Sentence Similarity
sentence-transformers
TensorBoard
Safetensors
Portuguese
bert
feature-extraction
Trained with AutoTrain
text-embeddings-inference
Instructions to use cnmoro/micro-bertim-embeddings with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use cnmoro/micro-bertim-embeddings with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("cnmoro/micro-bertim-embeddings") sentences = [ "search_query: i love autotrain", "O pôr do sol pinta o céu com tons de laranja e vermelho", "Joana adora estudar matemática nas tardes de sábado", "Os pássaros voam em formação, criando um espetáculo no horizonte" ] 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.models.Transformer" | |
| }, | |
| { | |
| "idx": 1, | |
| "name": "1", | |
| "path": "1_Pooling", | |
| "type": "sentence_transformers.models.Pooling" | |
| } | |
| ] |