Sentence Similarity
sentence-transformers
PyTorch
TensorFlow
Safetensors
bert
feature-extraction
text-embeddings-inference
Instructions to use SeyedAli/Multilingual-Text-Semantic-Search-Siamese-BERT-V1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use SeyedAli/Multilingual-Text-Semantic-Search-Siamese-BERT-V1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("SeyedAli/Multilingual-Text-Semantic-Search-Siamese-BERT-V1") 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] - Notebooks
- Google Colab
- Kaggle
Update train_script.py
Browse files- train_script.py +1 -1
train_script.py
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@@ -291,7 +291,7 @@ class Dataset:
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument('--model', default='
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parser.add_argument('--steps', type=int, default=2000)
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parser.add_argument('--save_steps', type=int, default=10000)
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parser.add_argument('--batch_size', type=int, default=64)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument('--model', default='SeyedAli/Multilingual-Text-Semantic-Search-Siamese-BERT-V1')
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parser.add_argument('--steps', type=int, default=2000)
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parser.add_argument('--save_steps', type=int, default=10000)
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parser.add_argument('--batch_size', type=int, default=64)
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