--- language: - en tags: - sentence-transformers - sentence-similarity - feature-extraction - dense - generated_from_trainer - dataset_size:70764 - loss:MultipleNegativesRankingLoss base_model: sentence-transformers/allenai-specter widget: - source_sentence: The hyperspace ω(f) when f is a transitive dendrite mapping sentences: - '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 closed and non-empty subsets of $X$ endowed with the Hausdorff metric. Let $f:X\to X$ be a continuous function. In this paper we study some topological properties of the hyperspace $\omega(f)$, the collection of all omega limits sets $\omega(x,f)$ with $x\in X$. We prove the following: $i)$ If $X$ has no isolated points, then, for every continuous function $f:X\to X$, $int_{2^X}(\omega(f))=\emptyset$. $ii)$ If $X$ is a dendrite for which every arc contains a free arc and $f:X\to X$ is transitive, then the hyperspace $\omega(f)$ is totally disconnected. $iii)$ Let $D_\infty$ be the Wazewski''s universal dendrite. Then there exists a transitive continuous function $f:D_\infty\to D_\infty$ for which the hyperspace $\omega(f)$ contains an arc; hence, $\omega(f)$ is not totally disconnected. ' - 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}. - source_sentence: New mechanism for delayed ENSO impact on the East Asian Summer Monsoon sentences: - Abstract Due to its potentially life—threatening and devastating economic impacts, variability in the East Asian Summer Monsoon needs better understanding, more accurate simulation and improved prediction. Much of the current ability of long-range summer monsoon forecasts stems from the link to the preceding winter El Niño Southern Oscillation, but the mechanisms behind this lagged impact are not fully understood. In this study, a simple new mechanism is proposed, based on migrating Atmospheric Angular Momentum anomalies. The polewards migration of atmospheric angular momentum associated with winter El Niño is shown to impact the northwest Pacific anticyclone responsible for much of the year-to-year variability in the monsoon. Model forecasts of the summer monsoon are shown to have increased accuracy when this new mechanism is active, with the increase in the success rate of forecasts due to migrating angular momentum anomalies shown to be as large as the effect of El Niño itself. - 'Automated algorithm selection in black-box optimization typically relies on supervised models that map landscape features to algorithm performance labels. Such models are costly to train, benchmark-dependent, and often fail to generalize to unseen problem classes. We study an unsupervised alternative: multi-kernel clustering over heterogeneous landscape representations, in which problem instances are grouped without using performance labels in the clustering stage, and the resulting clusters are mapped post hoc to solver recommendations through a strictly separated three-stage evaluation protocol. Drawing on two decades of advances in multiple kernel learning, we adopt a multi-kernel k-means formulation that jointly learns cluster assignments and kernel weights over four heterogeneous landscape views: ELA, DeepELA, DoE2Vec, and TransOptAS. On affine BBOB-derived selector tasks for Differential Evolution (DE) and Particle Swarm Optimization (PSO) at a fixed evaluation budget, we report mean plus or minus standard deviation selector profiles over 50 independent random seeds for stochastic configurations. Multi-kernel clustering obtains the strongest mean profile on the DE portfolio and remains competitive with, and nominally ahead of, the leading baselines on the more compressed PSO portfolio, where differences among the best methods are small relative to stochastic variation. In representative median-seed runs used for visualization, the learned kernel weights retain ELA and TransOptAS while assigning zero weight to DeepELA and DoE2Vec, providing a task-specific interpretation of which representations are retained by the multi-kernel model for selector-oriented grouping.' - Prolonged social isolation (SI) and negative emotion are associated with an increased risk of cardiovascular diseases (CVDs). However, it remains elusive whether SI and emotional states affect the pathological process of myocardial infarction (MI). In this study, SI models with different duration and MI model were co-established in mice. Anxiety and depression were assessed by a series of behavioral tests including open field test, elevated plus maze test, novelty-suppressed feeding test, tail suspension test and forced swim test. Cardiac function, heart infarct size and fibrosis were assessed by echocardiography, TTC staining, and Masson staining. The activity of neurons across the whole brain, as well as the hypothalamic-pituitary-adrenal (HPA) axis, was also investigated. Long-term SI induced anxiety and depression-like behaviors and aggravated cardiac injury and inflammatory response in MI mice, while short-term SI induced anxiety-like behavior but not depression-like behavior and had no significant effects on cardiac injury. Long-term SI altered the activity of several brain regions related to emotional, reward, autonomic and neuroendocrine regulation in MI mice. Furthermore, serum corticosterone levels were altered, indicating the potential involvement of HPA axis. These findings reveal that long-term SI exacerbated cardiac dysfunction and cardiac injury after MI. The underlying mechanisms involved in this process may include negative mood, dysregulation of the reward system, autonomic nervous system modulation, and HPA axis activation. These findings will contribute to our better understanding of heart-brain connections. - source_sentence: Towards a Theory of Dobrakov-Sobolev Spaces sentences: - The aim of this paper is to introduce a generalization of Sobolev spaces based on the Dobrakov integral. More precisely, we consider the setting of Banach-valued functions and Fomin differentiable Borel operator-valued measures on a finite-dimensional