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1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "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
+}

README.md

@@ -1,3 +1,647 @@
 ---
-license: mit
+base_model: nreimers/MiniLM-L6-H384-uncased
+datasets: []
+language: []
+library_name: sentence-transformers
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:730454
+- loss:MultipleNegativesRankingLoss
+widget:
+- source_sentence: Markov chains and performance comparison of switched diversity
+    systems
+  sentences:
+  - An algorithm for speaker's lip segmentation and features extraction is presented.
+    A color video sequence of speaker's face is acquired, under natural lighting conditions
+    and without any particular make-up. First, a logarithmic color transform is performed
+    from the RGB to HI (hue, intensity) color space. Second, a statistical approach
+    using Markov random field modeling determines the red hue prevailing region and
+    motion in a spatiotemporal neighborhood. Third, the final label field is used
+    to extract ROI (region of interest) and geometrical features.
+  - There are about 90 million high performance mobile phones used in Japan. We are
+    now planning to develop new applications of mobile phone to support children and
+    elder and disabled people who are out of scope of major mobile phone application
+    based on their requirements. We have a responsibility to extend the application
+    filed of mobile phone as a leading country of ubiquitous life. This paper discusses
+    possibilities to realize mobile ad hoc networks using Bluetooth functions equipped
+    on a mobile phone. Hierarchical mobile ad hoc networks using Bluetooth in a mobile
+    phone are firstly developed as a test platform. The test platform proves the possibility
+    of developing mobile ad hoc network by mobile phone built-in Bluetooth functions.
+    We demonstrate their capabilities by showing results of implementing game applications
+    on the test platform. The paper also describes some example applications using
+    mobile ad hoc network technologies, which include a location tracking system for
+    children on the way to a school and an alarm system for hearing impaired people
+  - Switch-and-stay combining (SSC) diversity systems have the advantage of offering
+    one of the least complex solutions to mitigating the effect of fading. In this
+    paper, we present a Markov chain-based analytical framework for the performance
+    analysis of various switching strategies used in conjunction with SSC systems.
+    The resulting expressions are quite general, and are applicable to dual-branch
+    diversity systems operating over a variety of correlated and/or unbalanced fading
+    channels. The mathematical formalism is illustrated by some selected numerical
+    examples, along with their discussion and interpretation. As a result, this paper
+    presents a thorough comparison and highlights the main differences and tradeoffs
+    between the various SSC switching strategies.
+- source_sentence: 'Effect of age on the failure properties of human meniscus: High-speed
+    strain mapping of tissue tears.'
+  sentences:
+  - 'The knee meniscus is a soft fibrous tissue with a high incidence of injury in
+    older populations. The objective of this study was to determine the effect of
+    age on the failure behavior of human knee meniscus when applying uniaxial tensile
+    loads parallel or perpendicular to the primary circumferential fiber orientation.
+    Two age groups were tested: under 40 and over 65 years old. We paired high-speed
+    video with digital image correlation to quantify for the first time the planar
+    strains occurring in the tear region at precise time points, including at ultimate
+    tensile stress, when the tissue begins losing load-bearing capacity. On average,
+    older meniscus specimens loaded parallel to the fiber axis had approximately one-third
+    less ultimate tensile strain and absorbed 60% less energy to failure within the
+    tear region than younger specimens (p < 0.05). Older specimens also had significantly
+    reduced strength and material toughness when loaded perpendicular to the fibers
+    (p < 0.05). These age-related changes indicate a loss of collagen fiber extensibility
+    and weakening of the non-fibrous matrix with age. In addition, we found that when
+    loaded perpendicular to the circumferential fibers, tears propagated near the
+    planes of maximum tensile stress and strain. Whereas when loaded parallel to the
+    circumferential fibers, tears propagated oblique to the loading axis, closer to
+    the planes of maximum shear stress and strain. Our experimental results can assist
+    the selection of valid failure criteria for meniscus, and provide insight into
+    the effect of age on the failure mechanisms of soft fibrous tissue.'
