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)