metadata

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:

pip install -U sentence-transformers

Then you can load this model and run inference.

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)