Feature Extraction
Transformers
Safetensors
sentence-transformers
English
Armenian
xlm-roberta
ArmBench-TextEmbed
text-embeddings-inference
Instructions to use Metric-AI/armenian-text-embeddings-2-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Metric-AI/armenian-text-embeddings-2-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Metric-AI/armenian-text-embeddings-2-large")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Metric-AI/armenian-text-embeddings-2-large") model = AutoModel.from_pretrained("Metric-AI/armenian-text-embeddings-2-large") - sentence-transformers
How to use Metric-AI/armenian-text-embeddings-2-large with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Metric-AI/armenian-text-embeddings-2-large") 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
- Xet hash:
- 9a184b2b11046834b0cd331a6add8c60f316cd5fc20f545baf507182cfc22a21
- Size of remote file:
- 17.1 MB
- SHA256:
- 3a56def25aa40facc030ea8b0b87f3688e4b3c39eb8b45d5702b3a1300fe2a20
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