Sentence Similarity
sentence-transformers
Safetensors
Transformers
Persian
English
qwen3
text-generation
persian
text-embeddings
dense-retrieval
semantic-search
knowledge-distillation
feature-extraction
text-embeddings-inference
Instructions to use Yaldat/Yalda-Embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Yaldat/Yalda-Embedding with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Yaldat/Yalda-Embedding") 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 Yaldat/Yalda-Embedding with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Yaldat/Yalda-Embedding") model = AutoModelForCausalLM.from_pretrained("Yaldat/Yalda-Embedding", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 313 Bytes
eba50b4 | 1 2 3 4 5 6 7 8 9 10 | {
"word_embedding_dimension": 1024,
"pooling_mode_cls_token": false,
"pooling_mode_mean_tokens": false,
"pooling_mode_max_tokens": false,
"pooling_mode_mean_sqrt_len_tokens": false,
"pooling_mode_weightedmean_tokens": false,
"pooling_mode_lasttoken": true,
"include_prompt": true
} |