space. To build the necessary rigorous foundation, we establish analogs of several key results from the theory of differentiable real-valued measures, including the Leibniz rule and the integration by parts formula, all within the context of Dobrakov integration. These results are then embedded into the general scheme of vector-valued distribution theory. In particular, we describe the configuration of test spaces that yields an appropriate definition of a generalized derivative with respect to a differentiable operator-valued measure. - AI agents are increasingly used to automate research and development tasks, yet existing benchmarks typically evaluate them on prescribed workflows or narrow action spaces. Even nominally open-ended tasks can often be solved by retrieving a well-known recipe and tuning a few hyperparameters, making it unclear whether strong results reflect genuine optimization or memorized solutions. We introduce InferenceBench, where an agent must deploy an OpenAI-compatible inference server and optimize the speed of LLM inference. Each agent receives a target LLM, one H100 GPU, an optimization scenario, and a wall-clock time budget of two hours. Three optimization scenarios isolate distinct bottlenecks of inference (prefill latency, decode latency, and concurrent request throughput) and a fourth balances all three at the same time. Across 15 frontier agent configurations, agents reliably improve over a naive PyTorch baseline (up to $8.08\times$) and often match or exceed serving engines with default settings ($4.05\times$ for vLLM), but still fall below a simple hyperparameter search under the same time budget (up to $11.53\times$). Qualitative analysis of agent trajectories shows that although agents enumerate many relevant optimization techniques, they overwhelmingly converge on a single inference framework. They test only a few distinct configurations and spend the remaining budget re-measuring, repairing, or optimizing hyperparameters rather than exploring substantially different strategies. This suggests the bottleneck is not domain knowledge, but the ability to propose diverse configurations, evaluate them systematically, and submit the best identified solution. Overall, InferenceBench reflects the ability of agents to operate in an open-ended AI engineering setting, where memorized solutions lead to limited improvements. - Abstract Patients with multiple myeloma bearing a deletion of chromosome 17p (del(17p)), mutation of TP53 , or both have poorer prognosis compared to patients without these aberrations. We investigated the activity and mechanism of melflufen (melphalan flufenamide) in myeloma models with wild type TP53 ( TP53 wt) and complete TP53 deletion ( TP53 −/− ) and assessed the efficacy of melflufen in patients with del(17p) and/or TP53 mutation. Ex vivo data from myeloma plasma cells (PC) showed comparable activity of melflufen in del(17p), TP53 −/− , and TP53 wt samples. scRNAseq data demonstrated that melflufen sensitive PCs had lower expression of p53 target genes and higher expression of genes associated with DNA damage repair and cell cycle checkpoints. Irrespective of TP53 status, melflufen induced apoptosis, DNA damage, and mitochondrial dysfunction, while only in TP53 −/− cells, it led to changes in expression of cell cycle checkpoint and apoptosis genes. Post-hoc analysis of the OCEAN trial melflufen-treated del(17p) patient population also demonstrated favorable progression free survival compared to pomalidomide-treated cohort. Our insights into the molecular mechanisms of melflufen activity in TP53 −/− myeloma support its clinical efficacy and application in the del(17p) and TP53 −/− patient population. Trial registration NCT03151811, registration 2017-05-09. - source_sentence: 'Strengthening complexity in EU: the role of educational policy and R&D spending' sentences: - The drivers of economic complexity in the countries of the European Union (EU) are a question of significant importance, especially in the context of the emergence of a trade war. Studying the relationship between economic complexity and macroeconomic, educational and technological variables, we offer valuable conclusions for the design and formulation of new policies. The Economic Complexity Index (ECI) is analysed in the context of education expenditure, gross fixed capital formation, fiscal balance, R&D expenditure, and patent applications, using relevant econometric techniques such as the 3SLS and the SURE analysis. Education expenditures are found to affect significantly the Economic Complexity of a country. Moreover, after the outburst of the sovereign-debt crisis the fiscal balance has become a prerequisite for all European countries, showing a positive relationship with economic complexity. This result poses the question of how weaker economies can finance the necessary policies to increase their economic complexity, such as raising spending on education, R&D, and fixed capital investment, while maintaining fiscal stability. - 'Proxy metrics are widely used to improve the precision and velocity of online experimentation (aka A/B testing). Although proxies are often motivated by long-term outcomes that the experimenter does not observe, in many settings they are used alongside a contemporaneous but statistically insensitive north star. This can lead to a practical dilemma: when should experimenters trust the proxy metric, and when should they trust the north star? In this paper, I propose an optimal blending approach that smoothly guides decision-making towards the north star as the power of the experiment increases and away from the north star as the quality of the proxy metric improves. I study the implications of this decision-making framework for the design of experiments and of experimentation programs. Equipped with better (worse) proxy metrics, experimenters should run smaller and more (larger and fewer) experiments. I show how to leverage past experiments to estimate optimal