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--- |
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license: apache-2.0 |
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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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- newsqa |
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- LLukas22/cqadupstack |
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- LLukas22/fiqa |
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- LLukas22/scidocs |
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- deepset/germanquad |
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- LLukas22/nq |
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language: |
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- en |
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- de |
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--- |
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# all-MiniLM-L12-v2-embedding-all |
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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). |
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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/all-MiniLM-L12-v2-embedding-all') |
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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: 1E+00 |
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- per device batch size: 60 |
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- effective batch size: 180 |
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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: 2E-02 |
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- D-Adaptation: True |
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- Warmup: True |
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- number of epochs: 20 |
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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 | 0.0708 | 0.0619 | |
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| 1 | 0.0609 | 0.0567 | |
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| 2 | 0.0531 | 0.0542 | |
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| 3 | 0.0475 | 0.0528 | |
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| 4 | 0.0428 | 0.0521 | |
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| 5 | 0.0389 | 0.0513 | |
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| 6 | 0.0352 | 0.0508 | |
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| 7 | 0.0322 | 0.0494 | |
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| 8 | 0.0289 | 0.0485 | |
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| 9 | 0.0264 | 0.0483 | |
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| 10 | 0.0242 | 0.0466 | |
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| 11 | 0.0221 | 0.0459 | |
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| 12 | 0.0204 | 0.0469 | |
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| 13 | 0.0189 | 0.0459 | |
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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 | 0.507 | 0.665 | 0.721 | 0.784 | 0.847 | |
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| 1 | 0.501 | 0.661 | 0.719 | 0.783 | 0.846 | |
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| 2 | 0.508 | 0.669 | 0.726 | 0.789 | 0.851 | |
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| 3 | 0.507 | 0.665 | 0.722 | 0.785 | 0.85 | |
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| 4 | 0.506 | 0.667 | 0.724 | 0.788 | 0.851 | |
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| 5 | 0.511 | 0.673 | 0.731 | 0.795 | 0.857 | |
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| 6 | 0.51 | 0.674 | 0.732 | 0.794 | 0.856 | |
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| 7 | 0.512 | 0.674 | 0.732 | 0.796 | 0.859 | |
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| 8 | 0.515 | 0.678 | 0.736 | 0.799 | 0.861 | |
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| 9 | 0.514 | 0.679 | 0.737 | 0.8 | 0.862 | |
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| 10 | 0.52 | 0.683 | 0.741 | 0.803 | 0.864 | |
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| 11 | 0.522 | 0.686 | 0.744 | 0.806 | 0.866 | |
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| 12 | 0.519 | 0.683 | 0.741 | 0.804 | 0.864 | |
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| 13 | 0.522 | 0.685 | 0.743 | 0.806 | 0.865 | |
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## Framework versions |
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- Transformers: 4.25.1 |
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- PyTorch: 2.0.0.dev20230210+cu118 |
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- PyTorch Lightning: 1.8.6 |
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- Datasets: 2.7.1 |
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- Tokenizers: 0.13.1 |
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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/Retrieval-Augmented-QA). |
