sentence-transformers safetensors bert sentence-similarity feature-extraction generated_from_trainer dataset_size:160000 loss:MarginDistillationLoss arxiv:1908.10084 base_model:sentence-transformers/all-MiniLM-L6-v2 base_model:finetune:sentence-transformers/all-MiniLM-L6-v2 autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: sentence-transformers/all-MiniLM-L6-v2
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:160000
- loss:MarginDistillationLoss
widget:
- source_sentence: why is it a healthy thing to be in a dynamic work environment?
  sentences:
  - Workplace Mindfulness In spite of the advancements in the field, a major limita
    tion of the extant research is the lack of an effective means  to measure workplace
    mindfulness. Workplace mindfulness  is particularly concerned with events in the
    workplace (e.g.,  work tasks and meetings) rather than with events occurring  outside
    of the work setting, such as life situations (e.g., driv ing and showering). At
    work, employees are embedded in  task-oriented workflows, processes, and employment
    rela tionships (Zivnuska et al., 2016). Hence, prior research has  theorized that
    workplace mindfulness depends on the par ticular context—namely, the work environment
    (Dane &  Brummel, 2014)—and focusing on this specific setting can  help mindfulness
    scholars tackle issues of theoretical impor tance and practical concern (Dane,
    2011; Dane & Brummel,  2014). Further, scholars have indicated that some employees  may
    be more mindful at work than others due to specific  experiences they have accrued
    (Dane & Brummel, 2014). It  is therefore possible that, for some employees, certain
    fea tures and events in the workplace—that is, contextual stimuli encountered
    in this setting (Dane & Brummel, 2014; Ziv nuska et al., 2016)—may induce workplace
    mindfulness. As  such, any measure of workplace mindfulness should essen tially
    capture an employee’s awareness and attention to the  work-related issues that
    an individual encounters within the  work setting (Elsbach & Pratt, 2007; George,
    2009).
  - "Examining workplace mindfulness \nand its relations to job  \nperformance and\
    \  \nturnover intention Dynamic work environments tend to be associated with high\
    \ levels of emotional  arousal and stress – byproducts of the time pressure and\
    \ unpredictability pervading such  environments (Brehmer, 1992; Klein, 1998).\
    \ Over time, these pressures may become  difficult to bear, leading people to\
    \ consider relinquishing their employment in the  dynamic work setting. On this\
    \ point, research demonstrates negative relationships  between psychological and\
    \ physiological job-related demands and people’s intentions to  leave their organizations\
    \ (Begley, 1998; Kemery et al., 1987). With that said, intention to  leave (i.e.\
    \ turnover intention) is subject to a number of influences, including not only\
    \  features of the work context, but also individual-level factors (Cardador et\
    \ al., 2011;  Meyer et al., 2002). As such, even within the same work setting,\
    \ people may differ in  their turnover intentions."
  - Workplace Mindfulness Mindfulness can also be conceptualized as a trait character
    ized by receptive awareness and attention to ongoing events  and experiences (Brown
    & Ryan, 2003; Feldman et al.,  2007). Compared with the traditional conceptualization
    of a  trait, mindfulness as an individual difference is less stable and  can be
    affected more by internal and external stimuli, though  it remains more stable
    than a state. For instance, Brown and  Ryan (2003, p. 823) indicated that mindfulness
    involves  “an open, undivided observation of what is occurring both  internally
    and externally.” Cardaciotto et al., (2008, p. 205)defined mindfulness as “the
    tendency to be highly aware of  one’s internal and external experiences in the
    context of an  accepting, nonjudgmental stance toward those experiences.”
