Sentence Similarity
sentence-transformers
PyTorch
TensorFlow
ONNX
Safetensors
OpenVINO
Transformers
distilbert
feature-extraction
text-embeddings-inference
Instructions to use sentence-transformers/distilbert-multilingual-nli-stsb-quora-ranking with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use sentence-transformers/distilbert-multilingual-nli-stsb-quora-ranking with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("sentence-transformers/distilbert-multilingual-nli-stsb-quora-ranking") 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 sentence-transformers/distilbert-multilingual-nli-stsb-quora-ranking with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/distilbert-multilingual-nli-stsb-quora-ranking") model = AutoModel.from_pretrained("sentence-transformers/distilbert-multilingual-nli-stsb-quora-ranking") - Inference
- Notebooks
- Google Colab
- Kaggle
File size: 589 Bytes
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"_name_or_path": "old_models/distilbert-multilingual-nli-stsb-quora-ranking/0_Transformer",
"activation": "gelu",
"architectures": [
"DistilBertModel"
],
"attention_dropout": 0.1,
"dim": 768,
"dropout": 0.1,
"hidden_dim": 3072,
"initializer_range": 0.02,
"max_position_embeddings": 512,
"model_type": "distilbert",
"n_heads": 12,
"n_layers": 6,
"output_past": true,
"pad_token_id": 0,
"qa_dropout": 0.1,
"seq_classif_dropout": 0.2,
"sinusoidal_pos_embds": false,
"tie_weights_": true,
"transformers_version": "4.7.0",
"vocab_size": 119547
}
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