Duplicated from Alibaba-NLP/gte-Qwen2-1.5B-instruct
How to use DecisionOptimizationSystemProduction/DeepFeatTextEmbeddingLarge with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("DecisionOptimizationSystemProduction/DeepFeatTextEmbeddingLarge", 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]
How to use DecisionOptimizationSystemProduction/DeepFeatTextEmbeddingLarge with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("DecisionOptimizationSystemProduction/DeepFeatTextEmbeddingLarge", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("DecisionOptimizationSystemProduction/DeepFeatTextEmbeddingLarge", trust_remote_code=True, device_map="auto")
https://huggingface.co/DecisionOptimizationSystemProduction/DeepFeatTextEmbeddingLarge/resolve/main/tokenizer.json
hf download hf://DecisionOptimizationSystemProduction/DeepFeatTextEmbeddingLarge/tokenizer.json
curl -L -o tokenizer.json https://huggingface.co/DecisionOptimizationSystemProduction/DeepFeatTextEmbeddingLarge/resolve/main/tokenizer.json