blending weights and experiment sizes. Lastly, I describe the real-world application of the methodology to an experimentation program at Netflix.' - We consider a quenched SU(2)$\times$U(1) gauge Higgs theory on the lattice, coupled to a static vector-like fermion which, in this case, is in the same gauge group representation as the Higgs field. Physical (i.e. locally gauge invariant) electrically charged and electrically neutral states of matter particles in the electroweak theory were described decades ago, but those constructions do not exhaust all the possibilities, and new types of electrically charged/neutral states, orthogonal to former constructions, are described here. The difference has to do with how the static source, which by itself does not create a physical state, is dressed by dynamical fields. We find that, unsurprisingly, the neutral static fermion is much lighter than any of the charged fermion states. But a lattice study of the propagation of the charged fermion states indicates the existence of (at least) two particle states with different masses in charged particle spectrum. - source_sentence: ERBB3 blockade sensitizes hepatocellular carcinoma to regorafenib after first-line tyrosine kinase inhibitor resistance by inhibiting HIF1A-ABCB1 signaling sentences: - We improve the theoretical predictions for rare $Z$-boson decays, $Z\to V\ell^+\ell^-$ ($\ell=e$ or $\mu$), where $V$ denotes a heavy vector quarkonium including $J/\Psi$, $\Psi (2S)$, and $\Upsilon (nS)$ with $n=1,2,3$. These processes are thought to be dominated by the electromagnetic fragmentation transition, i.e., $Z\to \gamma^*\ell^+\ell^-$ followed by $\gamma^*\rightarrow V$. The present study includes all of the relevant tree-level Feynman diagrams, which contribute to these decays in the standard model. Our analysis shows that, for the charmonium final states, the fragmentation transition almost saturates the whole contribution and the other diagrams can be neglected; while for the bottomonium final states, the inclusion of other diagrams can increase their branching fractions by $4\%\sim 9\%$. Further investigation of the differential distributions, especially the angular distributions, indicates that forward-backward asymmetries for final leptons in these processes would be zero in the standard model. Therefore, in future experimental facilities with large number of $Z$-boson events accumulated, studies of these rare $Z$ decays may help both to test the standard model and to probe its interesting extensions. - 'Historically, veterinary studies screening for breed, age and sex predisposition to disease have relied on collating small-scale studies of clinical datasets. The availability of larger datasets through groups such as the Small Animal Veterinary Surveillance Network (SAVSNET) promise access to information regarding a wide range of clinical presentations at scale, however, methodological limitations surrounding the extraction of specific disease information or screening for disease predispositions result in a substantial reduction in the number of animals studied. These studies often address very focused hypotheses - only leveraging a small fraction of the intrinsic value of the data at any one time. Here, we implemented an unsupervised machine learning methodology, creating a representation of a large volume of clinical notes collected by SAVSNET from veterinary practices across the UK. We utilise BERTopic, a topic-modelling tool based on Bidirectional Encoder Representations using Transformers (BERT) architecture, and show it is able to surface known phenotypes, such as breed predispositions to hypoadrenocorticism, diabetes mellitus and mitral valve disease, as well as potential novel patterns of disease phenotypes. This scalable and granular modelling technique facilitates the rapid interrogation of large clinical datasets, enabling the identification of a broad range of phenotypes within the population and the early detection of temporal changes indicative of emerging infectious or environmental diseases. Supplementary Information: The online version contains supplementary material available at 10.1186/s40537-026-01365-0.' - 'BACKGROUND/AIMS: Regorafenib is recommended by guidelines and trials as a sequential second-line therapy following progression on first-line sorafenib or lenvatinib in hepatocellular carcinoma (HCC). However, efficacy is limited, highlighting the urgent need to screen suitable patients and develop sensitization strategies. METHODS: Acquired sorafenib- or lenvatinib-resistant (SR or LR) HCC cell lines and organoids were established. Genome-wide CRISPR library screen was performed in SR or LR cell strains to identify synthetic lethal targets of regorafenib. RNA-seq and FITC-regorafenib efflux assay were used to elucidate ERBB3-driven downstream signaling. Preclinical mouse models of cell line- and patient-derived xenografts and clinical cohorts of HCC patients were employed to validate the efficacy of ERBB3-guided patient stratification. RESULTS: Screening with CRISPR library, we showed that inhibition of ERBB3 was synthetic lethal with regorafenib in SR or LR cell strains and organoids. Mechanistically, SR or LR triggered feedback activation of ERBB3 signaling and mediated regorafenib efflux via ERBB3-HIF1A-ABCB1 cascade pathway, limiting sensitivity to regorafenib. Moreover, ERBB3-low tumors following SR or LR exhibited significant sensitivity to regorafenib, suggesting its potential as a predictive biomarker to screen optimal candidates for sequential therapy. Seribantumab, an ERBB3-targeting monoclonal antibody, inhibited ERBB3-HIF1A-ABCB1 cascade, and its combination with regorafenib exerted marked synergistic anti-tumor effects on ERBB3-high tumors resistant to sorafenib or lenvatinib both in vitro and in vivo. CONCLUSIONS: This study revealed that ERBB3 was a key resistance factor driving limited efficacy to sequential regorafenib, but also an effective therapeutic target whose inhibition enhanced