---
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- autotrain
base_model:
- cnmoro/micro-bertim
- adalbertojunior/distilbert-portuguese-cased
widget:
- source_sentence: 'search_query: i love autotrain'
  sentences:
  - 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
pipeline_tag: sentence-similarity
datasets:
- cnmoro/AllTripletsMsMarco-PTBR
license: apache-2.0
language:
- pt
---

A manually pruned version of [distilbert-portuguese-cased](https://huggingface.co./adalbertojunior/distilbert-portuguese-cased), finetuned to produce high quality embeddings in a lightweight form factor.

# Model Trained Using AutoTrain

- Problem type: Sentence Transformers

## Validation Metrics
loss: 0.3181200921535492

cosine_accuracy: 0.8921948650328134

## Usage

### Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

```bash
pip install -U sentence-transformers
```

Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer

# Download from the Hugging Face Hub
model = SentenceTransformer("cnmoro/micro-bertim-embeddings")
# Run inference
sentences = [
    '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)
print(embeddings.shape)

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
```