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, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("DecisionOptimizationSystemProduction/DeepFeatTextEmbeddingLarge", trust_remote_code=True) model = AutoModelForMultimodalLM.from_pretrained("DecisionOptimizationSystemProduction/DeepFeatTextEmbeddingLarge", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
| [ | |
| { | |
| "idx": 0, | |
| "name": "0", | |
| "path": "", | |
| "type": "sentence_transformers.models.Transformer" | |
| }, | |
| { | |
| "idx": 1, | |
| "name": "1", | |
| "path": "1_Pooling", | |
| "type": "sentence_transformers.models.Pooling" | |
| }, | |
| { | |
| "idx": 2, | |
| "name": "2", | |
| "path": "2_Normalize", | |
| "type": "sentence_transformers.models.Normalize" | |
| } | |
| ] |