
# 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 |
| 12 | 0.011 | 0.0414 |

## 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 |
| 12 | 0.324 | 0.427 | 0.464 | 0.501 | 0.536 |

## 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/Retrieval-Augmented-QA).
{"language": ["en", "de"], "license": "apache-2.0", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers", "generated_from_trainer"], "datasets": ["squad", "newsqa", "LLukas22/cqadupstack", "LLukas22/fiqa", "LLukas22/scidocs", "deepset/germanquad", "LLukas22/nq"], "pipeline_tag": "sentence-similarity"}
sentence-transformers pytorch tensorboard xlm-roberta feature-extraction sentence-similarity transformers generated_from_trainer en de dataset:squad dataset:newsqa dataset:LLukas22/cqadupstack dataset:LLukas22/fiqa dataset:LLukas22/scidocs dataset:deepset/germanquad dataset:LLukas22/nq license:apache-2.0 endpoints_compatible region:us
TAGS
#sentence-transformers #pytorch #tensorboard #xlm-roberta #feature-extraction #sentence-similarity #transformers #generated_from_trainer #en #de #dataset-squad #dataset-newsqa #dataset-LLukas22/cqadupstack #dataset-LLukas22/fiqa #dataset-LLukas22/scidocs #dataset-deepset/germanquad #dataset-LLukas22/nq #license-apache-2.0 #endpoints_compatible #region-us 

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


This model is a fine-tuned version of paraphrase-multilingual-mpnet-base-v2 on the following datasets: squad, newsqa, LLukas22/cqadupstack, LLukas22/fiqa, LLukas22/scidocs, deepset/germanquad, LLukas22/nq.


Usage (Sentence-Transformers)
-----------------------------


Using this model becomes easy when you have sentence-transformers installed:


Then you can use the model like this:


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: 0, Train Loss: 0.085, Validation Loss: 0.0625
Epoch: 1, Train Loss: 0.0598, Validation Loss: 0.0554
Epoch: 2, Train Loss: 0.0484, Validation Loss: 0.0518
Epoch: 3, Train Loss: 0.0405, Validation Loss: 0.0485
Epoch: 4, Train Loss: 0.0341, Validation Loss: 0.0463
Epoch: 5, Train Loss: 0.0287, Validation Loss: 0.0454
Epoch: 6, Train Loss: 0.0243, Validation Loss: 0.0445
Epoch: 7, Train Loss: 0.0207, Validation Loss: 0.0426
Epoch: 8, Train Loss: 0.0177, Validation Loss: 0.0424
Epoch: 9, Train Loss: 0.0153, Validation Loss: 0.0421
Epoch: 10, Train Loss: 0.0134, Validation Loss: 0.0417
Epoch: 11, Train Loss: 0.012, Validation Loss: 0.0411
Epoch: 12, Train Loss: 0.011, Validation Loss: 0.0414


Evaluation results
------------------



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.



TAGS
#sentence-transformers #pytorch #tensorboard #xlm-roberta #feature-extraction #sentence-similarity #transformers #generated_from_trainer #en #de #dataset-squad #dataset-newsqa #dataset-LLukas22/cqadupstack #dataset-LLukas22/fiqa #dataset-LLukas22/scidocs #dataset-deepset/germanquad #dataset-LLukas22/nq #license-apache-2.0 #endpoints_compatible #region-us 

TAGS
#sentence-transformers #pytorch #tensorboard #xlm-roberta #feature-extraction #sentence-similarity #transformers #generated_from_trainer #en #de #dataset-squad #dataset-newsqa #dataset-LLukas22/cqadupstack #dataset-LLukas22/fiqa #dataset-LLukas22/scidocs #dataset-deepset/germanquad #dataset-LLukas22/nq #license-apache-2.0 #endpoints_compatible #region-us 
