Sentence Similarity
sentence-transformers
Safetensors
English
bert
feature-extraction
Generated from Trainer
dataset_size:30
loss:MatryoshkaLoss
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use mp-ac/mpac-bge-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use mp-ac/mpac-bge-large with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("mp-ac/mpac-bge-large") sentences = [ "O NAT foi criado em 13 de setembro de 2012 pelo Ato n.º 25 da Procuradoria-Geral de Justiça do MPAC.", "Quando o NAT foi criado?", "O que significa NAT?", "Quem instituiu o NAT?" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -63,7 +63,7 @@ metrics:
|
|
| 63 |
- cosine_mrr@10
|
| 64 |
- cosine_map@100
|
| 65 |
model-index:
|
| 66 |
-
- name: BGE
|
| 67 |
results:
|
| 68 |
- task:
|
| 69 |
type: information-retrieval
|
|
|
|
| 63 |
- cosine_mrr@10
|
| 64 |
- cosine_map@100
|
| 65 |
model-index:
|
| 66 |
+
- name: MPAC BGE Large
|
| 67 |
results:
|
| 68 |
- task:
|
| 69 |
type: information-retrieval
|