Embedding Models
Collection
Some embedding models I've trained, finetuned, distilled, converted, or something else entirely • 15 items • Updated
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)
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Model tree for cnmoro/bert-tiny-embeddings-english-portuguese
Base model
google/bert_uncased_L-2_H-128_A-2
from sentence_transformers import SentenceTransformer model = SentenceTransformer("cnmoro/bert-tiny-embeddings-english-portuguese") sentences = [ "dogs are playful", "i love cats", "i love dogs" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3]