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---
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 |
| 6 | 0.0243 | 0.0445 |
| 7 | 0.0207 | 0.0426 |
| 8 | 0.0177 | 0.0424 |
| 9 | 0.0153 | 0.0421 |
| 10 | 0.0134 | 0.0417 |
| 11 | 0.012 | 0.0411 |
## 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 |
| 6 | 0.306 | 0.404 | 0.44 | 0.478 | 0.515 |
| 7 | 0.309 | 0.41 | 0.445 | 0.485 | 0.521 |
| 8 | 0.31 | 0.411 | 0.448 | 0.487 | 0.524 |
| 9 | 0.315 | 0.417 | 0.454 | 0.493 | 0.529 |
| 10 | 0.319 | 0.42 | 0.457 | 0.495 | 0.53 |
| 11 | 0.323 | 0.424 | 0.46 | 0.497 | 0.531 |
## 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).
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