Feature Extraction
Transformers
Safetensors
sentence-transformers
English
Armenian
xlm-roberta
text-embeddings-inference
Instructions to use Metric-AI/armenian-text-embeddings-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Metric-AI/armenian-text-embeddings-1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Metric-AI/armenian-text-embeddings-1")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Metric-AI/armenian-text-embeddings-1") model = AutoModel.from_pretrained("Metric-AI/armenian-text-embeddings-1", device_map="auto") - sentence-transformers
How to use Metric-AI/armenian-text-embeddings-1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Metric-AI/armenian-text-embeddings-1") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
Add the Sentence Transformers tag to make this model easier to find
#1
by tomaarsen HF Staff - opened
Hello!
Pull Request overview
- Add the Sentence Transformers tag
Details
With this tag, the model will now also show up here: https://huggingface.co/models?library=sentence-transformers&sort=trending, i.e. among all other popular embedding models.
- Tom Aarsen
bugdaryan changed pull request status to merged
Hello Tom,
Thanks for the update! That sounds great—appreciate you adding the tag.
Best,
Spartak