SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2

Model Details

Model Description

• Model Type: Sentence Transformer
• Base model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
• Maximum Sequence Length: 128 tokens
• Output Dimensionality: 384 dimensions
• Similarity Function: Cosine Similarity

Model Sources

• Documentation: Sentence Transformers Documentation
• Repository: Sentence Transformers on GitHub
• Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)

Usage

Direct Usage (Sentence Transformers)

Evaluation

Metrics

Triplet

• Dataset: triplet_eval
• Evaluated with TripletEvaluator

Binary Classification

• Dataset: binary_eval
• Evaluated with BinaryClassificationEvaluator

Training Details

Training Datasets

Unnamed Dataset

• Size: 12,800 training samples
• Columns: sentence_0, sentence_1, and sentence_2
• Approximate statistics based on the first 1000 samples:

  	sentence_0	sentence_1	sentence_2
  type	string	string	string
  details	min: 21 tokens mean: 39.66 tokens max: 79 tokens	min: 37 tokens mean: 126.26 tokens max: 128 tokens	min: 40 tokens mean: 125.44 tokens max: 128 tokens

• Samples:

  sentence_0	sentence_1	sentence_2
  Làm thế nào để đánh giá hiệu quả của các thuật toán nâng cao chất lượng ảnh trong điều kiện thực tế?	Over the past decades, various super-resolution (SR) techniques have been developed to enhance the spatial resolution of digital images. Despite the great number of methodical contributions, there is still a lack of comparative validations of SR under practical conditions, as capturing real ground truth data is a challenging task. Therefore, current studies are either evaluated 1) on simulated data or 2) on real data without a pixel-wise ground truth.[object Object]To facilitate comprehensive studies, this paper introduces the publicly available Super-Resolution Erlangen (SupER) database that includes real low-resolution images along with high-resolution ground truth data. Our database comprises image sequences with more than 20k images captured from 14 scenes under various types of motions and photometric conditions. The datasets cover four spatial resolution levels using camera hardware binning. With this database, we benchmark 15 single-image and multi-frame SR algorithms. Our experiments quantit...	Deep learning is ubiquitous across many areas areas of computer vision. It often requires large scale datasets for training before being fine-tuned on small-to-medium scale problems. Activity, or, in other words, action recognition, is one of many application areas of deep learning. While there exist many Convolutional Neural Network architectures that work with the RGB and optical flow frames, training on the time sequences of 3D body skeleton joints is often performed via recurrent networks such as LSTM.[object Object]In this paper, we propose a new representation which encodes sequences of 3D body skeleton joints in texture-like representations derived from mathematically rigorous kernel methods. Such a representation becomes the first layer in a standard CNN network e.g., ResNet-50, which is then used in the supervised domain adaptation pipeline to transfer information from the source to target dataset. This lets us leverage the available Kinect-based data beyond training on a single dataset and...
  Làm thế nào để xử lý lượng lớn dữ liệu thực thể một cách hiệu quả mà vẫn đảm bảo độ chính xác khi thời gian và tài nguyên tính toán bị giới hạn Trong điều kiện phải làm việc với các nguồn dữ liệu không đồng nhất, liệu có phương pháp nào có thể áp dụng lược đồ linh hoạt để cải thiện hiệu suất?	Entity Resolution (ER) is the task of finding entity profiles that correspond to the same real-world entity. Progressive ER aims to efficiently resolve large datasets when limited time and/or computational resources are available. In practice, its goal is to provide the best possible partial solution by approximating the optimal comparison order of the entity profiles. So far, Progressive ER has only been examined in the context of structured (relational) data sources, as the existing methods rely on schema knowledge to save unnecessary comparisons: they restrict their search space to similar entities with the help of schema-based blocking keys (i.e., signatures that represent the entity profiles). As a result, these solutions are not applicable in Big Data integration applications, which involve large and heterogeneous datasets, such as relational and RDF databases, JSON files, Web corpus etc. To cover this gap, we propose a family of schema-agnostic Progressive ER methods, which do n...	Pattern matching on large graphs is the foundation for a variety of application domains. Strict latency requirements and continuously increasing graph sizes demand the usage of highly parallel in-memory graph processing engines that need to consider non-uniform memory access (NUMA) and concurrency issues to scale up on modern multiprocessor systems. To tackle these aspects, graph partitioning becomes increasingly important. Hence, we present a technique to process graph pattern matching on NUMA systems in this paper. As a scalable pattern matching processing infrastructure, we leverage a data-oriented architecture that preserves data locality and minimizes concurrency-related bottlenecks on NUMA systems. We show in detail, how graph pattern matching can be asynchronously processed on a multiprocessor system.
  Làm thế nào để tối ưu hóa việc truyền dữ liệu từ các thiết bị theo dõi động vật trong điều kiện băng thông hạn chế mà vẫn đảm bảo độ chính xác của thông tin sinh thái học?	Bio-loggers, electronic devices used to track animal behaviour through various sensors, have become essential in wildlife research.[object Object]Despite continuous improvements in their capabilities, bio-loggers still face significant limitations in storage, processing, and data transmission due to the constraints of size and weight, which are necessary to avoid disturbing the animals.[object Object]This study aims to explore how selective data transmission, guided by machine learning, can reduce the energy consumption of bio-loggers, thereby extending their operational lifespan without requiring hardware modifications.	T-distributed stochastic neighbor embedding (tSNE) is a popular and prize-winning approach for dimensionality reduction and visualizing high-dimensional data. However, tSNE is non-parametric: once visualization is built, tSNE is not designed to incorporate additional data into existing representation. It highly limits the applicability of tSNE to the scenarios where data are added or updated over time (like dashboards or series of data snapshots).[object Object]In this paper we propose, analyze and evaluate LION-tSNE (Local Interpolation with Outlier coNtrol) - a novel approach for incorporating new data into tSNE representation. LION-tSNE is based on local interpolation in the vicinity of training data, outlier detection and a special outlier mapping algorithm. We show that LION-tSNE method is robust both to outliers and to new samples from existing clusters. We also discuss multiple possible improvements for special cases.[object Object]We compare LION-tSNE to a comprehensive list of possible benchmark approach...

