Feature Extraction
sentence-transformers
ONNX
Safetensors
OpenVINO
Transformers
modernbert
granite
embeddings
multilingual
mteb
sentence-similarity
text-embeddings-inference
Instructions to use Hisham480/multilingual-r2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Hisham480/multilingual-r2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Hisham480/multilingual-r2") 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] - Transformers
How to use Hisham480/multilingual-r2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Hisham480/multilingual-r2")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Hisham480/multilingual-r2") model = AutoModel.from_pretrained("Hisham480/multilingual-r2") - Notebooks
- Google Colab
- Kaggle
| { | |
| "word_embedding_dimension": 384, | |
| "pooling_mode_cls_token": true, | |
| "pooling_mode_mean_tokens": false, | |
| "pooling_mode_max_tokens": false, | |
| "pooling_mode_mean_sqrt_len_tokens": false, | |
| "pooling_mode_weightedmean_tokens": false, | |
| "pooling_mode_lasttoken": false, | |
| "include_prompt": false | |
| } |