Feature Extraction
sentence-transformers
PyTorch
ONNX
Safetensors
OpenVINO
xlm-roberta
mteb
Sentence Transformers
sentence-similarity
Eval Results (legacy)
Eval Results
text-embeddings-inference
Instructions to use intfloat/multilingual-e5-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use intfloat/multilingual-e5-large with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("intfloat/multilingual-e5-large") 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] - Inference
- Notebooks
- Google Colab
- Kaggle
Update config.json
#35
by ildodeltaRule - opened
- config.json +1 -1
config.json
CHANGED
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@@ -13,7 +13,7 @@
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"initializer_range": 0.02,
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"intermediate_size": 4096,
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"layer_norm_eps": 1e-05,
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"max_position_embeddings":
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"model_type": "xlm-roberta",
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"num_attention_heads": 16,
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"num_hidden_layers": 24,
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"initializer_range": 0.02,
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| 14 |
"intermediate_size": 4096,
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"layer_norm_eps": 1e-05,
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+
"max_position_embeddings": 512,
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"model_type": "xlm-roberta",
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"num_attention_heads": 16,
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"num_hidden_layers": 24,
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