Sentence Similarity
sentence-transformers
Safetensors
Korean
English
xlm-roberta
text-embeddings
retrieval
mteb
korean
multilingual
e5
text-embeddings-inference
Instructions to use jjp97/laal-embedding-v0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use jjp97/laal-embedding-v0 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("jjp97/laal-embedding-v0") 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: 293 Bytes
a5e4bd5 | 1 2 3 4 5 6 7 8 9 10 11 12 13 | {
"model": "intfloat/multilingual-e5-large-instruct",
"train_examples": 43983,
"batch_size": 512,
"epochs": 3,
"learning_rate": 1e-05,
"total_steps_approx": 255,
"warmup_ratio": 0.1,
"tau": 0.05,
"gor_lambda": 0.001,
"gor_max_samples": 64,
"max_hn_per_example_train": 2
} |