sentence-transformers safetensors bert sentence-similarity feature-extraction generated_from_trainer dataset_size:730454 loss:MultipleNegativesRankingLoss arxiv:1908.10084 arxiv:1705.00652 base_model:nreimers/MiniLM-L6-H384-uncased base_model:finetune:nreimers/MiniLM-L6-H384-uncased autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
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}
}
```

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