Feature Extraction
Transformers
Safetensors
sentence-transformers
English
modernbert
splade
sparse
retrieval
sentence-similarity
custom_code
text-embeddings-inference
Instructions to use Linkup-Platform/linkup-sparseup-embed-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Linkup-Platform/linkup-sparseup-embed-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Linkup-Platform/linkup-sparseup-embed-v1", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Linkup-Platform/linkup-sparseup-embed-v1", trust_remote_code=True) model = AutoModel.from_pretrained("Linkup-Platform/linkup-sparseup-embed-v1", trust_remote_code=True, device_map="auto") - sentence-transformers
How to use Linkup-Platform/linkup-sparseup-embed-v1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Linkup-Platform/linkup-sparseup-embed-v1", trust_remote_code=True) 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
| { | |
| "backend": "tokenizers", | |
| "clean_up_tokenization_spaces": true, | |
| "cls_token": "[CLS]", | |
| "is_local": true, | |
| "mask_token": "[MASK]", | |
| "max_length": 299, | |
| "model_input_names": [ | |
| "input_ids", | |
| "attention_mask" | |
| ], | |
| "model_max_length": 299, | |
| "pad_to_multiple_of": null, | |
| "pad_token": "[MASK]", | |
| "pad_token_type_id": 0, | |
| "padding_side": "right", | |
| "sep_token": "[SEP]", | |
| "stride": 0, | |
| "tokenizer_class": "TokenizersBackend", | |
| "truncation_side": "right", | |
| "truncation_strategy": "longest_first", | |
| "unk_token": "[UNK]" | |
| } | |