regorafenib sensitivity after SR or LR.' pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - cosine_accuracy@1 - cosine_accuracy@3 - cosine_accuracy@5 - cosine_accuracy@10 - cosine_precision@1 - cosine_precision@3 - cosine_precision@5 - cosine_precision@10 - cosine_recall@1 - cosine_recall@3 - cosine_recall@5 - cosine_recall@10 - cosine_ndcg@10 - cosine_mrr@10 - cosine_map@100 model-index: - name: chowder-embed (SPECTER fine-tuned on the chowder corpus) results: - task: type: information-retrieval name: Information Retrieval dataset: name: NanoSCIDOCS type: NanoSCIDOCS metrics: - type: cosine_accuracy@1 value: 0.38 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.62 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.72 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.86 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.38 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.31333333333333335 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.256 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.176 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.08066666666666666 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.19466666666666665 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.26266666666666666 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.3616666666666666 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.3456136739010105 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.534436507936508 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.26477117793177524 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoSciFact type: NanoSciFact metrics: - type: cosine_accuracy@1 value: 0.6 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.68 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.72 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.8 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.24666666666666665 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.16 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.565 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.67 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.715 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.79 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.6831330092247734 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.6557142857142857 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.6496795415115648 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoNFCorpus type: NanoNFCorpus metrics: - type: cosine_accuracy@1 value: 0.3 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.36 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.46 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.54 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.3 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2533333333333333 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.248 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.222 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.012486154892961041 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.0309389720826588 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.047390511275329145 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.10215743728341801 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.24495153881224493 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.3605714285714285 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.09185350554401533 name: Cosine Map@100 - task: type: nano-beir name: Nano BEIR dataset: name: NanoBEIR mean type: NanoBEIR_mean metrics: - type: cosine_accuracy@1 value: 0.4266666666666667 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.5533333333333333 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.6333333333333333 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.7333333333333334 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.4266666666666667 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.27111111111111114 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.22133333333333335 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.16266666666666665 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.21938427385320922 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.29853521291644186 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.3416857259806653 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.4179413679833615 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.42456607397934293 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.5169074074074074 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.33543474166245174 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: chowder val type: chowder-val metrics: - type: cosine_accuracy@1 value: 0.952 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.9805 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.989 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9955 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.952 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.32683333333333325 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.1978 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09955000000000001 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.952 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.9805 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.989 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9955 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.9743050654346748 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.9674267857142853 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.9676369980648283 name: Cosine Map@100 --- # chowder-embed (SPECTER fine-tuned on the chowder corpus) This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/allenai-specter](https://huggingface.co/sentence-transformers/allenai-specter) on the parquet dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, classification, clustering, and more. ## Model Details ### Model Description - **Model