---
base_model:
- sentence-transformers/paraphrase-multilingual-mpnet-base-v2
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
- 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
datasets:
- victormuryn/wsd-training-dataset
---
# use-natural-pt
This model is a fine-tuned version of [paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2), trained on the Ukrainian text corpus [UberText 2.0](https://lang.org.ua/en/ubertext/) on the raw corpus with pool targets, with no data augmentation applied. It is part of the [Ukrainian Sentence Embeddings](https://huggingface.co/collections/victormuryn/ukrainian-sentence-embeddings-use) collection, which explores the effect of different training strategies on sentence embedding quality for Ukrainian.

## Model Description
The model was fine-tuned using a contrastive objective on UberText 2.0.

## Collection Overview
| Model                   | Description                                           |
| ----------------------- | ----------------------------------------------------- |
| use-natural-no-pt        | Raw UberText 2.0, no augmentation, no pool targets     |
| **use-natural-pt**        | Raw UberText 2.0, no augmentation, pool targets        |
| use-generated-no-pt      | Generated augmentation, no pool targets                |
| use-generated-pt         | Generated augmentation, pool targets                   |
| use-translation-no-pt    | Back-translation augmentation, no pool targets         |
| use-translation-pt       | Back-translation augmentation, pool targets            |
| use-mask-no-pt           | Masking augmentation, no pool targets                  |
| use-mask-pt              | Masking augmentation, pool targets                     |
| use-dropout-no-pt        | Dropout augmentation, no pool targets                  |
| use-dropout-pt           | Dropout augmentation, pool targets                     |
| use-token-shuffle-no-pt  | Token-shuffling augmentation, no pool targets          |
| use-token-shuffle-pt     | Token-shuffling augmentation, pool targets             |
| use-combined-no-pt       | Combined augmentation strategies, no pool targets      |
| use-combined-pt          | Combined augmentation strategies, pool targets         |
| use-stochastic-no-pt     | Markov-based stochastic augmentation, no pool targets  |
| use-stochastic-pt        | Markov-based stochastic augmentation, pool targets     |

## Usage
```python
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("victormuryn/use-natural-pt")
sentences = [
    "Проводжає сина мати захищати рідний край",
    "Хоч би малесеньку хатину він мріяв мати над Дніпром",
]
embeddings = model.encode(sentences)
print(embeddings.shape)
```

## Training Details
- **Base model:** paraphrase-multilingual-mpnet-base-v2  
- **Training corpus:** UberText 2.0  
- **Augmentation:** None  
- **Pool targets:** Yes  

## Citation
To be added

## License
Apache 2.0