
# all-MiniLM-L12-v2-embedding-all

This model is a fine-tuned version of [all-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L12-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/all-MiniLM-L12-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: 60
- effective batch size: 180
- 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: 20
- mixed_precision_training: bf16

## Training results
| Epoch | Train Loss | Validation Loss |
| ----- | ---------- | --------------- |
| 0 | 0.0708 | 0.0619 |
| 1 | 0.0609 | 0.0567 |
| 2 | 0.0531 | 0.0542 |
| 3 | 0.0475 | 0.0528 |
| 4 | 0.0428 | 0.0521 |
| 5 | 0.0389 | 0.0513 |
| 6 | 0.0352 | 0.0508 |
| 7 | 0.0322 | 0.0494 |
| 8 | 0.0289 | 0.0485 |
| 9 | 0.0264 | 0.0483 |
| 10 | 0.0242 | 0.0466 |
| 11 | 0.0221 | 0.0459 |
| 12 | 0.0204 | 0.0469 |
| 13 | 0.0189 | 0.0459 |

## Evaluation results
| Epoch | top_1 | top_3 | top_5 | top_10 | top_25 |
| ----- | ----- | ----- | ----- | ----- | ----- |
| 0 | 0.507 | 0.665 | 0.721 | 0.784 | 0.847 |
| 1 | 0.501 | 0.661 | 0.719 | 0.783 | 0.846 |
| 2 | 0.508 | 0.669 | 0.726 | 0.789 | 0.851 |
| 3 | 0.507 | 0.665 | 0.722 | 0.785 | 0.85 |
| 4 | 0.506 | 0.667 | 0.724 | 0.788 | 0.851 |
| 5 | 0.511 | 0.673 | 0.731 | 0.795 | 0.857 |
| 6 | 0.51 | 0.674 | 0.732 | 0.794 | 0.856 |
| 7 | 0.512 | 0.674 | 0.732 | 0.796 | 0.859 |
| 8 | 0.515 | 0.678 | 0.736 | 0.799 | 0.861 |
| 9 | 0.514 | 0.679 | 0.737 | 0.8 | 0.862 |
| 10 | 0.52 | 0.683 | 0.741 | 0.803 | 0.864 |
| 11 | 0.522 | 0.686 | 0.744 | 0.806 | 0.866 |
| 12 | 0.519 | 0.683 | 0.741 | 0.804 | 0.864 |
| 13 | 0.522 | 0.685 | 0.743 | 0.806 | 0.865 |

## 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 bert 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 #bert #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 

all-MiniLM-L12-v2-embedding-all
===============================


This model is a fine-tuned version of all-MiniLM-L12-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: 60
* effective batch size: 180
* 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: 20
* mixed\_precision\_training: bf16


Training results
----------------


Epoch: 0, Train Loss: 0.0708, Validation Loss: 0.0619
Epoch: 1, Train Loss: 0.0609, Validation Loss: 0.0567
Epoch: 2, Train Loss: 0.0531, Validation Loss: 0.0542
Epoch: 3, Train Loss: 0.0475, Validation Loss: 0.0528
Epoch: 4, Train Loss: 0.0428, Validation Loss: 0.0521
Epoch: 5, Train Loss: 0.0389, Validation Loss: 0.0513
Epoch: 6, Train Loss: 0.0352, Validation Loss: 0.0508
Epoch: 7, Train Loss: 0.0322, Validation Loss: 0.0494
Epoch: 8, Train Loss: 0.0289, Validation Loss: 0.0485
Epoch: 9, Train Loss: 0.0264, Validation Loss: 0.0483
Epoch: 10, Train Loss: 0.0242, Validation Loss: 0.0466
Epoch: 11, Train Loss: 0.0221, Validation Loss: 0.0459
Epoch: 12, Train Loss: 0.0204, Validation Loss: 0.0469
Epoch: 13, Train Loss: 0.0189, Validation Loss: 0.0459


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 #bert #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 #bert #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 
