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--- |
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pipeline_tag: sentence-similarity |
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language: |
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- multilingual |
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- grc |
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- en |
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- la |
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license: apache-2.0 |
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tags: |
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- sentence-transformers |
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- sentence-similarity |
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--- |
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# SPhilBerta |
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The paper [Exploring Language Models for Classical Philology](https://aclanthology.org/2023.acl-long.846/) 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)](https://aclanthology.org/2020.emnlp-main.365/). Our paper can be found [here](https://arxiv.org/abs/2308.12008). |
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## Usage |
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### Sentence-Transformers |
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When you have [sentence-transformers](https://www.SBERT.net) installed, you can use the model like this: |
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```python |
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from sentence_transformers import SentenceTransformer |
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sentences = ["This is an example sentence", "Each sentence is converted"] |
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model = SentenceTransformer('{MODEL_NAME}') |
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embeddings = model.encode(sentences) |
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print(embeddings) |
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``` |
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### HuggingFace Transformers |
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Without [sentence-transformers](https://www.SBERT.net), 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. |
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```python |
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from transformers import AutoTokenizer, AutoModel |
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import torch |
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#Mean Pooling - Take attention mask into account for correct averaging |
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def mean_pooling(model_output, attention_mask): |
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token_embeddings = model_output[0] #First element of model_output contains all token embeddings |
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input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() |
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return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) |
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# Sentences we want sentence embeddings for |
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sentences = ['This is an example sentence', 'Each sentence is converted'] |
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# Load model from HuggingFace Hub |
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tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}') |
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model = AutoModel.from_pretrained('{MODEL_NAME}') |
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# Tokenize sentences |
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encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') |
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# Compute token embeddings |
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with torch.no_grad(): |
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model_output = model(**encoded_input) |
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# Perform pooling. In this case, mean pooling. |
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sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) |
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print("Sentence embeddings:") |
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print(sentence_embeddings) |
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``` |
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## Contact |
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If you have any questions or problems, feel free to [reach out](mailto:riemenschneider@cl.uni-heidelberg.de). |
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## Citation |
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```bibtex |
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@incollection{riemenschneiderfrank:2023b, |
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author = "Riemenschneider, Frederick and Frank, Anette", |
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title = "{Graecia capta ferum victorem cepit. Detecting Latin Allusions to Ancient Greek Literature}", |
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year = "2023", |
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url = "https://arxiv.org/abs/2308.12008", |
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note = "to appear", |
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publisher = "Association for Computational Linguistics", |
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booktitle = "Proceedings of the First Workshop on Ancient Language Processing", |
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address = "Varna, Bulgaria" |
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} |
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``` |
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