- source_sentence: what is workplace mindfulness
  sentences:
  - "Examining workplace mindfulness \nand its relations to job  \nperformance and\
    \  \nturnover intention Drawing on these observations, we consider whether workplace\
    \ mindfulness relates  to turnover intention within dynamic work environments.\
    \ Here, research indicates that  mindfulness leads people to cope with challenging\
    \ or stressful situations proactively and  adaptively (e.g. Shapiro et al., 2007;\
    \ Weinstein et al., 2009). In particular, mindfulness  facilitates self-regulation\
    \ (Atkins and Parker, 2012; Glomb et al., 2011) and enables peo ple to respond\
    \ to potentially stressful events with greater equanimity and less rumination\
    \  (Brown et al., 2007; Carlson, 2013; Shapiro et al., 2006). Consequently, mindfulness\
    \  may guard against emotional exhaustion at work – a possibility supported by\
    \ recent  empirical research (Hülsheger et al., 2013). Given these lines of theory\
    \ and evidence,  mindfulness should enhance one’s ability to cope with the stresses\
    \ and strains of a  dynamic work environment. Accordingly, we predict that those\
    \ high in workplace mind fulness will feel less compelled than others to permanently\
    \ depart from such an  environment."
  - Measuring State and Trait Mindfulness at the Workplace While a variety of definitions
    of Mindfulness seem to emphasize on similar  and overlapping  elements such as
    "presence", awareness" and "non judgment"; there remains dissent  among  researchers
    in the area of mindfulness regarding the scope and nature of the phenomenon, and  a
    lack of consensus regarding the definitions and components of mindfulness (Cigolla
    &  Brown, 2011; Grossman, 2011; Sutcliffe et al., 2016).  In the field of organizational
    science, Good et al. (2016) has conceptualized mindfulness as a  state, trait,
    practice and intervention.   A trait is referred to as a stable characteristic
    or behavioural pattern exhibited by an  individual over the long term (Hamaker
    et al., 2007). Therefore, trait mindfulness is a  dispositional individual difference
    in mindfulness (Allen & Kiburz, 2012). It is a stable  individual characteristic
    exhibited over a long period going much beyond any mindfulness based intervention
    or practice.
  - MAAS FMI Mindfulness is defined differently by various practitioners,  researchers
    and clinicians, as well as according to the various  schools of thought, which
    place more emphasis on particular  aspects of the concept compared to others (Brown
    et al., 2007).  Dane (2011) provides a synopsis of various academic and  philosophical
    conceptualisations of mindfulness showing that  scholars display noticeably high
    consensus on the nature of  mindfulness. One of the features common across multiple  conceptualisations
    is that mindfulness is a state of  consciousness (Brown & Ryan, 2003). It refers
    to ‘… a  heightened state of involvement and wakefulness or being in  the present’
    (Langer & Moldoveanu, 2000, p. 2). Brown, Kasser,  Ryan, Linley and Orzech (2009)
    explain that mindfulness is not  deliberative in nature. Inputs are allowed to
    enter one’s  awareness by simply noticing what is taking place. Mindfulness  refers
    to the simple act of observing without scrutiny, making  comparisons or evaluating
    events and experience and is thus  dissimilar to ‘self-awareness’ or reflexive
    consciousness in  other forms. Instead, mindfulness concerns a non-interference  with
    experience. Walach et al. (2006, p. 1544) explain that it is ‘a  warm and friendly,
    accepting and non-judgemental attitude  towards those elements of our mind. Suspending
    categorical  judgements, which normally follow every perception rather  quickly,
    is an integral part of mindfulness’. Therefore,  mindfulness is not a cold, cognitive
    process.
