from malaya.supervised.huggingface import load
from malaya.torch_model.huggingface import Embedding
openai_metrics = {
'b.cari.com.my': {
'positive score': 0.8729225971201091,
'negative score': 0.27480777421889363,
'top1': 0.31621790857858484,
'top3': 0.6242955541640576,
'top5': 0.6944270507201001,
'top10': 0.7623669380087664,
},
'c.cari.com.my': {
'positive score': 0.8173745331635356,
'negative score': 0.3100609159718768,
'top1': 0.08380430943129637,
'top3': 0.21388202048746027,
'top5': 0.27861179795125396,
'top10': 0.3589720946661957,
},
'malay-news': {
'positive score': 0.8448714707337686,
'negative score': 0.2741472719191583,
'top1': 0.1386895659334196,
'top3': 0.2952593812492648,
'top5': 0.3745441712739678,
'top10': 0.4754734737089754,
},
'twitter': {
'positive score': 0.8928321128367129,
'negative score': 0.26488808270585834,
'top1': 0.22942090082094518,
'top3': 0.4919014865764367,
'top5': 0.5930774351009541,
'top10': 0.7248724206789439,
},
}
available_huggingface = {
'mesolitica/mistral-embedding-191m-8k-contrastive': {
'Size (MB)': 334,
'embedding size': 768,
'Suggested length': 8192,
},
'mesolitica/mistral-embedding-349m-8k-contrastive': {
'Size (MB)': 633,
'embedding size': 768,
'Suggested length': 8192,
},
'mesolitica/embedding-malaysian-mistral-64M-32k': {
'Size (MB)': 96.5,
'embedding size': 768,
'Suggested length': 20480,
}
}
info = """
Entire Malaysian embedding benchmark at https://huggingface.co/spaces/mesolitica/malaysian-embedding-leaderboard
"""
[docs]def huggingface(
model: str = 'mesolitica/embedding-malaysian-mistral-64M-32k',
force_check: bool = True,
**kwargs,
):
"""
Load HuggingFace model for embedding task.
Parameters
----------
model: str, optional (default='mesolitica/embedding-malaysian-mistral-64M-32k')
Check available models at `malaya.embedding.available_huggingface`.
force_check: bool, optional (default=True)
Force check model one of malaya model.
Set to False if you have your own huggingface model.
Returns
-------
result: malaya.torch_model.huggingface.Embedding
"""
return load(
model=model,
class_model=Embedding,
available_huggingface=available_huggingface,
force_check=force_check,
path=__name__,
**kwargs,
)
