TaylorAI

/

gte-tiny

like 124

hi @simonw ,

You can use huggingface_hub and snapshot only the needed files and pass the folder to the SentenceTransformer constructor

from sentence_transformers import SentenceTransformer
from huggingface_hub import snapshot_download

sentences = ["This is an example sentence", "Each sentence is converted"]
model_path = snapshot_download(
    repo_id="TaylorAI/gte-tiny", allow_patterns=["*.json", "pytorch_model.bin"]
)

model = SentenceTransformer(model_path)
embeddings = model.encode(sentences)
print(embeddings)

I'd also recommend the use of model.safetensors

from sentence_transformers import SentenceTransformer
from huggingface_hub import snapshot_download

sentences = ["This is an example sentence", "Each sentence is converted"]
model_path = snapshot_download(
    repo_id="TaylorAI/gte-tiny", allow_patterns=["*.json", "model.safetensors"]
)

model = SentenceTransformer(model_path)
embeddings = model.encode(sentences)
print(embeddings)

I can also release another minimal version without the onnx weights for SentenceTransformers.

txtai has built-in logic for mean/cls pooling using Transformers, which only downloads the files it needs.

For example:

import txtai

embeddings = txtai.Embeddings(path="TaylorAI/gte-tiny")
embeddings.batchtransform(["text1", "text2"])

very cool @davidmezzetti , just test it