library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
- semantic-search
sge-hlm
Sentence embeddings for English and Ancient Greek
The HLM model architecture is based on Heidelberg-Boston @ SIGTYP 2024 Shared Task: Enhancing Low-Resource Language Analysis With Character-Aware Hierarchical Transformers but uses a simpler architecture with rotary embeddings (see the implementation in the HLM folder) instead of using DeBERTa as a base architecture. This architecture produces superior results compared to the vanilla BERT architecture for low-resource languages like Ancient Greek. It is trained to produce sentence embeddings using the method described in Sentence Embedding Models for Ancient Greek Using Multiligual Knowledge Distillation.
This model was distilled from BAAI/bge-base-en-v1.5 for embedding English and Ancient Greek text.
Usage (Sentence-Transformers)
Using sentence-transformers:
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('kevinkrahn/shlm-grc-en')
embeddings = model.encode(sentences)
print(embeddings)
Usage (HuggingFace Transformers)
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
def cls_pooling(model_output, attention_mask):
return model_output[0][:,0]
# 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('kevinkrahn/shlm-grc-en')
model = AutoModel.from_pretrained('kevinkrahn/shlm-grc-en')
# 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, cls pooling.
sentence_embeddings = cls_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
Citing & Authors
@inproceedings{riemenschneider-krahn-2024-heidelberg,
title = "Heidelberg-Boston @ {SIGTYP} 2024 Shared Task: Enhancing Low-Resource Language Analysis With Character-Aware Hierarchical Transformers",
author = "Riemenschneider, Frederick and
Krahn, Kevin",
editor = "Hahn, Michael and
Sorokin, Alexey and
Kumar, Ritesh and
Shcherbakov, Andreas and
Otmakhova, Yulia and
Yang, Jinrui and
Serikov, Oleg and
Rani, Priya and
Ponti, Edoardo M. and
Murado{\u{g}}lu, Saliha and
Gao, Rena and
Cotterell, Ryan and
Vylomova, Ekaterina",
booktitle = "Proceedings of the 6th Workshop on Research in Computational Linguistic Typology and Multilingual NLP",
month = mar,
year = "2024",
address = "St. Julian's, Malta",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.sigtyp-1.16",
pages = "131--141",
}
@inproceedings{krahn-etal-2023-sentence,
title = "Sentence Embedding Models for {A}ncient {G}reek Using Multilingual Knowledge Distillation",
author = "Krahn, Kevin and
Tate, Derrick and
Lamicela, Andrew C.",
editor = "Anderson, Adam and
Gordin, Shai and
Li, Bin and
Liu, Yudong and
Passarotti, Marco C.",
booktitle = "Proceedings of the Ancient Language Processing Workshop",
month = sep,
year = "2023",
address = "Varna, Bulgaria",
publisher = "INCOMA Ltd., Shoumen, Bulgaria",
url = "https://aclanthology.org/2023.alp-1.2",
pages = "13--22",
}