Sentence Similarity
sentence-transformers
TensorBoard
Safetensors
distilbert
feature-extraction
Trained with AutoTrain
text-embeddings-inference
Instructions to use RMS999/autotrain-9eptm-5kojx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use RMS999/autotrain-9eptm-5kojx with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("RMS999/autotrain-9eptm-5kojx") sentences = [ "search_query: i love autotrain", "search_query: huggingface auto train", "search_query: hugging face auto train", "search_query: i love autotrain" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Model Trained Using AutoTrain
- Problem type: Sentence Transformers
Validation Metrics
loss: 0.32150912284851074
runtime: 1.6515
samples_per_second: 12.11
steps_per_second: 1.211
: 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)
- Downloads last month
- 55
Model tree for RMS999/autotrain-9eptm-5kojx
Base model
distilbert/distilbert-base-uncased