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
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")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("vlad-m-dev/distiluse-base-multilingual-v2-merged-onnx", dtype="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
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