- source_sentence: what kind of skill is mindfulness in workplace
  sentences:
  - Workplace Mindfulness Supporting this rationale, Kudesia (2019) and Kudesia  and
    Nyima (2015) highlighted the context-specific nature  of mindfulness, recognizing
    that mindfulness reflects the  individual’s metacognitive skills, which are engaged
    in a  particular situation. Bishop (2002) suggested that mindful ness practice
    actually encompasses several metacognitive  processes, proposing that mindfulness
    can be described as  a type of metacognitive skill. In essence, the metacognitive  skills
    embedded in workplace mindfulness are a crucial part  of the self-regulatory loop,
    and their core purpose is to reduce  the discrepancy between the actual state
    and an undesired  state, or between an actual state and a desired state (Carver  &
    Scheier, 2002). As such, workplace mindfulness com prises employees’ metacognitive
    skills within the work set ting through which they demonstrate their regulation
    ability  when dealing with work-related activities. We argue that the  individual’s
    specific skills become especially pertinent when  we consider particular aspects
    of the metacognitive practice  pathway of mindfulness, such as self-regulating
    attention and  noticing subtle affective sensations. For example, mindfulness  is
    associated with the ability to distance oneself from stimuli  (Chambers et al.,
    2009). This distancing allows employees to  notice, prioritize, and respond to
    distractions in a conscious  way, without impulsivity or defensiveness (Bishop
    et al.,  2004; Teasdale, 1999; Teasdale et al., 1995).
  - integrative review Most of the existing literature reviews on workplace mindfulness
    have focused on the  characteristics and outcomes of mindfulness interventions
    in the workplace (Allen et al.,  2015; Eby et al., 2019; Jamieson & Tuckey, 2017;
    Johnson et al., 2020). These reviews have  not integrated individual and workplace
    factors as antecedents of workplace mindfulness as  well as the factors that can
    mediate or moderate the mindfulness/outcomes relationship.  While two reviews
    have integrated a broader range of factors that can mediate the  relationship
    between workplace mindfulness and workplace outcomes (Glomb et al., 2011;  Good
    et al., 2016), a key limitation is that they have not encompassed antecedents
    of  workplace mindfulness and moderators of the mindfulness/outcomes relationship.
    Another  review by Sutcliffe et al. (2016) discussed the antecedents of workplace
    mindfulness and  mediators of the mindfulness/outcomes relationship. However,
    their review has not provided  a comprehensive framework of workplace mindfulness
    that integrates individual and  workplace factors as antecedents, mediators, and
    moderators. Overall, knowledge of  antecedents, mediators, and moderators related
    to workplace mindfulness is fragmented in the  extant organizational literature
    and existing reviews have not provided a comprehensive  model with which to organize
    and reconcile understandings of antecedents of workplace mindfulness as well as
    mediating and moderating factors of the mindfulness/outcomes  relationship.
  - Measuring State and Trait Mindfulness at the Workplace On the other hand, "state"  is
    defined  as the experience of an individual while interacting  with a given situation
    (Hamaker et al., 2007).Therefore, state mindfulness is described as a  temporary
    state of attention in the present moment occurring both externally and internally  (Dane,
    2011).  Some researchers (Lau et al., 2006) believe mindfulness to be a psychological
    state that  varies across situations within individuals, while others (Baer et
    al., 2006) argue that   mindfulness is a stable characteristic.  If one were to
    view state mindfulness as the within (intra) person variation of mindfulness  experiences;
    and trait mindfulness as between (inter) person variation of mindfulness ability,  the
    two conceptualizations of mindfulness can be integrated into a meaningful whole.In
    fact, Jamieson & Tuckey (2016) assert that the intensity, duration and frequency
    with  which an individual engages in different states of mindfulness determine
    the trait mindfulness  of an individual (Hulsheger et al., 2013) (See Figure 1)
- source_sentence: what are the dimensions of mindfulness in workplaces?
  sentences:
  - Measuring State and Trait Mindfulness at the Workplace Mindfulness is known to
    be cultivated through mindfulness practices and interventions.  Linkage, therefore,
    exists between mindfulness practice and intervention along with trait and  6 |
    P a g e  state mindfulness as asserted by Jamieson & Tuckey (2016) who propose
    that mindfulness  practice along with mindfulness-based intervention (MBI) enhances
    an individuals’ state  mindfulness which leads to development of trait mindfulness
    (Hulsheger et al., 2013).  Although, this particular pathway hasn't been specifically
    investigated, evidence suggest that  State and Trait Mindfulness are indeed linked
    to each other. Brown & Rayn (2003) and  Hulsheger et al. (2013) have reported
    considerable variance in the state (within a person) and  trait (between person)
    mindfulness among individuals over a series of days. Additionally,  a  moderately
    strong association has also been reported between experiences of state and trait  mindfulness
    (Brown & Ryan, 2003).