+  - 'Objectives: We aimed to identify key demographic risk factors for hospital attendance
+    with COVID-19 infection. Design: Community survey Setting: The COVID Symptom Tracker
+    mobile application co-developed by physicians and scientists at Kings College
+    London, Massachusetts General Hospital, Boston and Zoe Global Limited was launched
+    in the UK and US on 24th and 29th March 2020 respectively. It captured self-reported
+    information related to COVID-19 symptoms and testing. Participants: 2,618,948
+    users of the COVID Symptom Tracker App. UK (95.7%) and US (4.3%) population. Data
+    cut-off for this analysis was 21st April 2020. Main outcome measures: Visit to
+    hospital and for those who attended hospital, the need for respiratory support
+    in three subgroups (i) self-reported COVID-19 infection with classical symptoms
+    (SR-COVID-19), (ii) self-reported positive COVID-19 test results (T-COVID-19),
+    and (iii) imputed/predicted COVID-19 infection based on symptomatology (I-COVID-19).
+    Multivariate logistic regressions for each outcome and each subgroup were adjusted
+    for age and gender, with sensitivity analyses adjusted for comorbidities. Classical
+    symptoms were defined as high fever and persistent cough for several days. Results:
+    Older age and all comorbidities tested were found to be associated with increased
+    odds of requiring hospital care for COVID-19. Obesity (BMI >30) predicted hospital
+    care in all models, with odds ratios (OR) varying from 1.20 [1.11; 1.31] to 1.40
+    [1.23; 1.60] across population groups. Pre-existing lung disease and diabetes
+    were consistently found to be associated with hospital visit with a maximum OR
+    of 1.79 [1.64,1.95] and 1.72 [1.27; 2.31]) respectively. Findings were similar
+    when assessing the need for respiratory support, for which age and male gender
+    played an additional role. Conclusions: Being older, obese, diabetic or suffering
+    from pre-existing lung, heart or renal disease placed participants at increased
+    risk of visiting hospital with COVID-19. It is of utmost importance for governments
+    and the scientific and medical communities to work together to find evidence-based
+    means of protecting those deemed most vulnerable from COVID-19. Trial registration:
+    The App Ethics have been approved by KCL ethics Committee REMAS ID 18210, review
+    reference LRS-19/20-18210'
+  - Social networking sites (SNS) have growing popularity and several sites compete
+    with each other. This study examines three models to determine how competition
+    between Facebook and other social networking sites may affect continuance intention
+    on Facebook. The first model examines the relationship between having an account
+    on four different SNSs and its impact on Facebook. Twitter users have lower intentions
+    to continue using Facebook, Instagram users have higher intentions. The second
+    model examines attitudes toward specific alternatives and found that users who
+    felt alternatives were attractive have lower intentions to continue using Facebook.
+    The third model examined general attitudes about alternative attractiveness and
+    attitudes toward switching, this model explained a moderate to substantial amount
+    of the variance in continuance intention. This study makes important contributions
+    to both research and practice.
+- source_sentence: Bayesian duration modeling and learning for speech recognition
+  sentences:
+  - Measuring solar irradiance allows for direct maximization of the efficiency in
+    photovoltaic power plants. However, devices for solar irradiance sensing, such
+    as pyranometers and pyrheliometers, are expensive and difficult to calibrate and
+    thus seldom utilized in photovoltaic power plants. Indirect methods are instead
+    implemented in order to maximize efficiency. This paper proposes a novel approach
+    for solar irradiance measurement based on neural networks, which may, in turn,
+    be used to maximize efficiency directly. An initial estimate suggests the cost
+    of the sensor proposed herein may be price competitive with other inexpensive
+    solutions available in the market, making the device a good candidate for large
+    deployment in photovoltaic power plants. The proposed sensor is implemented through
+    a photovoltaic cell, a temperature sensor, and a low-cost microcontroller. The
+    use of a microcontroller allows for easy calibration, updates, and enhancement
+    by simply adding code libraries. Furthermore, it can be interfaced via standard
+    communication means with other control devices, integrated into control schemes,
+    and remote-controlled through its embedded web server. The proposed approach is
+    validated through experimental prototyping and compared against a commercial device.
