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zihoo
/
all-MiniLM-L6-v2-WMGPL

Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:160000
loss:MarginDistillationLoss
text-embeddings-inference
Model card Files Files and versions
xet
Community

Instructions to use zihoo/all-MiniLM-L6-v2-WMGPL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use zihoo/all-MiniLM-L6-v2-WMGPL with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("zihoo/all-MiniLM-L6-v2-WMGPL")
    
    sentences = [
        "why is it a healthy thing to be in a dynamic work environment?",
        "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.”"
    ]
    embeddings = model.encode(sentences)
    
    similarities = model.similarity(embeddings, embeddings)
    print(similarities.shape)
    # [4, 4]
  • Notebooks
  • Google Colab
  • Kaggle
all-MiniLM-L6-v2-WMGPL
91.9 MB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 4 commits
zihoo's picture
zihoo
Add new SentenceTransformer model.
f62c771 verified over 1 year ago
  • 1_Pooling
    Add new SentenceTransformer model. over 1 year ago
  • .gitattributes
    1.52 kB
    initial commit over 1 year ago
  • README.md
    42.9 kB
    Add new SentenceTransformer model. over 1 year ago
  • config.json
    657 Bytes
    Add new SentenceTransformer model. over 1 year ago
  • config_sentence_transformers.json
    205 Bytes
    Add new SentenceTransformer model. over 1 year ago
  • model.safetensors
    90.9 MB
    xet
    Add new SentenceTransformer model. over 1 year ago
  • modules.json
    349 Bytes
    Add new SentenceTransformer model. over 1 year ago
  • sentence_bert_config.json
    53 Bytes
    Add new SentenceTransformer model. over 1 year ago
  • special_tokens_map.json
    695 Bytes
    Add new SentenceTransformer model. over 1 year ago
  • tokenizer.json
    712 kB
    Add new SentenceTransformer model. over 1 year ago
  • tokenizer_config.json
    1.46 kB
    Add new SentenceTransformer model. over 1 year ago
  • vocab.txt
    232 kB
    Add new SentenceTransformer model. over 1 year ago