  - Assessing Facets of Doing so, the present endeavor makes three contributions to
    the literature.  First, using the multidimensional scale developed in the present
    work, we  will provide first insights into the differential validities of subfacets
    of mind fulness for key work outcomes. This will foster a refined understanding
    of  the mechanisms of action inherent to mindfulness and help understand why  mindfulness
    matters for which work outcome (Bishop, Lau, Shapiro, Carlson,  Anderson, Carmody,
    Segal, Abbey, Speca, Velting, & Devins, 2004; Shapiro,  Carlson, Astin, & Freedman,
    2006). Second, although prominent mindfulness  theories and scholarly work on
    mindfulness in the clinical area suggest that  mindfulness consists of multiple
    subfacets (Baer et al., 2006; Bishop et al.,  2004), research on mindfulness in
    the context of work has almost exclusively  operationalized mindfulness by assessing
    the awareness component (for an  exception see Liang et al., 2017). This bears
    the risk of construct deficiency  due to which mindfulness might not have been
    considered in its entirety in  relation to work outcomes. By equating a sub-aspect
    with the overall con struct, the role of the overall construct of mindfulness
    in the context of work  might have been underestimated. The present work takes
    a course correction  and informs the organizational mindfulness literature by
    revealing the extent  to which a work-related scale capturing the construct of
    mindfulness compre hensively provides a better understanding of the extent to
    which mindfulness  relates to important work outcomes. Third, the present work
    makes a practi cal contribution to the organizational mindfulness literature by
    offering a reli able and valid multidimensional scale that is applicable in the
    context of work  and that enables other researchers to use this refined, multidimensional
    oper ationalization of mindfulness in their work and thereby helps move this field  forward.
    Apart from the benefits of capturing the construct of mindfulness  comprehensively
    and being able to differentiate between subfacets, a contex tualized trait mindfulness
    scale bears important advantages over non-contex tualized generic mindfulness
    scales. This has been documented by research in  the area of personality and personnel
    psychology, providing ample evidence  that contextualizing items and providing
    participants with a context-specific   frame of reference that conceptually overlaps
    with the criterion domain  improves criterion-related validity (Bing, Whanger,
    Davison, & VanHook,  2004; Lievens, De Corte, & Schollaert, 2008; Shaffer & Postlethwaite,
    2012).
  - 'Workplace Mindfulness In our model, we conceptualize workplace mindfulness as  a
    reflective construct because its components share a common  theme and each component
    depends on the construct of work place mindfulness (e.g., Diamantopoulos et al.,
    2008; Edwards  & Bagozzi, 2000; MacKenzie et al., 2005). Workplace mind fulness
    is composed of three dimensions: awareness, attention,  and acceptance. These
    three dimensions do not form a sequen tial process. That is, one or two might
    potentially emerge first,  with the others appearing later; however, the three
    aspects are  ultimately integrated to represent workplace mindfulness. Put  differently,
    awareness, attention, and acceptance are specific  manifestations of workplace
    mindfulness, which together  reflect the concept of workplace mindfulness.'
- source_sentence: what is mindfulness?
  sentences:
  - "Examining workplace mindfulness \nand its relations to job  \nperformance and\
    \  \nturnover intention To begin, one of the most theoretically and practically\
    \ important outcomes in work place settings is job performance. While job performance\
    \ commands much scholarly  attention (see Motowidlo, 2003, for a review), little\
    \ research has empirically connected  mindfulness to job performance. Nevertheless,\
    \ an emerging body of research has dem onstrated linkages between mindfulness\
    \ and performance across a number of tasks (e.g.  Ostafin and Kassman, 2012; Ruedy\
    \ and Schweitzer, 2010; Shao and Skarlicki, 2009). As  research in this vein suggests,\
    \ mindfulness contributes to performance by improving  cognitive flexibility and\
    \ alertness (Moore and Malinowski, 2009; Zeidan et al., 2010)  and guarding against\
    \ distractions and performance blunders (Herndon, 2008). Taken  together, these\
    \ findings raise the possibility that workplace mindfulness facilitates job  performance."