+  - Form is a framework used to construct tools for analyzing the runtime behavior
+    of standalone and distributed software systems. The architecture of Form is based
+    on the event broadcast and pipe and filter styles. In the implementation of this
+    architecture, execution profiles may be generated from standalone or distributed
+    systems. The profile data is subsequently broadcast by Form to one or more views.
+    Each view is a tool used to support program understanding or other software development
+    activities. The authors describe the Form architecture and implementation, as
+    well as a tool that was built using Form. This tool profiles Java-based distributed
+    systems and generates UML sequence diagrams to describe their execution. We also
+    present a case study that shows how this tool was used to extract sequence diagrams
+    from a three-tiered EJB-based distributed application.
+  - We present Bayesian duration modeling and learning for speech recognition under
+    nonstationary speaking rates and noise conditions. In this study, the Gaussian,
+    Poisson and gamma distributions are investigated, to characterize duration models.
+    The maximum a posteriori (MAP) estimate of the gamma duration model is developed.
+    To exploit the sequential learning, we adopt the Poisson duration model, incorporated
+    with gamma prior density, which belongs to the conjugate prior family. When the
+    adaptation data are sequentially observed, the gamma posterior density is produced
+    for twofold advantages. One is to determine the optimal quasi-Bayes (QB) duration
+    parameter, which can be merged in HMM's for speech recognition. The other one
+    is to build the updating mechanism of gamma prior statistics for sequential learning.
+    An expectation-maximization algorithm is applied to fulfill parameter estimation.
+    In the experiments, the proposed Bayesian approaches significantly improve the
+    speech recognition performance of Mandarin broadcast news. Batch and sequential
+    learning are investigated for MAP and QB duration models, respectively.
+- source_sentence: Configurable security for scavenged storage systems
+  sentences:
+  - Scavenged storage systems harness unused disk space from individual workstations
+    the same way idle CPU cycles are harnessed by desktop grid applications like Seti@Home.
+    These systems provide a promising low cost, high-performance storage solution
+    in certain high-end computing scenarios. However, selecting the security level
+    and designing the security mechanisms for such systems is challenging as scavenging
+    idle storage opens the door for security threats absent in traditional storage
+    systems that use dedicated nodes under a single administrative domain. Moreover,
+    increased security often comes at the price of performance and scalability. This
+    paper develops a general threat model for systems that use scavenged storage,
+    presents the design of a protocol that addresses these threats and is optimized
+    for throughput, and evaluates the overheads brought by the new security protocol
+    when configured to provide a number of different security properties.
+  - Histone methyltransferases are involved in many important biological processes,
+    and abnormalities in these enzymes are associated with tumorigenesis and progression.
+    Disruptor of telomeric silencing 1-like (DOT1L), a key hub in histone lysine methyltransferases,
+    has been reported to play an important role in the processes of mixed-lineage
+    leukemia (MLL)-rearranged leukemias and validated to be a potential therapeutic
+    target. In this study, we identified a novel DOT1L inhibitor, DC_L115 (CAS no.
+    1163729-79-0), by combining structure-based virtual screening with biochemical
+    analyses. This potent inhibitor DC_L115 shows high inhibitory activity toward
+    DOT1L (IC50 = 1.5 μM). Through a process of surface plasmon resonance-based binding
+    assays, DC_L115 was founded to bind to DOT1L with a binding affinity of 0.6 μM
+    in vitro. Moreover, this compound selectively inhibits MLL-rearranged cell proliferation
+    with an IC50 value of 37.1 μM. We further predicted the binding modes of DC_L115
+    through molecular docking anal...
