bowphs / SPhilBerta

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"
}