Type:** Sentence Transformer - **Base model:** [sentence-transformers/allenai-specter](https://huggingface.co/sentence-transformers/allenai-specter) - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 768 dimensions - **Similarity Function:** Cosine Similarity - **Supported Modality:** Text - **Training Dataset:** - parquet - **Language:** en ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'transformer_task': 'feature-extraction', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'last_hidden_state'}}, 'module_output_name': 'token_embeddings', 'architecture': 'BertModel'}) (1): Pooling({'embedding_dimension': 768, 'pooling_mode': 'cls', 'include_prompt': True}) ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("Wakeleyhuh/chowder-embed-specter") # Run inference queries = [ 'ERBB3 blockade sensitizes hepatocellular carcinoma to regorafenib after first-line tyrosine kinase inhibitor resistance by inhibiting HIF1A-ABCB1 signaling', ] documents = [ 'BACKGROUND/AIMS: Regorafenib is recommended by guidelines and trials as a sequential second-line therapy following progression on first-line sorafenib or lenvatinib in hepatocellular carcinoma (HCC). However, efficacy is limited, highlighting the urgent need to screen suitable patients and develop sensitization strategies. METHODS: Acquired sorafenib- or lenvatinib-resistant (SR or LR) HCC cell lines and organoids were established. Genome-wide CRISPR library screen was performed in SR or LR cell strains to identify synthetic lethal targets of regorafenib. RNA-seq and FITC-regorafenib efflux assay were used to elucidate ERBB3-driven downstream signaling. Preclinical mouse models of cell line- and patient-derived xenografts and clinical cohorts of HCC patients were employed to validate the efficacy of ERBB3-guided patient stratification. RESULTS: Screening with CRISPR library, we showed that inhibition of ERBB3 was synthetic lethal with regorafenib in SR or LR cell strains and organoids. Mechanistically, SR or LR triggered feedback activation of ERBB3 signaling and mediated regorafenib efflux via ERBB3-HIF1A-ABCB1 cascade pathway, limiting sensitivity to regorafenib. Moreover, ERBB3-low tumors following SR or LR exhibited significant sensitivity to regorafenib, suggesting its potential as a predictive biomarker to screen optimal candidates for sequential therapy. Seribantumab, an ERBB3-targeting monoclonal antibody, inhibited ERBB3-HIF1A-ABCB1 cascade, and its combination with regorafenib exerted marked synergistic anti-tumor effects on ERBB3-high tumors resistant to sorafenib or lenvatinib both in vitro and in vivo. CONCLUSIONS: This study revealed that ERBB3 was a key resistance factor driving limited efficacy to sequential regorafenib, but also an effective therapeutic target whose inhibition enhanced regorafenib sensitivity after SR or LR.', 'We improve the theoretical predictions for rare $Z$-boson decays, $Z\\to V\\ell^+\\ell^-$ ($\\ell=e$ or $\\mu$), where $V$ denotes a heavy vector quarkonium including $J/\\Psi$, $\\Psi (2S)$, and $\\Upsilon (nS)$ with $n=1,2,3$. These processes are thought to be dominated by the electromagnetic fragmentation transition, i.e., $Z\\to \\gamma^*\\ell^+\\ell^-$ followed by $\\gamma^*\\rightarrow V$. The present study includes all of the relevant tree-level Feynman diagrams, which contribute to these decays in the standard model. Our analysis shows that, for the charmonium final states, the fragmentation transition almost saturates the whole contribution and the other diagrams can be neglected; while for the bottomonium final states, the inclusion of other diagrams can increase their branching fractions by $4\\%\\sim 9\\%$. Further investigation of the differential distributions, especially the angular distributions, indicates that forward-backward asymmetries for final leptons in these processes would be zero in the standard model. Therefore, in future experimental facilities with large number of $Z$-boson events accumulated, studies of these rare $Z$ decays may help both to test the standard model and to probe its interesting extensions.', 'Historically, veterinary studies screening for breed, age and sex predisposition to disease have relied on collating small-scale studies of clinical datasets. The availability of larger datasets through groups such as the Small Animal Veterinary Surveillance Network (SAVSNET) promise access to information regarding a wide range of clinical presentations at scale, however, methodological limitations surrounding the extraction of specific disease information or screening for disease predispositions result in a substantial reduction in the number of animals studied. These studies often address very focused hypotheses - only leveraging a small fraction of the intrinsic value of the data at any one time. Here, we implemented an unsupervised machine learning methodology, creating a representation of a large volume of clinical notes collected by SAVSNET from veterinary practices across the UK. We utilise BERTopic, a topic-modelling tool based on Bidirectional Encoder Representations using Transformers (BERT) architecture, and show it is able to surface known phenotypes, such as breed predispositions to hypoadrenocorticism, diabetes mellitus and mitral valve disease, as well as potential novel patterns of disease phenotypes. This scalable and granular modelling technique facilitates the rapid interrogation of large clinical datasets, enabling the identification of a broad range of phenotypes within the population and the early detection of temporal changes indicative of emerging infectious or environmental diseases. Supplementary Information: The online version contains supplementary material available at 10.1186/s40537-026-01365-0.', ] query_embeddings = model.encode_query(queries) document_embeddings = model.encode_document(documents) print(query_embeddings.shape, document_embeddings.shape) # [1, 768] [3, 768] # Get the similarity scores for the embeddings similarities = model.similarity(query_embeddings, document_embeddings) print(similarities) # tensor([[0.8640, 0.0814, 0.0283]]) ``` ## Evaluation ### Metrics #### Information Retrieval * Datasets: `NanoSCIDOCS`, `NanoSciFact`, `NanoNFCorpus` and `chowder-val` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.sentence_transformer.evaluation.InformationRetrievalEvaluator) | Metric | NanoSCIDOCS | NanoSciFact | NanoNFCorpus | chowder-val | |:--------------------|:------------|:------------|:-------------|:------------| | cosine_accuracy@1 | 0.38 | 0.6 | 0.3 | 0.952 | | cosine_accuracy@3 | 0.62 | 0.68 | 0.36 | 0.9805 | | cosine_accuracy@5 | 0.72 | 0.72 | 0.46 | 0.989 | | cosine_accuracy@10 | 0.86 | 0.8 | 0.54 | 0.9955 | | cosine_precision@1 | 0.38 | 0.6 | 0.3 | 0.952 | | cosine_precision@3 | 0.3133 | 0.2467 | 0.2533 | 0.3268 | | cosine_precision@5 | 0.256 | 0.16 | 0.248 | 0.1978 | | cosine_precision@10 | 0.176 | 0.09 | 0.222 | 0.0996 | | cosine_recall@1 | 0.0807 | 0.565 | 0.0125 | 0.952 | | cosine_recall@3 | 0.1947 | 0.67 | 0.0309 | 0.9805 | | cosine_recall@5 | 0.2627 | 0.715 | 0.0474 | 0.989 | | cosine_recall@10 | 0.3617 | 0.79 | 0.1022 | 0.9955 | | **cosine_ndcg@10** | **0.3456** | **0.6831** | **0.245** | **0.9743** | | cosine_mrr@10 | 0.5344 | 0.6557 | 0.3606 | 0.9674 | | cosine_map@100 | 0.2648 | 0.6497 | 0.0919 | 0.9676 | #### Nano BEIR * Dataset: `NanoBEIR_mean` * Evaluated with [NanoBEIREvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.sentence_transformer.evaluation.NanoBEIREvaluator) with these parameters: ```json { "dataset_names": [ "scidocs", "scifact", "nfcorpus" ], "dataset_id": "sentence-transformers/NanoBEIR-en" } ``` | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.4267 | | cosine_accuracy@3 | 0.5533 | | cosine_accuracy@5 | 0.6333 | | cosine_accuracy@10 | 0.7333 | | cosine_precision@1 | 0.4267 | | cosine_precision@3 | 0.2711 | | cosine_precision@5 | 0.2213 | | cosine_precision@10 | 0.1627 | | cosine_recall@1 | 0.2194 | | cosine_recall@3 | 0.2985 | | cosine_recall@5 | 0.3417 | | cosine_recall@10 | 0.4179 | | **cosine_ndcg@10** | **0.4246** | | cosine_mrr@10 | 0.5169 | | cosine_map@100 | 0.3354 | ## Training Details ### Training Dataset #### parquet * Dataset: parquet * Size: 70,764 training samples * Columns: anchor and positive * Approximate statistics based on the first 100 samples: | | anchor | positive | |:---------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------| | type | string | string | | modality | text | text | | details | | | * Samples: | anchor | positive | |:------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Los-mamba: A low-rank recursive mamba framework for mitigating stationary bias in trajectory prediction | Trajectory prediction plays a key role in autonomous driving and intelligent transportation systems. Mamba performs well in modeling long sequences but struggles with short-term static or local motion features. In this paper, we propose the Lo w-rank Recur s ive Mamba framework ( Los-Mamba ). It addresses prediction bias caused by inadequate modeling of local static features in trajectory prediction tasks. Firstly, Los-Mamba uses hybrid Mamba branches to capture both global motion trends and local features. Secondly, it reduces trajectory feature dimensionality through low-rank approximation, simplifying computational complexity. Thirdly, Los-Mamba also includes a spatio-temporal constraint mechanism. It uses position coding and a multilayer perceptron (MLP) to interpolate static and dynamic trajectories, improving the representation of local features. Experiments show that Los-Mamba outperforms baseline models on datasets like ETH and Hotel. Its average displacement error (ADE) and fi... | | The Universal Language of CSI:Unifying Wireless Sensing Across Devices and Environments | WiFi sensing based on Channel State Information (CSI) promises ubiquitous, device-free perception, yet current research remains trapped in a Tower of Babel - fragmented into isolated silos where models are tailored to specific hardware dialects, fixed environments, and narrow tasks. The primary bottleneck is the Heterogeneity Gap: the disparity in signal dimensions, sampling rates, and semantic labels that prevents cross-system understanding. To bridge this gap, we propose a foundation-model framework that treats CSI not merely as raw signals but as a structured language with a learnable universal grammar. We first curate and standardize a large collection of heterogeneous real-world CSI datasets, establishing a unified infrastructure that allows incompatible signal formats to be treated as a single corpus. Second, we introduce a modular architecture that acts as a universal translator where lightweight dataset-specific adapters tokenize diverse signal inputs into a shared latent vocab... | | Long-term follow-up of the transplant-eligible cohort of the EMN12/HOVON-129 study for primary plasma cell leukemia patients | Primary plasma cell leukemia (pPCL) is a rare and aggressive plasma cell disorder [ 1 , 2 ]. Compared to multiple myeloma (MM), pPCL is characterized by a higher frequency of poor-risk features, such as high-risk cytogenetic abnormalities (HRCAs) and elevated LDH [ 1 , 2 ]. Although MM treatment has improved, the survival of pPCL patients remains poor [ 1 , 2 ]. We therefore initiated the phase 2 EMN12/HOVON-129 study for patients with newly diagnosed pPCL, which showed that carfilzomib and lenalidomide-based therapy in conjunction with autologous stem-cell-transplantation (ASCT) provides efficient disease control [ 3 ]. Here, we present the long-term follow-up of patients aged 18–65 years included in the EMN12/HOVON-129 study. We also aimed to evaluate if patients with HRCAs, elevated LDH, or other high-risk features have a worse clinical outcome. Inclusion criteria were newly diagnosed pPCL (defined as >2 × 10 9 /L circulating plasma cells or >20% plasma cells in peripheral blood) an... | * Loss: [MultipleNegativesRankingLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim", "gather_across_devices": false, "directions": [ "query_to_doc" ], "partition_mode": "joint", "hardness_mode": null, "hardness_strength": 0.0 } ``` ### Evaluation Dataset #### parquet * Dataset: parquet * Size: 2,000 evaluation samples * Columns: anchor and positive * Approximate statistics based on the first 100 samples: | | anchor | positive | |:---------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------| | type | string | string | | modality | text | text | | details | | | * Samples: | anchor | positive | |:---------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Boolean Algebra -- Driven Sepsis Diagnosis | Sepsis remains a diagnostic challenge due to its heterogeneous molecular signatures and complex immune responses. In this study, we develop a logical data analysis framework based on Boolean polynomial rings. This method constructs an ideal $\mathcal{I}$ of selection criteria that isolate empty subsets of previously analyzed patient data. This approach enables the derivation of interpretable classification rules based on biomarker profiles. We demonstrate that logical data analysis identifies distinct logical patterns for positive and negative sepsis classification. For instance, elevated levels of GLP-1 and MyD88 are associated with septic states in our dataset, whereas high TRAIL and low MyD88 concentrations may suggest a non-septic condition. Importantly, a new way to integrate expert knowledge to filter out potential overfitting or dataset-specific artifacts is shown. Our findings highlight the utility of logics in generating transparent, biologically plausible rules for a data-bas... | | Notes on the bootstrap of four-point conformal integrals | We set up a bootstrap workflow to study four-point conformal integrals in position space, using leading singularities, single-valued multiple polylogarithmic ans\"atze and boundary data from expansion by regions. These four-point conformal integrals are general in the sense that they are generated by the four-point projections of all possible $f$-graphs, including all non-planar $f$-graph sectors. For three-loop cases, fourteen of the fifteen inequivalent integrand basis can be directly calculated by \texttt{HyperlogProcedures} and the last one is fixed by Gram identity. Then we concentrate on how far the bootstrap workflow can go for four-loop cases, though it works for three-loop cases as well. We show that integrals with several leading singularities can be made tractable by decomposing them into pieces with simpler cut structure. Some four-loop integrals which can not be calculated or very hard to be calculated by other methods for now are obtained in this way. We also provide a pa... | | Clean2FX: Label-conditioned modeling for clean-to-effect guitar audio transformations | We present Clean2FX, a study and demo of label-conditioned clean-to-effect transformation for electric guitar audio. Given a clean guitar input and a target effect label, the task is to synthesize the corresponding effected signal while preserving the musical content. Training and evaluation pairs are constructed from EGFxSet real, single tone recordings by assembling matched clean/effected chords, melodies, and mixed timelines. This allows for controlled comparison across effects. We evaluate four neural approaches under a common spectrogram-based transformation setting: two variational autoencoders and two U-Net models that differ in whether they operate on linear or log-magnitude representations. Performance is measured using linear-magnitude spectrogram MSE and Fr\'echet Audio Distance. The U-Net models outperform the variational autoencoder variants. Per-effect results show that distortion effects are most readily improved, whereas delay and reverb effects exhibit weaker FAD gains... | * Loss: [MultipleNegativesRankingLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim", "gather_across_devices": false, "directions": [ "query_to_doc" ], "partition_mode": "joint", "hardness_mode": null, "hardness_strength": 0.0 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `per_device_train_batch_size`: 16 - `learning_rate`: 2e-05 - `warmup_steps`: 0.1 - `weight_decay`: 0.01 - `fp16`: True - `per_device_eval_batch_size`: 16 - `load_best_model_at_end`: True - `seed`: 12 - `batch_sampler`: no_duplicates #### All Hyperparameters
Click to expand - `per_device_train_batch_size`: 16 - `num_train_epochs`: 3 - `max_steps`: -1 - `learning_rate`: 2e-05 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: None - `warmup_steps`: 0.1 - `optim`: adamw_torch_fused - `optim_args`: None - `weight_decay`: 0.01 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `optim_target_modules`: None - `gradient_accumulation_steps`: 1 - `average_tokens_across_devices`: True - `max_grad_norm`: 1.0 - `label_smoothing_factor`: 0.0 - `bf16`: False - `fp16`: True - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `use_liger_kernel`: False - `liger_kernel_config`: None - `use_cache`: False - `neftune_noise_alpha`: None - `torch_empty_cache_steps`: None - `auto_find_batch_size`: False - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `include_num_input_tokens_seen`: no - `log_level`: passive - `log_level_replica`: warning - `disable_tqdm`: False - `project`: huggingface - `trackio_space_id`: None - `trackio_bucket_id`: None - `trackio_static_space_id`: None - `per_device_eval_batch_size`: 16 - `prediction_loss_only`: True - `eval_on_start`: False - `eval_do_concat_batches`: True - `eval_use_gather_object`: False - `eval_accumulation_steps`: None - `include_for_metrics`: [] - `batch_eval_metrics`: False - `save_only_model`: False - `save_on_each_node`: False - `enable_jit_checkpoint`: False - `push_to_hub`: False - `hub_private_repo`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_always_push`: False - `hub_revision`: None - `load_best_model_at_end`: True - `ignore_data_skip`: False - `restore_callback_states_from_checkpoint`: False - `full_determinism`: False - `seed`: 12 - `data_seed`: None - `use_cpu`: False - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `parallelism_config`: None - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `dataloader_prefetch_factor`: None - `dataloader_multiprocessing_context`: None - `dataloader_in_order`: True - `remove_unused_columns`: True - `label_names`: None - `train_sampling_strategy`: random - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `ddp_static_graph`: None - `ddp_backend`: None - `ddp_timeout`: 1800 - `fsdp`: None - `fsdp_config`: None - `deepspeed`: None - `debug`: [] - `skip_memory_metrics`: True - `do_predict`: False - `resume_from_checkpoint`: None - `local_rank`: -1 - `prompts`: None - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional - `router_mapping`: {} - `learning_rate_mapping`: {} - `warmup_ratio`: None
