Sentence Similarity
sentence-transformers
Safetensors
modernbert
feature-extraction
dense
Generated from Trainer
dataset_size:5647936
loss:EmbedDistillLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use yjoonjang/MIMO-mmBERT-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use yjoonjang/MIMO-mmBERT-base with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("yjoonjang/MIMO-mmBERT-base") sentences = [ "Quiero dar las gracias -quiero decirlo correctamente- a la Comisión dimitida que se encuentra todavía en funciones, porque no fue una labor sencilla.", "They say you killed Joffrey.", "es", "I should like to thank the Commission which - I want to get this right - has resigned, but which is still in office. It has not been an easy time." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
| [ | |
| { | |
| "idx": 0, | |
| "name": "0", | |
| "path": "", | |
| "type": "sentence_transformers.base.modules.transformer.Transformer" | |
| }, | |
| { | |
| "idx": 1, | |
| "name": "1", | |
| "path": "1_Pooling", | |
| "type": "sentence_transformers.sentence_transformer.modules.pooling.Pooling" | |
| } | |
| ] |