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J.A.R.V.I.S-rust/resources/models/all-MiniLM-L6-v2
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Priler 8e830334e8 New intent classification engine - MiniLM L6v2 and MiniLM L12v2 ONNX
2026-02-08 07:16:03 +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 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
vocab.txt 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

ONNX port of sentence-transformers/all-MiniLM-L6-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/all-MiniLM-L6-v2")
embeddings = list(model.embed(documents))

# [
#     array([
#         0.00611658, 0.00068912, -0.0203846, ..., -0.01751488, -0.01174267,
#         0.01463472
#     ],
#           dtype=float32),
#     array([
#         0.00173448, -0.00329958, 0.01557874, ..., -0.01473586, 0.0281806,
#         -0.00448205
#     ],
#           dtype=float32)
# ]