Sentence Similarity
sentence-transformers
Safetensors
bert
multilingual
layer-pruning
vocab-pruning
knowledge-distillation
minilm-l12
text-embeddings-inference
Instructions to use gomyk/minilm-student-L6_uniform_distilled with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use gomyk/minilm-student-L6_uniform_distilled with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("gomyk/minilm-student-L6_uniform_distilled") 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
| { | |
| "word_embedding_dimension": 384, | |
| "pooling_mode_cls_token": false, | |
| "pooling_mode_mean_tokens": true, | |
| "pooling_mode_max_tokens": false, | |
| "pooling_mode_mean_sqrt_len_tokens": false, | |
| "pooling_mode_weightedmean_tokens": false, | |
| "pooling_mode_lasttoken": false, | |
| "include_prompt": true | |
| } |