Sentence Similarity
sentence-transformers
Safetensors
Transformers
qwen2
text-generation
mteb
Qwen2
custom_code
Eval Results (legacy)
text-embeddings-inference
Instructions to use DecisionOptimizationSystemProduction/DeepFeatTextEmbeddingLarge with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
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] - Transformers
How to use DecisionOptimizationSystemProduction/DeepFeatTextEmbeddingLarge with Transformers:
# 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) - Notebooks
- Google Colab
- Kaggle
| { | |
| "__version__": { | |
| "sentence_transformers": "2.7.0", | |
| "transformers": "4.39.3", | |
| "pytorch": "2.1.0+cu121" | |
| }, | |
| "prompts": { | |
| "query": "Instruct: Given a web search query, retrieve relevant passages that answer the query\nQuery: " | |
| }, | |
| "default_prompt_name": null | |
| } | |