Sentence Similarity
sentence-transformers
Safetensors
Transformers
English
Chinese
qwen2_vl
image-text-to-text
mteb
Qwen2-VL
vidore
custom_code
Eval Results (legacy)
Instructions to use Alibaba-NLP/gme-Qwen2-VL-2B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Alibaba-NLP/gme-Qwen2-VL-2B-Instruct with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Alibaba-NLP/gme-Qwen2-VL-2B-Instruct", trust_remote_code=True) sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Transformers
How to use Alibaba-NLP/gme-Qwen2-VL-2B-Instruct with Transformers:
# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("Alibaba-NLP/gme-Qwen2-VL-2B-Instruct", trust_remote_code=True) model = AutoModelForMultimodalLM.from_pretrained("Alibaba-NLP/gme-Qwen2-VL-2B-Instruct", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Results on M-BEIR
#7
by wongyukim - opened
Thanks for your nice work!
Could you provide the M-BEIR results on global candidate pool? (not on local candidate pool)
Thank you.
same. The results we reproduced were similar to those of the local pool, but the paper does not specify whether it was tested on the local pool or the global pool.
May I ask how the local candidate pool is constructed, and can it be run directly on MTEB?@LightSunKing @wongyukim