  - Workplace Mindfulness Mindfulness is also defined as a state (e.g., Bishop et
    al.,  2004; Good et al., 2016; Lau et al., 2006; Tanay & Bernstein,  2013) of
    being aware of and attentive to what is taking place  internally and externally
    at that moment (Good et al., 2016;  Lau et al., 2006; Tanay & Bernstein, 2013).
    For example, Lau  et al., (2006, p. 1447) described mindfulness as “a mode, or  state-like
    quality that is maintained only when attention to  experience is intentionally
    cultivated with an open, nonjudg mental orientation to experience.” More recently,
    Good et al.,  (2016, p. 117) defined mindfulness as “receptive attention to  and
    awareness of present events and experience.”
  - Workplace Mindfulness Brown and Ryan (2003) further propose that, despite their  intertwined
    nature, distinctions exist between attention and  awareness—the insights gained
    by sustained awareness can  only be translated into specific actions by paying
    focused  attention to our behaviors or the tasks at hand (Martin,  1997). Hence,
    heightened attention to and awareness of  experiences and events should capture
    two different aspects  of mindfulness. Recent research has also emphasized that  attention
    and awareness should be distinguished from each  other because attention reflects
    an ever-changing factor of  consciousness, whereas awareness refers to a specific
    and  stable state of consciousness (Selart et al., in press). In the  past, attention
    and awareness have proved important to the  study of mindfulness-promoting practices
    (Brown & Ryan,  2004), as some of these practices highlight focused attention  whereas
    others emphasize awareness (Bishop et al., 2004).  Notably, research has yielded
    empirical support confirming  these distinctions (Feldman et al., 2007).
---

# SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2). 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:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) <!-- at revision fa97f6e7cb1a59073dff9e6b13e2715cf7475ac9 -->
- **Maximum Sequence Length:** 350 tokens
- **Output Dimensionality:** 384 dimensions
- **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': 350, '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})
  (2): Normalize()
)
```

## 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("zihoo/all-MiniLM-L6-v2-WMGPL")
# Run inference
sentences = [
    'what is mindfulness?',
    'Workplace Mindfulness Mindfulness is also defined as a state (e.g., Bishop et al.,  2004; Good et al., 2016; Lau et al., 2006; Tanay & Bernstein,  2013) of being aware of and attentive to what is taking place  internally and externally at that moment (Good et al., 2016;  Lau et al., 2006; Tanay & Bernstein, 2013). For example, Lau  et al., (2006, p. 1447) described mindfulness as “a mode, or  state-like quality that is maintained only when attention to  experience is intentionally cultivated with an open, nonjudg mental orientation to experience.” More recently, Good et al.,  (2016, p. 117) defined mindfulness as “receptive attention to  and awareness of present events and experience.”',
    'Workplace Mindfulness Brown and Ryan (2003) further propose that, despite their  intertwined nature, distinctions exist between attention and  awareness—the insights gained by sustained awareness can  only be translated into specific actions by paying focused  attention to our behaviors or the tasks at hand (Martin,  1997). Hence, heightened attention to and awareness of  experiences and events should capture two different aspects  of mindfulness. Recent research has also emphasized that  attention and awareness should be distinguished from each  other because attention reflects an ever-changing factor of  consciousness, whereas awareness refers to a specific and  stable state of consciousness (Selart et al., in press). In the  past, attention and awareness have proved important to the  study of mindfulness-promoting practices (Brown & Ryan,  2004), as some of these practices highlight focused attention  whereas others emphasize awareness (Bishop et al., 2004).  Notably, research has yielded empirical support confirming  these distinctions (Feldman et al., 2007).',
]
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: 160,000 training samples
