bossiara13/J.A.R.V.I.S-py
1
1
Fork 0
Code Issues Pull requests Projects Releases 6 Packages Activity Actions
J.A.R.V.I.S-py/resources/models/paraphrase-multilingual-MiniLM-L12-v2-onnx-Q
History
Exact
Priler e367d11b7c Fix intent classifier init
2026-02-08 07:30:46 +05:00
..
.gitattributes New intent classification engine - MiniLM L6v2 and MiniLM L12v2 ONNX 2026-02-08 07:16:03 +05:00
config.json New intent classification engine - MiniLM L6v2 and MiniLM L12v2 ONNX 2026-02-08 07:16:03 +05:00
model.onnx Fix intent classifier init 2026-02-08 07:30:46 +05:00
ort_config.json New intent classification engine - MiniLM L6v2 and MiniLM L12v2 ONNX 2026-02-08 07:16:03 +05:00
README.md New intent classification engine - MiniLM L6v2 and MiniLM L12v2 ONNX 2026-02-08 07:16:03 +05:00
special_tokens_map.json New intent classification engine - MiniLM L6v2 and MiniLM L12v2 ONNX 2026-02-08 07:16:03 +05:00
tokenizer.json New intent classification engine - MiniLM L6v2 and MiniLM L12v2 ONNX 2026-02-08 07:16:03 +05:00
tokenizer_config.json New intent classification engine - MiniLM L6v2 and MiniLM L12v2 ONNX 2026-02-08 07:16:03 +05:00
unigram.json New intent classification engine - MiniLM L6v2 and MiniLM L12v2 ONNX 2026-02-08 07:16:03 +05:00

README.md

license pipeline_tag
apache-2.0 sentence-similarity

Quantized ONNX port of sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 for text classification and similarity searches.

Usage

Here's an example of performing inference using the model with FastEmbed.

from fastembed import TextEmbedding

documents = [
    "You should stay, study and sprint.",
    "History can only prepare us to be surprised yet again.",
]

model = TextEmbedding(model_name="sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2")
embeddings = list(model.embed(documents))

# [
#     array([1.96449570e-02, 1.60677675e-02, 4.10149433e-02...]),
#     array([-1.56669170e-02, -1.66313536e-02, -6.84525725e-03...])
# ]