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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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