Feature Extraction
sentence-transformers
PyTorch
Safetensors
English
roberta
language
granite
embeddings
sparse-encoder
sparse
splade
text-embeddings-inference
Instructions to use seerware/granite-embedding-30m-sparse with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use seerware/granite-embedding-30m-sparse with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("seerware/granite-embedding-30m-sparse") 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
File size: 688 Bytes
d8f09e1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 | {
"_name_or_path": "granite-embedding-30m-sparse",
"architectures": [
"RobertaForMaskedLM"
],
"attention_probs_dropout_prob": 0.1,
"bos_token_id": 0,
"classifier_dropout": null,
"eos_token_id": 2,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 384,
"initializer_range": 0.02,
"intermediate_size": 1536,
"layer_norm_eps": 1e-12,
"max_position_embeddings": 514,
"model_type": "roberta",
"num_attention_heads": 12,
"num_hidden_layers": 6,
"pad_token_id": 1,
"position_embedding_type": "absolute",
"torch_dtype": "bfloat16",
"transformers_version": "4.48.2",
"type_vocab_size": 2,
"use_cache": true,
"vocab_size": 50265
}
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