Sentence Similarity
sentence-transformers
Safetensors
Transformers
new
feature-extraction
mteb
multilingual
text-embeddings-inference
custom_code
Eval Results (legacy)
Instructions to use Alibaba-NLP/gte-multilingual-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Alibaba-NLP/gte-multilingual-base with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Alibaba-NLP/gte-multilingual-base", trust_remote_code=True) sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Transformers
How to use Alibaba-NLP/gte-multilingual-base with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Alibaba-NLP/gte-multilingual-base", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
How to fine-tune?
#10
by havardox - opened
Pretty basic question. How do I fine-tune the model in SentenceTransformers?
Hello!
You can use this snippet: https://huggingface.co/blog/train-sentence-transformers#trainer
But replace
# 1. Load a model to finetune with 2. (Optional) model card data
model = SentenceTransformer(
"microsoft/mpnet-base",
model_card_data=SentenceTransformerModelCardData(
language="en",
license="apache-2.0",
model_name="MPNet base trained on AllNLI triplets",
)
)
with
# 1. Load a model to finetune with 2. (Optional) model card data
model = SentenceTransformer(
"Alibaba-NLP/gte-multilingual-base",
trust_remote_code=True,
)
The rest of the blogpost should help explain what the code means.
There's more documentation here: https://sbert.net/docs/sentence_transformer/training_overview.html
- Tom Aarsen