Sentence Similarity
sentence-transformers
Safetensors
English
bert
feature-extraction
dense
Generated from Trainer
dataset_size:70764
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use Wakeleyhuh/chowder-embed-specter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Wakeleyhuh/chowder-embed-specter with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Wakeleyhuh/chowder-embed-specter") sentences = [ "The hyperspace ω(f) when f is a transitive dendrite mapping", "Knee osteoarthritis (OA) is a major cause of chronic pain and disability in older adults, yet scalable home-based interventions remain limited, partly due to the lack of clinically effective digital solutions. This study is the first fully powered randomized, double-blind, sham-controlled trial to test a digitally delivered, home-based protocol combining transcranial direct current stimulation (tDCS) and mindfulness-based meditation (MBM) for knee OA pain. A total of 208 participants were randomized to active tDCS + MBM, active tDCS + sham MBM, sham tDCS + active MBM, or double sham; they completed ten 20-min sessions over 2 weeks. Knee pain intensity was measured at baseline, post treatment, and monthly for 3 months. Although both active tDCS groups improved, the difference between the combined tDCS + MBM intervention and tDCS alone was not statistically significant. Benefits were not sustained at 3 months. These findings offer the first evidence that a remotely supervised, digitally delivered tDCS + MBM intervention can rapidly reduce knee OA pain, supporting future personalized and extended treatment studies. Clinical trial registration: ClinicalTrials.gov NCT04375072 (registered May 7, 2020).", " Let $X$ be a compact metric space. By $2^X$ we denote the hyperspace of all\nclosed and non-empty subsets of $X$ endowed with the Hausdorff metric. Let\n$f:X\\to X$ be a continuous function. In this paper we study some topological\nproperties of the hyperspace $\\omega(f)$, the collection of all omega limits\nsets $\\omega(x,f)$ with $x\\in X$. We prove the following: $i)$ If $X$ has no\nisolated points, then, for every continuous function $f:X\\to X$,\n$int_{2^X}(\\omega(f))=\\emptyset$. $ii)$ If $X$ is a dendrite for which every\narc contains a free arc and $f:X\\to X$ is transitive, then the hyperspace\n$\\omega(f)$ is totally disconnected. $iii)$ Let $D_\\infty$ be the Wazewski's\nuniversal dendrite. Then there exists a transitive continuous function\n$f:D_\\infty\\to D_\\infty$ for which the hyperspace $\\omega(f)$ contains an arc;\nhence, $\\omega(f)$ is not totally disconnected.\n", "Let $X_1,\\,X_2,\\,\\ldots,\\,X_N$, $N\\in\\mathbb N$ be independent, discrete, integer-valued random variables. Assume that $X_j\\geqslant m_j$ almost surely for each $j=1,\\,2,\\,\\ldots,\\,N$, where $m_1,\\,m_2,\\,\\ldots,\\,m_N\\in\\mathbb{Z}$ satisfy $m_1+\\cdots+m_N<0$. Furthermore, suppose that the sequence $X_1,\\,X_2,\\,\\ldots$ is periodic in distribution, i.e. $X_k{\\buildrel d \\over =} X_{k+N}$ for all $k\\in\\mathbb N$. We derive computable representations for the distribution functions of $\\max\\{X_1,\\,X_1+X_2,\\,\\ldots\\}$, $\\max\\{X_2,\\,X_2+X_3,\\,\\ldots\\}$, $\\ldots$, $\\max\\{X_N,\\,X_N+X_{N+1},\\,\\ldots\\}$. The obtained formulas are based on a linear recurrence whose initial values are determined from a linear system that involves the roots of an associated characteristic equation and the distributions of $X_1,\\,X_2,\\,\\ldots,\\,X_N$. Several examples are presented, including a biseasonal-biased Rademacher random walk for which the distribution, generating functions, and all moments admit explicit closed-form expressions. In addition, we identify and correct several inaccuracies in the results reported in \\cite{Grigutis2024}." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
File size: 777 Bytes
8c06721 | 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 29 30 31 | {
"add_cross_attention": false,
"architectures": [
"BertModel"
],
"attention_probs_dropout_prob": 0.1,
"bos_token_id": null,
"classifier_dropout": null,
"dtype": "float32",
"eos_token_id": null,
"gradient_checkpointing": false,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 768,
"initializer_range": 0.02,
"intermediate_size": 3072,
"is_decoder": false,
"layer_norm_eps": 1e-12,
"max_position_embeddings": 512,
"model_type": "bert",
"num_attention_heads": 12,
"num_hidden_layers": 12,
"pad_token_id": 0,
"position_embedding_type": "absolute",
"tie_word_embeddings": true,
"transformers_version": "5.15.0",
"type_vocab_size": 2,
"use_cache": false,
"vocab_size": 31116
}
|