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vlad-m-dev
/
distiluse-base-multilingual-v2-merged-onnx

Feature Extraction
sentence-transformers
ONNX
Transformers
fastText
sentence-embeddings
sentence-similarity
semantic-search
vector-search
retrieval-augmented-generation
multilingual
cross-lingual
low-resource
merged-model
combined-model
tokenizer-embedded
tokenizer-integrated
standalone
all-in-one
quantized
int8
int8-quantization
optimized
efficient
fast-inference
low-latency
lightweight
small-model
edge-ready
arm64
edge-device
mobile-device
on-device
mobile-inference
tablet
smartphone
embedded-ai
onnx-runtime
onnx-model
MiniLM
MiniLM-L12-v2
paraphrase
usecase-ready
plug-and-play
production-ready
deployment-ready
real-time
distiluse
Model card Files Files and versions
xet
Community

Instructions to use vlad-m-dev/distiluse-base-multilingual-v2-merged-onnx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use vlad-m-dev/distiluse-base-multilingual-v2-merged-onnx with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("vlad-m-dev/distiluse-base-multilingual-v2-merged-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]
  • Transformers

    How to use vlad-m-dev/distiluse-base-multilingual-v2-merged-onnx with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("feature-extraction", model="vlad-m-dev/distiluse-base-multilingual-v2-merged-onnx")
    # pip install -U transformers accelerate
    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("vlad-m-dev/distiluse-base-multilingual-v2-merged-onnx", device_map="auto")
  • fastText

    How to use vlad-m-dev/distiluse-base-multilingual-v2-merged-onnx with fastText:

    from huggingface_hub import hf_hub_download
    import fasttext
    
    model = fasttext.load_model(hf_hub_download("vlad-m-dev/distiluse-base-multilingual-v2-merged-onnx", "model.bin"))
  • Notebooks
  • Google Colab
  • Kaggle
distiluse-base-multilingual-v2-merged-onnx
276 MB
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  • 1 contributor
History: 14 commits
vlad-m-dev's picture
vlad-m-dev
Upload label_encoder_temporal_intent.json
2963340 verified about 1 year ago
  • .gitattributes
    1.52 kB
    initial commit over 1 year ago
  • README.md
    2.96 kB
    Update README.md over 1 year ago
  • combined_tokenizer_embedded_model.onnx
    136 MB
    xet
    Upload combined_tokenizer_embedded_model.onnx over 1 year ago
  • dense_768to512.onnx
    1.58 MB
    xet
    Rename dense_768to512_cpu.onnx to dense_768to512.onnx about 1 year ago
  • future_intent_classifier.onnx
    33.8 kB
    xet
    Upload 3 files about 1 year ago
  • label_encoder_temporal_intent.json
    232 Bytes
    Upload label_encoder_temporal_intent.json about 1 year ago
  • merged_sentence_model.onnx
    138 MB
    xet
    Upload 2 files about 1 year ago
  • past_intent_classifier.onnx
    119 kB
    xet
    Upload 3 files about 1 year ago
  • temporal_intent_classifier.onnx
    8.16 kB
    xet
    Upload 3 files about 1 year ago