Sentence Similarity
sentence-transformers
Safetensors
modernbert
feature-extraction
dense
Generated from Trainer
dataset_size:800640
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use Shuu12121/Owl-ph2-len2048 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Shuu12121/Owl-ph2-len2048 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Shuu12121/Owl-ph2-len2048") 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
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### Model Description
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- **Model Type:** Sentence Transformer
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- **Base model:** [Shuu12121/Owl-ph2-base-len2048](https://huggingface.co/Shuu12121/Owl-ph2-base-len2048)
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- **Maximum Sequence Length:** 1024 tokens
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- **Output Dimensionality:** 768
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- **Similarity Function:** Cosine Similarity
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### Model Description
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- **Model Type:** Sentence Transformer
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- **Base model:** [Shuu12121/Owl-ph2-base-len2048](https://huggingface.co/Shuu12121/Owl-ph2-base-len2048)
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- **Maximum Sequence Length:** 1024 tokens (2048 tokens during pretraining)
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- **Output Dimensionality:** 768
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- **Similarity Function:** Cosine Similarity
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