• Loss: TripletLoss with these parameters:
  json

  1{ 2 "distance_metric": "TripletDistanceMetric.COSINE", 3 "triplet_margin": 0.5 4}

Unnamed Dataset

• Size: 25,600 training samples
• Columns: sentence_0, sentence_1, and label
• Approximate statistics based on the first 1000 samples:

  	sentence_0	sentence_1	label
  type	string	string	float
  details	min: 21 tokens mean: 40.09 tokens max: 80 tokens	min: 28 tokens mean: 126.39 tokens max: 128 tokens	min: 0.0 mean: 0.52 max: 1.0

• Samples:

  sentence_0	sentence_1	label
  Làm thế nào để tối ưu hóa thuật toán tìm đường đi ngắn nhất trong đồ thị có trọng số khi thời gian tính toán trọng số cạnh là yếu tố quan trọng cần xem xét?	The shortest path problem in graphs is fundamental to AI. Nearly all variants of the problem and relevant algorithms that solve them ignore edge-weight computation time and its common relation to weight uncertainty. This implies that taking these factors into consideration can potentially lead to a performance boost in relevant applications. Recently, a generalized framework for weighted directed graphs was suggested, where edge-weight can be computed (estimated) multiple times, at increasing accuracy and run-time expense. We build on this framework to introduce the problem of finding the tightest admissible shortest path (TASP); a path with the tightest suboptimality bound on the optimal cost. This is a generalization of the shortest path problem to bounded uncertainty, where edge-weight uncertainty can be traded for computational cost. We present a complete algorithm for solving TASP, with guarantees on solution quality. Empirical evaluation supports the effectiveness of this approac...	1.0
  Làm thế nào để thiết kế bộ nhân xấp xỉ tiết kiệm năng lượng cho các thiết bị IoT khi yêu cầu độ chính xác có thể linh hoạt và tài nguyên phần cứng bị giới hạn?	Given the stringent requirements of energy efficiency for Internet-of-Things edge devices, approximate multipliers, as a basic component of many processors and accelerators, have been constantly proposed and studied for decades, especially in error-resilient applications. The computation error and energy efficiency largely depend on how and where the approximation is introduced into a design. Thus, this article aims to provide a comprehensive review of the approximation techniques in multiplier designs ranging from algorithms and architectures to circuits. We have implemented representative approximate multiplier designs in each category to understand the impact of the design techniques on accuracy and efficiency. The designs can then be effectively deployed in high-level applications, such as machine learning, to gain energy efficiency at the cost of slight accuracy loss.	1.0
  Làm thế nào để cải thiện tính tự nhiên trong giọng nói tổng hợp khi hệ thống hiện tại thường tạo ra ngữ điệu đơn điệu do phụ thuộc vào dữ liệu huấn luyện trung bình?	This work presents a SystemC-TLM based simulator for a RISC-V microcontroller. This simulator is focused on simplicity and easy expandable of a RISC-V. It is built around a full RISC-V instruction set simulator that supports full RISC-V ISA and extensions M, A, C, Zicsr and Zifencei. The ISS is encapsulated in a TLM-2 wrapper that enables it to communicate with any other TLM-2 compatible module. The simulator also includes a very basic set of peripherals to enable a complete SoC simulator. The running code can be compiled with standard tools and using standard C libraries without modifications. The simulator is able to correctly execute the riscv-compliance suite. The entire simulator is published as a docker image to ease its installation and use by developers. A porting of FreeRTOSv10.2.1 for the simulated SoC is also published.	0.0

• Loss: ContrastiveLoss with these parameters:
  json

  1{ 2 "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE", 3 "margin": 0.5, 4 "size_average": true 5}

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: steps
• per_device_train_batch_size: 64
• per_device_eval_batch_size: 64
• num_train_epochs: 1
• multi_dataset_batch_sampler: round_robin

All Hyperparameters

Training Logs

Framework Versions

• Python: 3.11.11
• Sentence Transformers: 3.4.1
• Transformers: 4.51.1
• PyTorch: 2.5.1+cu124
• Accelerate: 1.3.0
• Datasets: 3.5.0
• Tokenizers: 0.21.0

Citation

BibTeX

Sentence Transformers

TripletLoss

ContrastiveLoss