---
license: cc-by-nc-4.0
pipeline_tag: sentence-similarity
tags:
    - sentence-transformers
    - feature-extraction
    - sentence-similarity
    - transformers
    - generated_from_trainer
datasets:
    - squad
    - newsqa
    - LLukas22/cqadupstack
    - LLukas22/fiqa
    - LLukas22/scidocs
    - deepset/germanquad
    - LLukas22/nq
---

# paraphrase-multilingual-mpnet-base-v2-embedding-all

This model is a fine-tuned version of [paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2) on the following datasets: [squad](https://huggingface.co/datasets/squad), [newsqa](https://huggingface.co/datasets/newsqa), [LLukas22/cqadupstack](https://huggingface.co/datasets/LLukas22/cqadupstack), [LLukas22/fiqa](https://huggingface.co/datasets/LLukas22/fiqa), [LLukas22/scidocs](https://huggingface.co/datasets/LLukas22/scidocs), [deepset/germanquad](https://huggingface.co/datasets/deepset/germanquad), [LLukas22/nq](https://huggingface.co/datasets/LLukas22/nq).



## 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('LLukas22/paraphrase-multilingual-mpnet-base-v2-embedding-all')
embeddings = model.encode(sentences)
print(embeddings)
```

## Training hyperparameters
The following hyperparameters were used during training:

- learning_rate: 1E+00
- per device batch size: 40
- effective batch size: 120
- seed: 42
- optimizer: AdamW with betas (0.9,0.999) and eps 1E-08
- weight decay: 2E-02
- D-Adaptation: True
- Warmup: True
- number of epochs: 15
- mixed_precision_training: bf16

## Training results
| Epoch | Train Loss | Validation Loss |
| ----- | ---------- | --------------- |
| 0 | 0.085 | 0.0625 |
| 1 | 0.0598 | 0.0554 |
| 2 | 0.0484 | 0.0518 |
| 3 | 0.0405 | 0.0485 |
| 4 | 0.0341 | 0.0463 |
| 5 | 0.0287 | 0.0454 |

## Evaluation results
| Epoch | top_1 | top_3 | top_5 | top_10 | top_25 |
| ----- | ----- | ----- | ----- | ----- | ----- |
| 0 | 0.261 | 0.351 | 0.384 | 0.422 | 0.459 |
| 1 | 0.272 | 0.365 | 0.4 | 0.439 | 0.477 |
| 2 | 0.276 | 0.37 | 0.404 | 0.443 | 0.481 |
| 3 | 0.292 | 0.391 | 0.426 | 0.465 | 0.503 |
| 4 | 0.295 | 0.395 | 0.431 | 0.47 | 0.51 |
| 5 | 0.299 | 0.4 | 0.437 | 0.476 | 0.514 |

## Framework versions
- Transformers: 4.25.1
- PyTorch: 2.0.0.dev20230210+cu118
- PyTorch Lightning: 1.8.6
- Datasets: 2.7.1
- Tokenizers: 0.13.1
- Sentence Transformers: 2.2.2

## Additional Information
This model was trained as part of my Master's Thesis **'Evaluation of transformer based language models for use in service information systems'**. The source code is available on [Github](https://github.com/LLukas22/Master).