Cataloged from PORTULAN/serafim-900m-portuguese-pt-sentence-encoder-ir
This is a sentence-transformers model: It maps sentences & paragraphs to a 1536 dimensional dense vector space and can be used for tasks like clustering or semantic search.
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('PORTULAN/serafim-900m-portuguese-pt-sentence-encoder-ir')
embeddings = model.encode(sentences)
print(embeddings)
Without sentence-transformers, 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.
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# 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('PORTULAN/serafim-900m-portuguese-pt-sentence-encoder-ir')
model = AutoModel.from_pretrained('PORTULAN/serafim-900m-portuguese-pt-sentence-encoder-ir')
# 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, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
For an automated evaluation of this model, see the Sentence Embeddings Benchmark: https://seb.sbert.net
The model was trained with the parameters:
DataLoader:
sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader of length 1989040 with parameters:
{'batch_size': 40}
Loss:
sentence_transformers.losses.GISTEmbedLoss.GISTEmbedLoss with parameters:
{'guide': SentenceTransformer(
(0): Transformer({'max_seq_length': 128, '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, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
), 'temperature': 0.01}
Parameters of the fit()-Method:
{
"epochs": 1,
"evaluation_steps": 19891,
"evaluator": "sentence_transformers.evaluation.InformationRetrievalEvaluator.InformationRetrievalEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 1e-06
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 1989040,
"warmup_steps": 198904,
"weight_decay": 0.01
}
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: DebertaV2Model
(1): Pooling({'word_embedding_dimension': 1536, '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})
)
The article has been presented at EPIA 2024 conference and published by Springer:
@InProceedings{epia2024serafim,
title={Open Sentence Embeddings for Portuguese with the Serafim PT* encoders family},
author={LuÃs Gomes and António Branco and João Silva and João Rodrigues and Rodrigo Santos},
editor={Manuel Filipe Santos and José Machado and Paulo Novais and Paulo Cortez and Pedro Miguel Moreira},
booktitle={Progress in Artificial Intelligence},
doi={doi.org/10.1007/978-3-031-73503-5_22},
year={2024},
publisher={Springer Nature Switzerland},
address={Cham},
pages={267--279},
isbn={978-3-031-73503-5}
}
Before publication by Springer, the pre-print was available at arXiv:
@misc{gomes2024opensentenceembeddingsportuguese,
title={Open Sentence Embeddings for Portuguese with the Serafim PT* encoders family},
author={LuÃs Gomes and António Branco and João Silva and João Rodrigues and Rodrigo Santos},
year={2024},
eprint={2407.19527},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2407.19527},
}