SPhilBerta
The paper Exploring Language Models for Classical Philology is the first effort to systematically provide state-of-the-art language models for Classical Philology. Using PhilBERTa as a foundation, we introduce SPhilBERTa, a Sentence Transformer model to identify cross-lingual references between Latin and Ancient Greek texts. We employ the knowledge distillation method as proposed by Reimers and Gurevych (2020). Our paper can be found here.
Usage
Sentence-Transformers
When you have sentence-transformers installed, you can use the model like this:
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
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
#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('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# 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)
Contact
If you have any questions or problems, feel free to reach out.
Citation
@incollection{riemenschneiderfrank:2023b,
author = "Riemenschneider, Frederick and Frank, Anette",
title = "{Graecia capta ferum victorem cepit. Detecting Latin Allusions to Ancient Greek Literature}",
year = "2023",
url = "https://arxiv.org/abs/2308.12008",
note = "to appear",
publisher = "Association for Computational Linguistics",
booktitle = "Proceedings of the First Workshop on Ancient Language Processing",
address = "Varna, Bulgaria"
}