Instructions to use Yasser18/Embad_mini_LM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Model2Vec
How to use Yasser18/Embad_mini_LM with Model2Vec:
from model2vec import StaticModel model = StaticModel.from_pretrained("Yasser18/Embad_mini_LM") - sentence-transformers
How to use Yasser18/Embad_mini_LM with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Yasser18/Embad_mini_LM") 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] - Notebooks
- Google Colab
- Kaggle
File size: 330 Bytes
b3b138c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 | {
"model_type": "model2vec",
"architectures": [
"StaticModel"
],
"tokenizer_name": "sentence-transformers/all-MiniLM-L6-v2",
"apply_pca": 64,
"sif_coefficient": 0.0001,
"hidden_dim": 64,
"seq_length": 1000000,
"normalize": true,
"pooling": "mean",
"embedding_dtype": "float16"
} |