Sentence Similarity
sentence-transformers
Safetensors
Transformers
qwen2
text-generation
mteb
Qwen2
custom_code
Eval Results (legacy)
text-embeddings-inference
Instructions to use Alibaba-NLP/gte-Qwen2-7B-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Alibaba-NLP/gte-Qwen2-7B-instruct with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Alibaba-NLP/gte-Qwen2-7B-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/gte-Qwen2-7B-instruct with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Alibaba-NLP/gte-Qwen2-7B-instruct", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("Alibaba-NLP/gte-Qwen2-7B-instruct", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
hot fix: config.json
#48
by michaelfeil - opened
Quick oversight, I missed a , in the json!
thenlper changed pull request status to merged
that was fast, thank you! and sorry!
You're welcome! No problem at all. Thank you for your interest and contributions to the GTE model!