### Training Logs | Epoch | Step | Training Loss | Validation Loss | NanoSCIDOCS_cosine_ndcg@10 | NanoSciFact_cosine_ndcg@10 | NanoNFCorpus_cosine_ndcg@10 | NanoBEIR_mean_cosine_ndcg@10 | chowder-val_cosine_ndcg@10 | |:-------:|:--------:|:-------------:|:---------------:|:--------------------------:|:--------------------------:|:---------------------------:|:----------------------------:|:--------------------------:| | -1 | -1 | - | - | 0.3211 | 0.6251 | 0.1796 | 0.3753 | 0.8622 | | 0.0002 | 1 | 0.1790 | - | - | - | - | - | - | | 0.0301 | 133 | 0.1970 | - | - | - | - | - | - | | 0.0601 | 266 | 0.0910 | - | - | - | - | - | - | | 0.0902 | 399 | 0.0571 | - | - | - | - | - | - | | 0.1203 | 532 | 0.0445 | - | - | - | - | - | - | | 0.1504 | 665 | 0.0331 | - | - | - | - | - | - | | 0.1804 | 798 | 0.0311 | - | - | - | - | - | - | | 0.2105 | 931 | 0.0304 | - | - | - | - | - | - | | 0.2406 | 1064 | 0.0234 | - | - | - | - | - | - | | 0.2706 | 1197 | 0.0229 | - | - | - | - | - | - | | 0.3000 | 1327 | - | 0.0102 | 0.3527 | 0.6570 | 0.2448 | 0.4182 | - | | 0.3007 | 1330 | 0.0225 | - | - | - | - | - | - | | 0.3308 | 1463 | 0.0199 | - | - | - | - | - | - | | 0.3608 | 1596 | 0.0220 | - | - | - | - | - | - | | 0.3909 | 1729 | 0.0143 | - | - | - | - | - | - | | 0.4210 | 1862 | 0.0186 | - | - | - | - | - | - | | 0.4511 | 1995 | 0.0136 | - | - | - | - | - | - | | 0.4811 | 2128 | 0.0191 | - | - | - | - | - | - | | 0.5112 | 2261 | 0.0151 | - | - | - | - | - | - | | 0.5413 | 2394 | 0.0220 | - | - | - | - | - | - | | 0.5713 | 2527 | 0.0170 | - | - | - | - | - | - | | **0.6** | **2654** | **-** | **0.0069** | **0.3455** | **0.6831** | **0.245** | **0.4245** | **-** | | 0.6014 | 2660 | 0.0150 | - | - | - | - | - | - | | 0.6315 | 2793 | 0.0174 | - | - | - | - | - | - | | 0.6615 | 2926 | 0.0123 | - | - | - | - | - | - | | 0.6916 | 3059 | 0.0178 | - | - | - | - | - | - | | 0.7217 | 3192 | 0.0182 | - | - | - | - | - | - | | 0.7518 | 3325 | 0.0151 | - | - | - | - | - | - | | 0.7818 | 3458 | 0.0143 | - | - | - | - | - | - | | 0.8119 | 3591 | 0.0124 | - | - | - | - | - | - | | 0.8420 | 3724 | 0.0165 | - | - | - | - | - | - | | 0.8720 | 3857 | 0.0134 | - | - | - | - | - | - | | 0.9001 | 3981 | - | 0.0047 | 0.3545 | 0.6515 | 0.2247 | 0.4102 | - | | 0.9021 | 3990 | 0.0119 | - | - | - | - | - | - | | 0.9322 | 4123 | 0.0156 | - | - | - | - | - | - | | 0.9622 | 4256 | 0.0127 | - | - | - | - | - | - | | 0.9923 | 4389 | 0.0154 | - | - | - | - | - | - | | 1.0224 | 4522 | 0.0091 | - | - | - | - | - | - | | 1.0525 | 4655 | 0.0034 | - | - | - | - | - | - | | 1.0825 | 4788 | 0.0065 | - | - | - | - | - | - | | 1.1126 | 4921 | 0.0072 | - | - | - | - | - | - | | 1.1427 | 5054 | 0.0041 | - | - | - | - | - | - | | 1.1727 | 5187 | 0.0065 | - | - | - | - | - | - | | 1.2001 | 5308 | - | 0.0032 | 0.3672 | 0.6294 | 0.2201 | 0.4055 | - | | 1.2028 | 5320 | 0.0055 | - | - | - | - | - | - | | 1.2329 | 5453 | 0.0038 | - | - | - | - | - | - | | 1.2629 | 5586 | 0.0043 | - | - | - | - | - | - | | 1.2930 | 5719 | 0.0040 | - | - | - | - | - | - | | 1.3231 | 5852 | 0.0038 | - | - | - | - | - | - | | 1.3532 | 5985 | 0.0069 | - | - | - | - | - | - | | 1.3832 | 6118 | 0.0063 | - | - | - | - | - | - | | 1.4133 | 6251 | 0.0052 | - | - | - | - | - | - | | 1.4434 | 6384 | 0.0068 | - | - | - | - | - | - | | 1.4734 | 6517 | 0.0047 | - | - | - | - | - | - | | 1.5001 | 6635 | - | 0.0032 | 0.3589 | 0.6404 | 0.1966 | 0.3986 | - | | -1 | -1 | - | - | 0.3456 | 0.6831 | 0.2450 | 0.4246 | 0.9743 | * The bold row denotes the saved checkpoint. ### Training Time - **Training**: 44.8 minutes - **Evaluation**: 3.9 minutes - **Total**: 48.7 minutes ### Framework Versions - Python: 3.14.7 - Sentence Transformers: 5.7.0 - Transformers: 5.15.0 - PyTorch: 2.13.0+cu130 - Accelerate: 1.14.0 - Datasets: 5.0.1 - Tokenizers: 0.22.2 ## Additional Resources - [Training and Finetuning Embedding Models with Sentence Transformers](https://huggingface.co/blog/train-sentence-transformers): the end-to-end guide for training or finetuning Sentence Transformer models. - [Introduction to Matryoshka Embedding Models](https://huggingface.co/blog/matryoshka): variable-size embeddings that can be truncated with minimal quality loss. - [Binary and Scalar Embedding Quantization for Significantly Faster & Cheaper Retrieval](https://huggingface.co/blog/embedding-quantization): post-training compression of embedding vectors. - [Multimodal Embedding & Reranker Models with Sentence Transformers](https://huggingface.co/blog/multimodal-sentence-transformers): use text, image, audio, and video models through the same API. - [Training and Finetuning Multimodal Embedding & Reranker Models with Sentence Transformers](https://huggingface.co/blog/train-multimodal-sentence-transformers): train multimodal embedding models, with a Visual Document Retrieval walkthrough. ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### MultipleNegativesRankingLoss ```bibtex @misc{oord2019representationlearningcontrastivepredictive, title={Representation Learning with Contrastive Predictive Coding}, author={Aaron van den Oord and Yazhe Li and Oriol Vinyals}, year={2019}, eprint={1807.03748}, archivePrefix={arXiv}, primaryClass={cs.LG}, url={https://arxiv.org/abs/1807.03748}, } ```