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Add new SentenceTransformer model.
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metadata
library_name: sentence-transformers
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - autotrain
base_model: symanto/sn-xlm-roberta-base-snli-mnli-anli-xnli
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
datasets:
  - Omartificial-Intelligence-Space/Arabic-NLi-Pair

Model Trained Using AutoTrain

  • Problem type: Sentence Transformers

Validation Metrics

loss: 0.06426659971475601

runtime: 13.9296

samples_per_second: 488.744

steps_per_second: 15.291

: 4.99974548231102

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)