metadata
license: apache-2.0
datasets:
- cnmoro/AllTripletsMsMarco-PTBR
- Tevatron/msmarco-passage-corpus
language:
- en
- pt
library_name: model2vec
base_model:
- nomic-ai/nomic-embed-text-v2-moe
pipeline_tag: feature-extraction
This Model2Vec model was created by using Tokenlearn, with nomic-embed-text-v2-moe as a base, trained on around 20M passages (english and portuguese).
The output dimension is 50.
This is supposed to be a more minimalistic version of cnmoro/static-nomic-eng-ptbr
Usage
Load this model using the from_pretrained method:
from model2vec import StaticModel
# Load a pretrained Model2Vec model
model = StaticModel.from_pretrained("cnmoro/static-nomic-eng-ptbr-tiny")
# Compute text embeddings
embeddings = model.encode(["Example sentence"])