
# bert-base-uncased-embedding-step-scheduler

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the [squad](https://huggingface.co/datasets/squad) dataset.



## 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/bert-base-uncased-embedding-step-scheduler')
embeddings = model.encode(sentences)
print(embeddings)
```

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

- learning_rate: 2E-05
- per device batch size: 26
- effective batch size: 26
- seed: 42
- optimizer: AdamW with betas (0.9,0.999) and eps 1E-08
- weight decay: 1E-02
- D-Adaptation: False
- Warmup: False
- number of epochs: 3
- mixed_precision_training: bf16

## Training results
| Epoch | Train Loss | Validation Loss |
| ----- | ---------- | --------------- |
| 0 | 0.0647 | 0.0876 |
| 1 | 0.0328 | 0.0826 |
| 2 | 0.0298 | 0.082 |

## Evaluation results
| Epoch | top_1 | top_3 | top_5 | top_10 | top_25 |
| ----- | ----- | ----- | ----- | ----- | ----- |
| 0 | 0.586 | 0.778 | 0.843 | 0.911 | 0.968 |
| 1 | 0.596 | 0.792 | 0.853 | 0.917 | 0.969 |
| 2 | 0.595 | 0.794 | 0.854 | 0.917 | 0.97 |

## Framework versions
- Transformers: 4.25.1
- PyTorch: 1.13.1
- PyTorch Lightning: 1.8.6
- Datasets: 2.7.1
- Tokenizers: 0.12.1
- 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).

{"license": "cc-by-nc-4.0", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers", "generated_from_trainer"], "datasets": ["squad"], "pipeline_tag": "sentence-similarity"}
sentence-transformers pytorch tensorboard bert feature-extraction sentence-similarity transformers generated_from_trainer dataset:squad license:cc-by-nc-4.0 endpoints_compatible region:us
TAGS
#sentence-transformers #pytorch #tensorboard #bert #feature-extraction #sentence-similarity #transformers #generated_from_trainer #dataset-squad #license-cc-by-nc-4.0 #endpoints_compatible #region-us 

bert-base-uncased-embedding-step-scheduler
==========================================


This model is a fine-tuned version of bert-base-uncased on the squad dataset.


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: 2E-05
* per device batch size: 26
* effective batch size: 26
* seed: 42
* optimizer: AdamW with betas (0.9,0.999) and eps 1E-08
* weight decay: 1E-02
* D-Adaptation: False
* Warmup: False
* number of epochs: 3
* mixed\_precision\_training: bf16


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


Epoch: 0, Train Loss: 0.0647, Validation Loss: 0.0876
Epoch: 1, Train Loss: 0.0328, Validation Loss: 0.0826
Epoch: 2, Train Loss: 0.0298, Validation Loss: 0.082


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



Framework versions
------------------


* Transformers: 4.25.1
* PyTorch: 1.13.1
* PyTorch Lightning: 1.8.6
* Datasets: 2.7.1
* Tokenizers: 0.12.1
* 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 #dataset-squad #license-cc-by-nc-4.0 #endpoints_compatible #region-us 

TAGS
#sentence-transformers #pytorch #tensorboard #bert #feature-extraction #sentence-similarity #transformers #generated_from_trainer #dataset-squad #license-cc-by-nc-4.0 #endpoints_compatible #region-us 
