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
| { | |
| "cls_token": { | |
| "content": "[CLS]", | |
| "lstrip": false, | |
| "normalized": false, | |
| "rstrip": false, | |
| "single_word": false | |
| }, | |
| "mask_token": { | |
| "content": "[MASK]", | |
| "lstrip": false, | |
| "normalized": false, | |
| "rstrip": false, | |
| "single_word": false | |
| }, | |
| "pad_token": { | |
| "content": "[PAD]", | |
| "lstrip": false, | |
| "normalized": false, | |
| "rstrip": false, | |
| "single_word": false | |
| }, | |
| "sep_token": { | |
| "content": "[SEP]", | |
| "lstrip": false, | |
| "normalized": false, | |
| "rstrip": false, | |
| "single_word": false | |
| }, | |
| "unk_token": { | |
| "content": "[UNK]", | |
| "lstrip": false, | |
| "normalized": false, | |
| "rstrip": false, | |
| "single_word": false | |
| } | |
| } | |