| | --- |
| | base_model: microsoft/mpnet-base |
| | language: |
| | - en |
| | library_name: sentence-transformers |
| | pipeline_tag: sentence-similarity |
| | tags: |
| | - sentence-transformers |
| | - sentence-similarity |
| | - feature-extraction |
| | - generated_from_trainer |
| | - dataset_size:5130135 |
| | - loss:MultipleNegativesSymmetricRankingLoss |
| | - loss:CoSENTLoss |
| | - dataset_size:8233 |
| | widget: |
| | - source_sentence: This is a sample source sentence. |
| | target_sentence: This is a sample target sentence. |
| | license: apache-2.0 |
| | --- |
| | |
| |
|
| | # SentenceTransformer based on microsoft/mpnet-base |
| |
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| |
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| |
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| |
|
| | ### Model Sources |
| |
|
| | - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
| | - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
| | - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
| |
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| |
|
| | ## Usage |
| |
|
| | ### Direct Usage (Sentence Transformers) |
| |
|
| | First install the Sentence Transformers library: |
| |
|
| | ```bash |
| | pip install -U sentence-transformers |
| | ``` |
| |
|
| | Then you can load this model and run inference. |
| | ```python |
| | from sentence_transformers import SentenceTransformer |
| | |
| | # Download from the 🤗 Hub |
| | model = SentenceTransformer("sentence_transformers_model_id") |
| | # Run inference |
| | sentences = [ |
| | 'This form of necrosis, also termed necroptosis, requires the activity of receptor-interacting protein kinase 1 (RIP1) and its related kinase, RIP3 ', |
| | 'TNF-mediated programmed necrosis typically involves the receptor-interacting serine-threonine kinases 1 and 3 (RIP1 and RIP3), as evidenced in human, mouse, and zebrafish cell lines, as well as in a murine sepsis model', |
| | 'This large-scale study showed that IDH1/IDH2 mutations were mutually exclusive with inactivating TET2 mutations, suggesting that the two types of mutations had similar effects and were thus functionally redundant.', |
| | ] |
| | embeddings = model.encode(sentences) |
| | print(embeddings.shape) |
| | # [3, 768] |
| | |
| | # Get the similarity scores for the embeddings |
| | similarities = model.similarity(embeddings, embeddings) |
| | print(similarities.shape) |
| | # [3, 3] |
| | ``` |