+  - Employing channel state information at the network layer, efficient routing protocols
+    for equal-power and optimal-power allocation in a multihop network in fading are
+    proposed. The end-to-end outage probability from source to destination is used
+    as the optimization criterion. The problem of finding the optimal route is investigated
+    under either known mean channel state information (CSI) or known instantaneous
+    CSI. The analysis shows that the proposed routing strategy achieves full diversity
+    order, equal to the total number of nodes in the network excluding the destination,
+    only when instantaneous CSI is known and used. The optimal routing algorithm requires
+    a centralized exhaustive search which leads to an exponential complexity, which
+    is infeasible for large networks. An algorithm of polynomial complexity for a
+    centralized environment is developed by reducing the search space. A distributed
+    approach based on the Bellman-Ford routing algorithm is proposed which achieves
+    a good implementation complexity-performance trade-off.
+- source_sentence: Computationally efficient fixed complexity LLL algorithm for lattice-reduction-aided
+    multiple-input–multiple-output precoding
+  sentences:
+  - ABSTRACTThe success of the open innovation (OI) paradigm is still debated and
+    literature is searching for its determinants. Although firms’ internal social
+    context is crucial to explain the success or failure of OI practices, such context
+    is still poorly investigated. The aim of the paper is to analyse whether internal
+    social capital (SC), intended as employees’ propensity to interact and work in
+    groups in order to solve innovation issues, mediates the relationship between
+    OI practices and innovation ambidexterity (IA). Results, based on a survey research
+    developed in Finland, Italy and Sweden, suggest that collaborations with different
+    typologies of partners (scientific and business) achieve good results in terms
+    of IA, through the partial mediation of the internal SC.
+  - In multiple-input–multiple-output broadcast channels, lattice reduction (LR) preprocessing
+    technique can significantly improve the precoding performance. Among the existing
+    LR algorithms, the fixed complexity Lenstra–Lenstra–Lovasz (fcLLL) algorithm applying
+    limited number of LLL loops is suitable for the real-time communication system.
+    However, fcLLL algorithm suffers from higher average complexity. Aiming at this
+    problem, a computationally efficient fcLLL (CE-fcLLL) algorithm for LR-aided (LRA)
+    precoding is developed in this study. First, the authors analyse the impact of
+    fcLLL algorithm on the signal-to-noise ratio performance of LRA precoding by a
+    power factor (PF) which is defined to measure the relation of reduced basis and
+    transmit power of LRA precoding. Then, they propose a CE-fcLLL algorithm by designing
+    a new LLL loop and introducing new early termination conditions to reduce redundant
+    and inefficient LR operation in fcLLL algorithm. Finally, they define a PF loss
+    factor to optimise the PF threshold and the number of LLL loops, which can lead
+    to a performance-complexity tradeoff. Simulation results show that the proposed
+    algorithm for LRA precoding can achieve better bit-error-rate performance than
+    the fcLLL algorithm with remarkable complexity savings in the same upper complexity
+    bound.
+  - 'While multistage switching networks for vector multiprocessors have been studied
+    extensively, detailed evaluations of their performance are rare. Indeed, analytical
+    models, simulations with pseudo-synthetic loads, studies focused on average-value
+    parameters, and measurements of networks disconnected from the machine all provide
+    limited information. In this paper, instead, we present an in-depth empirical
+    analysis of a multistage switching network in a realistic setting: we use hardware
+    probes to examine the performance of the omega network of the Cedar shared-memory
+    machine executing real applications. The machine is configured with 16 vector
+    processors. The analysis suggests that the performance of multistage switching
+    networks is limited by traffic non-uniformities. We identify two major non-uniformities
+    that degrade Cedar''s performance and are likely to slow down other networks too.
+    The first one is the contention caused by the return messages in a vector access
+    as they converge from the memories to one processor port. This traffic convergence
+    penalizes vector reads and, more importantly, causes tree saturation. The second
+    non-uniformity is the uneven contention delays induced by even a relatively fair
+    scheme to resolve message collisions. Based on our observations, we argue that
+    intuitive optimizations for multistage switching networks may not be cost-effective.