* Columns: <code>sentence_0</code>, <code>sentence_1</code>, <code>sentence_2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence_0                                                                      | sentence_1                                                                           | sentence_2                                                                           | label                                                              |
  |:--------|:--------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------|
  | type    | string                                                                          | string                                                                               | string                                                                               | float                                                              |
  | details | <ul><li>min: 5 tokens</li><li>mean: 9.0 tokens</li><li>max: 25 tokens</li></ul> | <ul><li>min: 94 tokens</li><li>mean: 254.31 tokens</li><li>max: 350 tokens</li></ul> | <ul><li>min: 94 tokens</li><li>mean: 253.05 tokens</li><li>max: 350 tokens</li></ul> | <ul><li>min: -9.79</li><li>mean: 3.84</li><li>max: 20.17</li></ul> |
* Samples:
  | sentence_0                                    | sentence_1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               | sentence_2                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               | label                            |
  |:----------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------|
  | <code>why is mindfulness used at work</code>  | <code>Assessing Facets of Doing so, the present endeavor makes three contributions to the literature.  First, using the multidimensional scale developed in the present work, we  will provide first insights into the differential validities of subfacets of mind fulness for key work outcomes. This will foster a refined understanding of  the mechanisms of action inherent to mindfulness and help understand why  mindfulness matters for which work outcome (Bishop, Lau, Shapiro, Carlson,  Anderson, Carmody, Segal, Abbey, Speca, Velting, & Devins, 2004; Shapiro,  Carlson, Astin, & Freedman, 2006). Second, although prominent mindfulness  theories and scholarly work on mindfulness in the clinical area suggest that  mindfulness consists of multiple subfacets (Baer et al., 2006; Bishop et al.,  2004), research on mindfulness in the context of work has almost exclusively  operationalized mindfulness by assessing the awareness component (for an  exception see Liang et al., 2017). This bears the risk of con...</code> | <code>Assessing Facets of Over the last 7 years, research into mindfulness in the context of work  has been gaining momentum and there is a growing body of research pro viding initial evidence on the benefits of mindfulness for core workplace  outcomes. Especially health and well-being-related outcomes have been at  the center of research attention, but also interpersonal relationships, lead ership and performance outcomes (for reviews and meta-analyses see Eby,  Allen, Conley, Williamson, Henderson, & Mancini, 2019; Good, Lyddy,  Glomb, Bono, Brown, Duffy, Baer, Brewer, & Lazar, 2016; Mesmer-Magnus,  Manapragada, Viswesvaran, & Allen, 2017). Also, practitioners have become  increasingly interested in mindfulness and its applications in the context of  work. Organizations including Google, AETNA, IBM, or SAP, have started  offering mindfulness trainings to their workforce (Hyland, Lee, & Mills,  2015). With the first empirical studies appearing in the scientific IO literature  8 years ago (H...</code> | <code>-1.3994250297546387</code> |
  | <code>who developed mindfulness scales</code> | <code>MAAS FMI A variety of measures of mindfulness have been constructed  such as the MAAS (Brown & Ryan, 2003), the FMI (Buchheld  et al., 2001), the Toronto Mindfulness Scale (TMS) (Lau et al.,  2006), the Kentucky Inventory of Mindfulness (KIMS) (Baer,  Smith & Allen, 2004), the Cognitive and Affective Mindfulness  Scale (Feldman, Hayes, Kumar, Greeson & Laurenceau, 2007)  and the Southampton Mindfulness Questionnaire (Chadwick,  Hember, Symes, Peters, Kuipers, & Dagnan, 2008). These  scales differ because some measure mindfulness as a  unidimensional construct versus a multi-faceted construct  (Baer et al., 2006), while others measure mindfulness as a  trait-like or state-like construct (Dane, 2011). Some consider  only the mental state, whereas others include bodily  sensations and experience (Grossman, 2008). Furthermore,  some measures (e.g. KIMS) include the reported ability to  verbally describe experience (e.g. ‘I am good at finding the  words to describe my feelings’), while othe...