Text Classification
ONNX
GGUF
sentence-transformers
multilingual
llama.cpp
qwen3
embedding
llama-cpp
jina-embeddings-v5
feature-extraction
mteb
vllm
text-embeddings-inference
Instructions to use TonitoMC/jina-embeddings-v5-text-small-classification-onnx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use TonitoMC/jina-embeddings-v5-text-small-classification-onnx with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("TonitoMC/jina-embeddings-v5-text-small-classification-onnx") 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
File size: 1,536 Bytes
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"architectures": [
"Qwen3Model"
],
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"bos_token_id": 151643,
"dtype": "bfloat16",
"eos_token_id": 151645,
"head_dim": 128,
"hidden_act": "silu",
"hidden_size": 1024,
"initializer_range": 0.02,
"intermediate_size": 3072,
"layer_types": [
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],
"max_position_embeddings": 32768,
"max_window_layers": 28,
"model_type": "qwen3",
"num_attention_heads": 16,
"num_hidden_layers": 28,
"num_key_value_heads": 8,
"rms_norm_eps": 1e-06,
"rope_parameters": {
"rope_theta": 3500000,
"rope_type": "default"
},
"rope_scaling": null,
"rope_theta": 10000.0,
"sliding_window": null,
"task_names": [
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"text-matching",
"clustering",
"classification"
],
"tie_word_embeddings": true,
"transformers_version": "4.51.0",
"use_cache": true,
"use_sliding_window": false,
"vocab_size": 151936
}
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