metadata

library_name: sentence-transformers
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - autotrain
base_model: google-bert/bert-base-multilingual-cased
widget:
  - source_sentence: 'search_query: i love autotrain'
    sentences:
      - 'search_query: huggingface auto train'
      - 'search_query: hugging face auto train'
      - 'search_query: i love autotrain'
pipeline_tag: sentence-similarity

Model Trained Using AutoTrain

Problem type: Sentence Transformers

Validation Metrics

loss: 1.0433918237686157

runtime: 63.0935

samples_per_second: 2.599

steps_per_second: 0.174

: 3.0

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("sentence_transformers_model_id")
# Run inference
sentences = [
    'search_query: autotrain',
    'search_query: auto train',
    'search_query: i love autotrain',
]
embeddings = model.encode(sentences)
print(embeddings.shape)

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