</code> | <code>Workplace Mindfulness Mindfulness is widely considered as “paying attention in a par ticular way: on purpose, in the present moment, and nonjudg mentally” (Kabat-Zinn, 1994, p. 4). However, scholars have  not reached a consensus on the essential features of mindful ness, with various conceptualizations such as a set of skills, a state, a trait, and a cognitive process. In what follows, we sum marize the prevailing views of mindfulness in the literature.</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   | <code>8.103286743164062</code>   |
  | <code>what measures mindfulness</code>        | <code>Workplace Mindfulness Scholars have developed several measures of mindfulness  (Table 1). These measures help us understand the construct  of mindfulness, but they are very different in terms of con ceptualization, factor structure, scoring, reliability, and  validity. For example, the Freiburg Mindfulness Inventory  (FMI; Buchheld et al., 2001) and Toronto Mindfulness Scale  (TMS; Lau et al., 2006) were developed to measure states  of mindfulness. The Mindfulness Attention and Awareness  Scale (MAAS; Brown & Ryan, 2003), Cognitive and Affec tive Mindfulness Scale—Revised (CAMS-R; Feldman et al.,  2007), and Philadelphia Mindfulness Questionnaire (PMQ;  Cardaciotto et al., 2008) have been employed to measure  mindfulness as a trait. The Five Facet Mindfulness Question naire (FFMQ; Baer et al., 2006), Experiences Questionnaire  (EQ; Fresco et al., 2007), and Kentucky Inventory of Mind fulness Skills (KIMS; Baer et al., 2004) seek to measure  mindfulness skills. The Southampton Mindfulne...</code> | <code>Workplace Mindfulness Given this background, our conceptualization is expected  to be appropriate and valuable in the workplace because  compared with the general mindfulness scales, the Work place Mindfulness Scale can measure individual mindfulness in the work context more accurately and relevantly. Practi cally speaking, adopting a skill perspective emphasizing  the variability of mindfulness provides useful guidance to  employees and organizations, as they aim to improve indi viduals’ mindfulness by implementing interventions. The  skill view also assumes a degree of stability for mindful ness—that is, this construct is influenced by contextual fac tors but remains steady over a period of time.</code>                                                                                                                                                                                                                                                                                                         | <code>1.8723740577697754</code>  |
* Loss: <code>gpl.toolkit.loss.MarginDistillationLoss</code>

### Training Hyperparameters
#### Non-Default Hyperparameters

- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `num_train_epochs`: 1
- `max_steps`: 10000
- `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
- `torch_empty_cache_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`: 10000
- `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`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `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
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin

</details>

### Training Logs
| Epoch | Step  | Training Loss |
|:-----:|:-----:|:-------------:|
| 0.05  | 500   | 32.954        |
| 0.1   | 1000  | 29.8033       |
| 0.15  | 1500  | 29.0685       |
| 0.2   | 2000  | 29.799        |
| 0.25  | 2500  | 28.8365       |
| 0.3   | 3000  | 28.9655       |
| 0.35  | 3500  | 29.0616       |
| 0.4   | 4000  | 29.378        |
| 0.45  | 4500  | 29.0731       |
| 0.5   | 5000  | 27.8961       |
| 0.55  | 5500  | 28.9225       |
| 0.6   | 6000  | 29.1866       |
| 0.65  | 6500  | 28.4707       |
| 0.7   | 7000  | 28.291        |
| 0.75  | 7500  | 28.4819       |
| 0.8   | 8000  | 28.5333       |
| 0.85  | 8500  | 27.9674       |
| 0.9   | 9000  | 29.8078       |
| 0.95  | 9500  | 27.0718       |
| 1.0   | 10000 | 29.6496       |


### Framework Versions
- Python: 3.11.11
- Sentence Transformers: 3.3.1
- Transformers: 4.47.1
- PyTorch: 2.5.1+cu124
- Accelerate: 1.2.1
- Datasets: 3.2.0
- Tokenizers: 0.21.0

## 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",
}
```

<!--
## 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.*
-->