mmarco-mMiniLMv2-L12-H384-v1

295.4K
61
514
Small context
117M
14 languages
INT8
license:apache-2.0
by
cross-encoder
Embedding Model
OTHER
Good
295K downloads
Production-ready
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Edge AI:
Mobile
Laptop
Server
1GB+ RAM
Mobile
Laptop
Server
Quick Summary

--- license: apache-2.

Device Compatibility

Mobile
4-6GB RAM
Laptop
16GB RAM
Server
GPU
Minimum Recommended
1GB+ RAM

Code Examples

Usage with SentenceTransformerspython
from sentence_transformers import CrossEncoder
model = CrossEncoder('model_name')
scores = model.predict([('Query', 'Paragraph1'), ('Query', 'Paragraph2') , ('Query', 'Paragraph3')])
Usage with SentenceTransformerspython
from sentence_transformers import CrossEncoder
model = CrossEncoder('model_name')
scores = model.predict([('Query', 'Paragraph1'), ('Query', 'Paragraph2') , ('Query', 'Paragraph3')])
Usage with SentenceTransformerspython
from sentence_transformers import CrossEncoder
model = CrossEncoder('model_name')
scores = model.predict([('Query', 'Paragraph1'), ('Query', 'Paragraph2') , ('Query', 'Paragraph3')])
Usage with SentenceTransformerspython
from sentence_transformers import CrossEncoder
model = CrossEncoder('model_name')
scores = model.predict([('Query', 'Paragraph1'), ('Query', 'Paragraph2') , ('Query', 'Paragraph3')])
Usage with SentenceTransformerspython
from sentence_transformers import CrossEncoder
model = CrossEncoder('model_name')
scores = model.predict([('Query', 'Paragraph1'), ('Query', 'Paragraph2') , ('Query', 'Paragraph3')])
Usage with SentenceTransformerspython
from sentence_transformers import CrossEncoder
model = CrossEncoder('model_name')
scores = model.predict([('Query', 'Paragraph1'), ('Query', 'Paragraph2') , ('Query', 'Paragraph3')])
Usage with SentenceTransformerspython
from sentence_transformers import CrossEncoder
model = CrossEncoder('model_name')
scores = model.predict([('Query', 'Paragraph1'), ('Query', 'Paragraph2') , ('Query', 'Paragraph3')])
Usage with SentenceTransformerspython
from sentence_transformers import CrossEncoder
model = CrossEncoder('model_name')
scores = model.predict([('Query', 'Paragraph1'), ('Query', 'Paragraph2') , ('Query', 'Paragraph3')])

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