Sentence Similarity
sentence-transformers
PyTorch
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use whaleloops/phrase-bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use whaleloops/phrase-bert with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("whaleloops/phrase-bert") 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 whaleloops/phrase-bert with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("whaleloops/phrase-bert") model = AutoModel.from_pretrained("whaleloops/phrase-bert") - Inference
- Notebooks
- Google Colab
- Kaggle
zhichao yang commited on
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Parent(s): 1cb1b74
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README.md
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@@ -21,6 +21,8 @@ Using this model becomes easy when you have [sentence-transformers](https://www.
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pip install -U sentence-transformers
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Then you can use the model like this:
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```python
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pip install -U sentence-transformers
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```
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Our model is tested on pytorch=1.9.0, tranformers=4.8.1, sentence-tranformers = 2.1.0 TODO
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Then you can use the model like this:
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```python
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