Feature Extraction
sentence-transformers
ONNX
xlm-roberta
teradata
byom
embeddings
multilingual
text-embeddings-inference
Instructions to use Teradata/granite-embedding-278m-multilingual with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Teradata/granite-embedding-278m-multilingual with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Teradata/granite-embedding-278m-multilingual") 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
| {"bos_token": "<s>", "eos_token": "</s>", "sep_token": "</s>", "cls_token": "<s>", "unk_token": "<unk>", "pad_token": "<pad>", "mask_token": {"content": "<mask>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "model_max_length": 512, "special_tokens_map_file": null, "name_or_path": "granite-embedding-278m-multilingual", "tokenizer_class": "XLMRobertaTokenizer"} | |