+    Instead, we suggest changes to increase the network bandwidth at the root of the
+    traffic convergence tree and to delay traffic convergence up until the final stages
+    of the network. >'
 ---
+
+# SentenceTransformer based on nreimers/MiniLM-L6-H384-uncased
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [nreimers/MiniLM-L6-H384-uncased](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased). It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [nreimers/MiniLM-L6-H384-uncased](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased) <!-- at revision 3276f0fac9d818781d7a1327b3ff818fc4e643c0 -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 384 tokens
+- **Similarity Function:** Cosine Similarity
+<!-- - **Training Dataset:** Unknown -->
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
+- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+
+### Full Model Architecture
+
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 512, '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)
+
+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("sentence_transformers_model_id")
+# Run inference
+sentences = [
+    'Computationally efficient fixed complexity LLL algorithm for lattice-reduction-aided multiple-input–multiple-output precoding',
+    'In multiple-input–multiple-output broadcast channels, lattice reduction (LR) preprocessing technique can significantly improve the precoding performance. Among the existing LR algorithms, the fixed complexity Lenstra–Lenstra–Lovasz (fcLLL) algorithm applying limited number of LLL loops is suitable for the real-time communication system. However, fcLLL algorithm suffers from higher average complexity. Aiming at this problem, a computationally efficient fcLLL (CE-fcLLL) algorithm for LR-aided (LRA) precoding is developed in this study. First, the authors analyse the impact of fcLLL algorithm on the signal-to-noise ratio performance of LRA precoding by a power factor (PF) which is defined to measure the relation of reduced basis and transmit power of LRA precoding. Then, they propose a CE-fcLLL algorithm by designing a new LLL loop and introducing new early termination conditions to reduce redundant and inefficient LR operation in fcLLL algorithm. Finally, they define a PF loss factor to optimise the PF threshold and the number of LLL loops, which can lead to a performance-complexity tradeoff. Simulation results show that the proposed algorithm for LRA precoding can achieve better bit-error-rate performance than the fcLLL algorithm with remarkable complexity savings in the same upper complexity bound.',
+    'ABSTRACTThe success of the open innovation (OI) paradigm is still debated and literature is searching for its determinants. Although firms’ internal social context is crucial to explain the success or failure of OI practices, such context is still poorly investigated. The aim of the paper is to analyse whether internal social capital (SC), intended as employees’ propensity to interact and work in groups in order to solve innovation issues, mediates the relationship between OI practices and innovation ambidexterity (IA). Results, based on a survey research developed in Finland, Italy and Sweden, suggest that collaborations with different typologies of partners (scientific and business) achieve good results in terms of IA, through the partial mediation of the internal SC.',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 384]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities.shape)
+# [3, 3]
+```
+
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Dataset
+
+#### Unnamed Dataset
+
+
+* Size: 730,454 training samples
+* Columns: <code>sentence_0</code> and <code>sentence_1</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence_0                                                                        | sentence_1                                                                           |
+  |:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                               |
+  | details | <ul><li>min: 4 tokens</li><li>mean: 15.97 tokens</li><li>max: 48 tokens</li></ul> | <ul><li>min: 18 tokens</li><li>mean: 193.95 tokens</li><li>max: 512 tokens</li></ul> |
+* Samples:
