Sentence Similarity
sentence-transformers
Safetensors
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use Youmnaaaa/Semantic-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Youmnaaaa/Semantic-model with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Youmnaaaa/Semantic-model") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Transformers
How to use Youmnaaaa/Semantic-model with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Youmnaaaa/Semantic-model") model = AutoModel.from_pretrained("Youmnaaaa/Semantic-model") - Notebooks
- Google Colab
- Kaggle
| { | |
| "model_name": "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2", | |
| "data_xlsx": "chatbot_dataset_v6FINAL.xlsx", | |
| "data_source": "api", | |
| "places_endpoint": "https://aroundubackend-production.up.railway.app/api/v1/ai/data/places", | |
| "menu_endpoint": "https://aroundubackend-production.up.railway.app/api/mobile/places/{place_id}/menu", | |
| "corpus_rows": 18, | |
| "semantic_query_rows": 699, | |
| "min_score": 0.25, | |
| "top_k": 5 | |
| } |