Feature Extraction
sentence-transformers
Safetensors
xlm-roberta
sentence-similarity
text-embeddings-inference
Instructions to use Parveshiiii/Embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Parveshiiii/Embedding with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Parveshiiii/Embedding") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
Update 1_Pooling/config.json
Browse files- 1_Pooling/config.json +3 -3
1_Pooling/config.json
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{
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"word_embedding_dimension": 1024,
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"pooling_mode_cls_token":
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"pooling_mode_mean_tokens":
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"pooling_mode_max_tokens": false,
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"pooling_mode_mean_sqrt_len_tokens": false,
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"pooling_mode_weightedmean_tokens": false,
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"pooling_mode_lasttoken": false,
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"include_prompt":
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}
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{
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"word_embedding_dimension": 1024,
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"pooling_mode_cls_token": false,
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"pooling_mode_mean_tokens": true,
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"pooling_mode_max_tokens": false,
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"pooling_mode_mean_sqrt_len_tokens": false,
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"pooling_mode_weightedmean_tokens": false,
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"pooling_mode_lasttoken": false,
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"include_prompt": false
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}
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