Sentence Similarity
sentence-transformers
PyTorch
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use fogomg/BioBert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use fogomg/BioBert with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("fogomg/BioBert") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Transformers
How to use fogomg/BioBert with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("fogomg/BioBert") model = AutoModel.from_pretrained("fogomg/BioBert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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## Citing & Authors
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<!--- Describe where people can find more information -->
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## Citing & Authors
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<!--- Describe where people can find more information -->
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If you use the model kindly cite the following work
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```
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@inproceedings{deka2021unsupervised,
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title={Unsupervised Keyword Combination Query Generation from Online Health Related Content for Evidence-Based Fact Checking},
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author={Deka, Pritam and Jurek-Loughrey, Anna},
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booktitle={The 23rd International Conference on Information Integration and Web Intelligence},
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pages={267--277},
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year={2021}
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}
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```
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