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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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- 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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--- |
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# paraphrase-multilingual-mpnet-base-v2-embedding-all |
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This model is a fine-tuned version of [paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-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/paraphrase-multilingual-mpnet-base-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: 40 |
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- effective batch size: 120 |
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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: 15 |
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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 | 0.085 | 0.0625 | |
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| 1 | 0.0598 | 0.0554 | |
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| 2 | 0.0484 | 0.0518 | |
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| 3 | 0.0405 | 0.0485 | |
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| 4 | 0.0341 | 0.0463 | |
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| 5 | 0.0287 | 0.0454 | |
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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 | 0.261 | 0.351 | 0.384 | 0.422 | 0.459 | |
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| 1 | 0.272 | 0.365 | 0.4 | 0.439 | 0.477 | |
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| 2 | 0.276 | 0.37 | 0.404 | 0.443 | 0.481 | |
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| 3 | 0.292 | 0.391 | 0.426 | 0.465 | 0.503 | |
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| 4 | 0.295 | 0.395 | 0.431 | 0.47 | 0.51 | |
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| 5 | 0.299 | 0.4 | 0.437 | 0.476 | 0.514 | |
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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/Master). |
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