77 lines
2.1 KiB
Markdown
77 lines
2.1 KiB
Markdown
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---
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license: cc-by-nc-4.0
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pipeline_tag: sentence-similarity
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tags:
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- sentence-transformers
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- feature-extraction
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- sentence-similarity
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- transformers
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- generated_from_trainer
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datasets:
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- squad
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---
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# bert-base-uncased-embedding-step-scheduler
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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.
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## Usage (Sentence-Transformers)
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Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
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```
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pip install -U sentence-transformers
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```
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Then you can use the model like this:
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```python
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from sentence_transformers import SentenceTransformer
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sentences = ["This is an example sentence", "Each sentence is converted"]
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model = SentenceTransformer('LLukas22/bert-base-uncased-embedding-step-scheduler')
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embeddings = model.encode(sentences)
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print(embeddings)
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```
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## Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 2E-05
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- per device batch size: 26
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- effective batch size: 26
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- seed: 42
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- optimizer: AdamW with betas (0.9,0.999) and eps 1E-08
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- weight decay: 1E-02
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- D-Adaptation: False
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- Warmup: False
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- number of epochs: 3
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- mixed_precision_training: bf16
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## Training results
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| Epoch | Train Loss | Validation Loss |
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| ----- | ---------- | --------------- |
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| 0 | 0.0647 | 0.0876 |
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| 1 | 0.0328 | 0.0826 |
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| 2 | 0.0298 | 0.082 |
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## Evaluation results
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| Epoch | top_1 | top_3 | top_5 | top_10 | top_25 |
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| ----- | ----- | ----- | ----- | ----- | ----- |
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| 0 | 0.586 | 0.778 | 0.843 | 0.911 | 0.968 |
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| 1 | 0.596 | 0.792 | 0.853 | 0.917 | 0.969 |
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| 2 | 0.595 | 0.794 | 0.854 | 0.917 | 0.97 |
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## Framework versions
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- Transformers: 4.25.1
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- PyTorch: 1.13.1
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- PyTorch Lightning: 1.8.6
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- Datasets: 2.7.1
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- Tokenizers: 0.12.1
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- Sentence Transformers: 2.2.2
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## Additional Information
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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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