README.md

---
language:
- multilingual
- ar
- bg
- ca
- cs
- da
- de
- el
- en
- es
- et
- fa
- fi
- fr
- gl
- gu
- he
- hi
- hr
- hu
- hy
- id
- it
- ja
- ka
- ko
- ku
- lt
- lv
- mk
- mn
- mr
- ms
- my
- nb
- nl
- pl
- pt
- ro
- ru
- sk
- sl
- sq
- sr
- sv
- th
- tr
- uk
- ur
- vi
license: apache-2.0
library_name: sentence-transformers
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
language_bcp47:
- fr-ca
- pt-br
- zh-cn
- zh-tw
pipeline_tag: sentence-similarity
---

# lang-uk/ukr-paraphrase-multilingual-mpnet-base

This is a [sentence-transformers](https://www.SBERT.net) model fine-tuned for Ukrainian language: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.

The original model used for fine-tuning is `sentence-transformers/paraphrase-multilingual-mpnet-base-v2`. See our paper [Contextual Embeddings for Ukrainian: A Large Language Model Approach to Word Sense Disambiguation](https://aclanthology.org/2023.unlp-1.2/) for details.


## Usage (Sentence-Transformers)

Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:

```
pip install -U sentence-transformers
```

Then you can use the model like this:

```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]

model = SentenceTransformer('lang-uk/ukr-paraphrase-multilingual-mpnet-base')
embeddings = model.encode(sentences)
print(embeddings)
```



## Usage (HuggingFace Transformers)
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.

```python
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('lang-uk/ukr-paraphrase-multilingual-mpnet-base')
model = AutoModel.from_pretrained('lang-uk/ukr-paraphrase-multilingual-mpnet-base')

# 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, average pooling
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])

print("Sentence embeddings:")
print(sentence_embeddings)
```



## Citing & Authors


If you find this model helpful, feel free to cite our publication [Contextual Embeddings for {U}krainian: A Large Language Model Approach to Word Sense Disambiguation](https://aclanthology.org/2023.unlp-1.2/):
```bibtex 
@inproceedings{laba-etal-2023-contextual,
 title = "Contextual Embeddings for {U}krainian: A Large Language Model Approach to Word Sense Disambiguation",
 author = "Laba, Yurii and
 Mudryi, Volodymyr and
 Chaplynskyi, Dmytro and
 Romanyshyn, Mariana and
 Dobosevych, Oles",
 editor = "Romanyshyn, Mariana",
 booktitle = "Proceedings of the Second Ukrainian Natural Language Processing Workshop (UNLP)",
 month = may,
 year = "2023",
 address = "Dubrovnik, Croatia",
 publisher = "Association for Computational Linguistics",
 url = "https://aclanthology.org/2023.unlp-1.2",
 doi = "10.18653/v1/2023.unlp-1.2",
 pages = "11--19",
 abstract = "This research proposes a novel approach to the Word Sense Disambiguation (WSD) task in the Ukrainian language based on supervised fine-tuning of a pre-trained Large Language Model (LLM) on the dataset generated in an unsupervised way to obtain better contextual embeddings for words with multiple senses. The paper presents a method for generating a new dataset for WSD evaluation in the Ukrainian language based on the SUM dictionary. We developed a comprehensive framework that facilitates the generation of WSD evaluation datasets, enables the use of different prediction strategies, LLMs, and pooling strategies, and generates multiple performance reports. Our approach shows 77,9{\%} accuracy for lexical meaning prediction for homonyms.",
}
```

Copyright: Yurii Laba, Volodymyr Mudryi, Dmytro Chaplynskyi, Mariana Romanyshyn, Oles Dobosevych, [Ukrainian Catholic University](https://ucu.edu.ua/en/), [lang-uk project](https://lang.org.ua/en/), 2023

An original model used for fine-tuning was trained by [sentence-transformers](https://www.sbert.net/).