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Upload README.md
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README.md
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---
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#
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This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
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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 = ["
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model = SentenceTransformer('
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embeddings = model.encode(sentences)
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print(embeddings)
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```
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Parameters of the fit()-Method:
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```
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{
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"epochs":
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"evaluation_steps": 0,
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"evaluator": "NoneType",
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"max_grad_norm": 1,
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---
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# LaBSE-geonames-15K-MBML-1e-v1
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This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
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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 and some other packages:
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```
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pip install -U sentence-transformers
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pip install diffusers
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pip install safetensors
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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 = ["Vladivostok", "Astana"]
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model = SentenceTransformer('dima-does-code/LaBSE-geonames-15K-MBML-1e-v1')
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embeddings = model.encode(sentences)
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print(embeddings)
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```
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Parameters of the fit()-Method:
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```
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{
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"epochs": 10,
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"evaluation_steps": 0,
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"evaluator": "NoneType",
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"max_grad_norm": 1,
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