+  | sentence_0                                                                                                              | sentence_1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           |
+  |:------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>E-government in a corporatist, communitarian society: the case of Singapore</code>                                | <code>Singapore was one of the early adopters of e-government initiatives in keeping with its status as one of the few developed Asian countries and has continued to be at the forefront of developing e-government structures. While crediting the city-state for the speed of its development, observers have critiqued that the republic limits pluralism, which directly affects e-governance initiatives. This article draws on two recent government initiatives, the notions of corporatism and communitarianism and the concept of symmetry and asymmetry in communication to present the e-government and e-governance structures in Singapore. Four factors are presented as critical for the creation of a successful e-government infrastructure: an educated citizenry; adequate technical infrastructures; offering e-services that citizens need; and commitment from top government officials to support the necessary changes with financial resources and leadership. However, to have meaningful e-governance there has to be political plural...</code>                                                                                                                                                                                                                         |
+  | <code>Multicast routing representation in ad hoc networks using fuzzy Petri nets</code>                                 | <code>In an ad hoc network, each mobile node plays the role of a router and relays packets to final destinations. The network topology of an ad hoc network changes frequently and unpredictable, so that the routing and multicast become extremely challenging. We describe the multicast routing representation using fuzzy Petri net model with the concept of immediately reachable set in wireless ad hoc networks which all nodes equipped with GPS unit. It allows structured representation of network topology, and has a fuzzy reasoning algorithm for finding multicast tree and improves the efficiency of the ad hoc network routing scheme. Therefore when a packet is to be multicast to a group by a multicast source, a heuristic algorithm is used to compute the multicast tree based on the local network topology with a multicast source. Finally, the simulation shows that the percentage of the improvement is more than 15% when compared the IRS method with the original method.</code>                                                                                                                                                                                                                                                                                 |
+  | <code>A Prognosis Tool Based on Fuzzy Anthropometric and Questionnaire Data for Obstructive Sleep Apnea Severity</code> | <code>Obstructive sleep apnea (OSA) are linked to the augmented risk of morbidity and mortality. Although polysomnography is considered a well-established method for diagnosing OSA, it suffers the weakness of time consuming and labor intensive, and requires doctors and attending personnel to conduct an overnight evaluation in sleep laboratories with dedicated systems. This study aims at proposing an efficient diagnosis approach for OSA on the basis of anthropometric and questionnaire data. The proposed approach integrates fuzzy set theory and decision tree to predict OSA patterns. A total of 3343 subjects who were referred for clinical suspicion of OSA (eventually 2869 confirmed with OSA and 474 otherwise) were collected, and then classified by the degree of severity. According to an assessment of experiment results on g-means, our proposed method outperforms other methods such as linear regression, decision tree, back propagation neural network, support vector machine, and learning vector quantization. The proposed method is highly viable and capable of detecting the severity of OSA. It can assist doctors in pre-diagnosis of OSA before running the formal PSG test, thereby enabling the more effective use of medical resources.</code> |
+* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim"
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `per_device_train_batch_size`: 16
+- `per_device_eval_batch_size`: 16
+- `num_train_epochs`: 1
+- `multi_dataset_batch_sampler`: round_robin
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: no
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 16
+- `per_device_eval_batch_size`: 16
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 1
+- `eval_accumulation_steps`: None
+- `learning_rate`: 5e-05
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1
+- `num_train_epochs`: 1
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.0
+- `warmup_steps`: 0
+- `log_level`: passive
+- `log_level_replica`: warning
+- `log_on_each_node`: True
+- `logging_nan_inf_filter`: True
+- `save_safetensors`: True
+- `save_on_each_node`: False
+- `save_only_model`: False
+- `restore_callback_states_from_checkpoint`: False
+- `no_cuda`: False
+- `use_cpu`: False
+- `use_mps_device`: False
+- `seed`: 42
+- `data_seed`: None
+- `jit_mode_eval`: False
+- `use_ipex`: False
+- `bf16`: False
+- `fp16`: False
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: None
+- `local_rank`: 0
+- `ddp_backend`: None
+- `tpu_num_cores`: None
+- `tpu_metrics_debug`: False
+- `debug`: []
+- `dataloader_drop_last`: False
+- `dataloader_num_workers`: 0
+- `dataloader_prefetch_factor`: None
+- `past_index`: -1
+- `disable_tqdm`: False
+- `remove_unused_columns`: True
+- `label_names`: None
+- `load_best_model_at_end`: False
+- `ignore_data_skip`: False
+- `fsdp`: []
+- `fsdp_min_num_params`: 0
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `fsdp_transformer_layer_cls_to_wrap`: None
+- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch
+- `optim_args`: None
+- `adafactor`: False
+- `group_by_length`: False
+- `length_column_name`: length
+- `ddp_find_unused_parameters`: None
+- `ddp_bucket_cap_mb`: None
+- `ddp_broadcast_buffers`: False
+- `dataloader_pin_memory`: True
+- `dataloader_persistent_workers`: False
+- `skip_memory_metrics`: True
+- `use_legacy_prediction_loop`: False
+- `push_to_hub`: False
+- `resume_from_checkpoint`: None
+- `hub_model_id`: None
+- `hub_strategy`: every_save
+- `hub_private_repo`: False
+- `hub_always_push`: False
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `eval_do_concat_batches`: True
+- `fp16_backend`: auto
+- `push_to_hub_model_id`: None
+- `push_to_hub_organization`: None
+- `mp_parameters`: 
+- `auto_find_batch_size`: False
+- `full_determinism`: False
+- `torchdynamo`: None
+- `ray_scope`: last
+- `ddp_timeout`: 1800
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `dispatch_batches`: None
+- `split_batches`: None
+- `include_tokens_per_second`: False
+- `include_num_input_tokens_seen`: False
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `eval_on_start`: False
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: round_robin
+
+</details>
+
+### Training Logs
+| Epoch  | Step  | Training Loss |
+|:------:|:-----:|:-------------:|
+| 0.0110 | 500   | 0.4667        |
+| 0.0219 | 1000  | 0.179         |
+| 0.0329 | 1500  | 0.1543        |
+| 0.0438 | 2000  | 0.1284        |
+| 0.0548 | 2500  | 0.1123        |
+| 0.0657 | 3000  | 0.101         |
+| 0.0767 | 3500  | 0.0989        |
+| 0.0876 | 4000  | 0.0941        |
+| 0.0986 | 4500  | 0.0827        |
+| 0.1095 | 5000  | 0.0874        |
+| 0.1205 | 5500  | 0.0825        |
+| 0.1314 | 6000  | 0.0788        |
+| 0.1424 | 6500  | 0.0728        |
+| 0.1533 | 7000  | 0.0768        |
+| 0.1643 | 7500  | 0.0707        |
+| 0.1752 | 8000  | 0.0691        |
+| 0.1862 | 8500  | 0.0666        |
+| 0.1971 | 9000  | 0.0644        |
+| 0.2081 | 9500  | 0.0615        |
+| 0.2190 | 10000 | 0.0651        |
+| 0.2300 | 10500 | 0.0604        |
+| 0.2409 | 11000 | 0.0595        |
+| 0.2519 | 11500 | 0.0622        |
+| 0.2628 | 12000 | 0.0537        |
+| 0.2738 | 12500 | 0.0564        |
+| 0.2848 | 13000 | 0.0622        |
+| 0.2957 | 13500 | 0.052         |
+| 0.3067 | 14000 | 0.0475        |
+| 0.3176 | 14500 | 0.0569        |
+| 0.3286 | 15000 | 0.0511        |
+| 0.3395 | 15500 | 0.0476        |
+| 0.3505 | 16000 | 0.0498        |
+| 0.3614 | 16500 | 0.0527        |
+| 0.3724 | 17000 | 0.0556        |
+| 0.3833 | 17500 | 0.0495        |
+| 0.3943 | 18000 | 0.0482        |
