTEB Leaderboard: mteb-pt/leaderboard
Full Model Architecture
SentenceTransformer(
(0): WordEmbeddings(
(emb_layer): Embedding(592109, 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
@InProceedings{joulin2018loss,
title={Loss in Translation: Learning Bilingual Word Mapping with a Retrieval Criterion},
author={Joulin, Armand and Bojanowski, Piotr and Mikolov, Tomas and J'egou, Herv'e and Grave, Edouard},
year={2018},
booktitle={Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing},
}
@article{bojanowski2017enriching,
title={Enriching Word Vectors with Subword Information},
author={Bojanowski, Piotr and Grave, Edouard and Joulin, Armand and Mikolov, Tomas},
journal={Transactions of the Association for Computational Linguistics},
volume={5},
year={2017},
issn={2307-387X},
pages={135--146}
}
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