README.md

---
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- autotrain
base_model: google/bert_uncased_L-2_H-128_A-2
widget:
- source_sentence: 'dogs are playful'
  sentences:
  - 'i love cats'
  - 'i love dogs'
pipeline_tag: sentence-similarity
datasets:
- cnmoro/PremiseHypothesisLabel_ENPT
---

# Model Trained Using AutoTrain

- Problem type: Sentence Transformers

## Validation Metrics
loss: 0.056979671120643616

## Info

This is the bert-tiny model finetuned on 15B tokens for embedding/feature extraction, for English and Brazillian Portuguese languages.

The output vector size is 128.

This model only has 4.4M params but the quality of the embeddings punch way above its size after tuning.

## 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/bert-tiny-embeddings-english-portuguese")
# Run inference
sentences = [
    'first passage',
    'second passage'
]
embeddings = model.encode(sentences)
print(embeddings.shape)
```