+| 0.4052 | 18500 | 0.0556        |
+| 0.4162 | 19000 | 0.0454        |
+| 0.4271 | 19500 | 0.0452        |
+| 0.4381 | 20000 | 0.0431        |
+| 0.4490 | 20500 | 0.0462        |
+| 0.4600 | 21000 | 0.0473        |
+| 0.4709 | 21500 | 0.0387        |
+| 0.4819 | 22000 | 0.041         |
+| 0.4928 | 22500 | 0.0472        |
+| 0.5038 | 23000 | 0.0435        |
+| 0.5147 | 23500 | 0.0419        |
+| 0.5257 | 24000 | 0.0395        |
+| 0.5366 | 24500 | 0.043         |
+| 0.5476 | 25000 | 0.0419        |
+| 0.5585 | 25500 | 0.0394        |
+| 0.5695 | 26000 | 0.0403        |
+| 0.5805 | 26500 | 0.0436        |
+| 0.5914 | 27000 | 0.0414        |
+| 0.6024 | 27500 | 0.0418        |
+| 0.6133 | 28000 | 0.0411        |
+| 0.6243 | 28500 | 0.035         |
+| 0.6352 | 29000 | 0.0397        |
+| 0.6462 | 29500 | 0.0392        |
+| 0.6571 | 30000 | 0.0373        |
+| 0.6681 | 30500 | 0.0373        |
+| 0.6790 | 31000 | 0.0363        |
+| 0.6900 | 31500 | 0.0418        |
+| 0.7009 | 32000 | 0.0377        |
+| 0.7119 | 32500 | 0.0321        |
+| 0.7228 | 33000 | 0.0331        |
+| 0.7338 | 33500 | 0.0373        |
+| 0.7447 | 34000 | 0.0342        |
+| 0.7557 | 34500 | 0.0335        |
+| 0.7666 | 35000 | 0.0323        |
+| 0.7776 | 35500 | 0.0362        |
+| 0.7885 | 36000 | 0.0376        |
+| 0.7995 | 36500 | 0.0364        |
+| 0.8104 | 37000 | 0.0396        |
+| 0.8214 | 37500 | 0.0321        |
+| 0.8323 | 38000 | 0.0358        |
+| 0.8433 | 38500 | 0.0299        |
+| 0.8543 | 39000 | 0.0304        |
+| 0.8652 | 39500 | 0.0317        |
+| 0.8762 | 40000 | 0.0334        |
+| 0.8871 | 40500 | 0.0331        |
+| 0.8981 | 41000 | 0.0326        |
+| 0.9090 | 41500 | 0.0325        |
+| 0.9200 | 42000 | 0.0321        |
+| 0.9309 | 42500 | 0.0316        |
+| 0.9419 | 43000 | 0.0321        |
+| 0.9528 | 43500 | 0.0353        |
+| 0.9638 | 44000 | 0.0315        |
+| 0.9747 | 44500 | 0.0326        |
+| 0.9857 | 45000 | 0.031         |
+| 0.9966 | 45500 | 0.0315        |
+
+
+### Framework Versions
+- Python: 3.12.2
+- Sentence Transformers: 3.0.1
+- Transformers: 4.42.3
+- PyTorch: 2.3.1+cu121
+- Accelerate: 0.32.1
+- Datasets: 2.20.0
+- Tokenizers: 0.19.1
+
+## 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{henderson2017efficient,
+    title={Efficient Natural Language Response Suggestion for Smart Reply}, 
+    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
+    year={2017},
+    eprint={1705.00652},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config.json

@@ -0,0 +1,26 @@
+{
+  "_name_or_path": "nreimers/MiniLM-L6-H384-uncased",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "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": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 6,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.42.3",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.0.1",
+    "transformers": "4.42.3",
+    "pytorch": "2.3.1+cu121"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

log.txt

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+{'loss': 0.0315, 'grad_norm': 5.446718215942383, 'learning_rate': 6.702882677648297e-08, 'epoch': 1.0}
+{'train_runtime': 19215.0068, 'train_samples_per_second': 38.015, 'train_steps_per_second': 2.376, 'train_loss': 0.056530908753462804, 'epoch': 1.0}
+2024-07-20 07:20:41 - Save model to ./output

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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+size 90864192

modules.json

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+[
+  {
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+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
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+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  }
+]

sentence_bert_config.json

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+{
+  "max_seq_length": 512,
+  "do_lower_case": false
+}

special_tokens_map.json

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+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+      "single_word": false,
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+      "rstrip": false,
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+      "lstrip": false,
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+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}