Feature Extraction
sentence-transformers
Safetensors
English
bert
splade
sparse-encoder
lexical-semantics
word-in-context
word-sense-disambiguation
text-embeddings-inference
Instructions to use jadermcs/lexsplade-bert-large-silver with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use jadermcs/lexsplade-bert-large-silver with sentence-transformers:
from sentence_transformers import SparseEncoder model = SparseEncoder("jadermcs/lexsplade-bert-large-silver") queries = ["Which planet is known as the Red Planet?"] documents = [ "Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", ] query_embeddings = model.encode_query(queries) document_embeddings = model.encode_document(documents) similarities = model.similarity(query_embeddings, document_embeddings) print(similarities) - Notebooks
- Google Colab
- Kaggle
Download modules.json from jadermcs/lexsplade-bert-large-silver: direct link, hf CLI and curl.
- Browser
- Download file 258 Bytes
-
https://huggingface.co/jadermcs/lexsplade-bert-large-silver/resolve/main/modules.json
- Command line
-
hf download hf://jadermcs/lexsplade-bert-large-silver/modules.json
-
curl -L -o modules.json https://huggingface.co/jadermcs/lexsplade-bert-large-silver/resolve/main/modules.json
258 Bytes
| [ | |
| { | |
| "idx": 0, | |
| "name": "0", | |
| "path": "", | |
| "type": "modeling_lexsplade.DenseHiddenStatesTransformer" | |
| }, | |
| { | |
| "idx": 1, | |
| "name": "1", | |
| "path": "1_TargetWordSpladePooling", | |
| "type": "modeling_lexsplade.TargetWordSpladePooling" | |
| } | |
| ] |