Hugging Face's logo Hugging Face
  • Models
  • Datasets
  • Spaces
  • Buckets new
  • Docs
  • Enterprise
  • Pricing
    • Website
      • Tasks
      • HuggingChat
      • Collections
      • Languages
      • Organizations
    • Community
      • Blog
      • Posts
      • Daily Papers
      • Hardware
      • Learn
      • Discord
      • Forum
      • GitHub
    • Solutions
      • Team & Enterprise
      • Hugging Face PRO
      • Enterprise Support
      • Inference Providers
      • Inference Endpoints
      • Storage Buckets

  • Log In
  • Sign Up

MaxNoichl
/
discipline-tuned_specter_2_024

Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:43494
loss:TripletLoss
Eval Results (legacy)
text-embeddings-inference
Model card Files Files and versions
xet
Community

Instructions to use MaxNoichl/discipline-tuned_specter_2_024 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use MaxNoichl/discipline-tuned_specter_2_024 with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("MaxNoichl/discipline-tuned_specter_2_024")
    
    sentences = [
        "As a result of technological progress, environmental aspects and social change, the automotive industry is undergoing a radical transformation. The focus is no longer on the product \"vehicle\" but much more on the mobility service itself and the users individual experience and well-being during travel time. In that field of innovation, the study deals with a explorative investigation of using the travel time for a improvement of the mental health of the passenger. The vision is to integrate breathwork relaxation in combination with a human centric lighting scenario as an immersive service within luxury ride-hailing vehicles to enhance the mental health during automated rides and utilizing the time spent in cars for personal pleasure. To enable a user-centered and experimental approach, a test vehicle from the non-profit company bq.Labs was equipped with the bq breath work app and a spezialized LED-based lighting screen that was developed by Fraunhofer. The effects were tested on randomly selected and voluntary users in a guerrilla testing at three different locations in San Diego. The tests explored user acceptance of the innovative technologies by combining surveys, vital data collection, qualitative interviews and observations. Initial data analysis provides insights into the feasibility and potential effects on well-being and user perception. The study illustrates those innovations in the field of mobility, involve systemic dependencies and considerations beyond technology, encompassing social and psychological dimensions. It underscores that successful innovations require a holistic, user-centered approach that considers technological, social, and psychological dimensions. The findings lay the groundwork for future research and development of innovation strategies in the evolving field of mobility and personalized strength.",
        "We examined casual decision-making among a group of participants, which frequently occurs in daily life. In such a situation, participants do not have strong preferences for the decision. In addition, because the process of decision-making among people is part of the time they spend together, it is important to feel enjoyment in the process and satisfaction with the final decision. In this paper, we propose a game mechanism for generating a sense of enjoyment in the decision-making process through communication and a sense of acceptance of the final decision. We experimentally compared two ways to make decisions about beverages: ) majority voting and ) the proposed game. In the latter case, the participants enjoyed playing the game and were satisfied with the decision-making process.",
        "This paper presents several important factors affecting the resale prices of used rental cars. In fact, this paper empirically shows and proves several conjectures regarding the determinants for used rental car resale values through the use of detailed micro data from one of the biggest rental car companies. Specifically, the age of a used car has two composite effects on its resale value, even though overall the two effects work negatively with a concavity, as rental cars ages. On the other hand, two mileage variables interact with each other and produce overall decreasing effects on the resale prices with the opposite interactions. In terms of the effects of brand image, Hyundai and Renault-Samsung have positive effects on resale values generally. Ssangyong has a positive effect on the resale values in the SUV category, and Kia and GM-Daewoo are generally inferior to the other brands in terms of resale values in all categories. In terms of seasonal effects, we can conclude that this paper confirms the general perception regarding seasonal effects on resale values. In details, from November to February, resale values are affected negatively, and March is the recovering month of increasing demand in the used car market. August seems to be the highest season for the used car market due to several demand increases. As a result, this paper plays an important role in providing a substantial amount of information on the factors affecting the resale prices of rental cars.",
        "In this paper we present an approach used to enhance students' competency in software verification. Students were asked to apply software verification techniques to a complex formal specification system. The complexity of the system stems from its sophisticated requirements. Selecting such system for this study was intentional for the following two reasons ) the system is difficult to understand and analyze because of the domain knowledge required to generate formal specifications in temporal logic and ) the system is large and complex which lends itself to a wide range of applicable verification techniques, and thus highlights the differences in the capabilities of each of the software verification approaches. Students were assessed using multiple criteria including; examination in applying learned techniques, students' attitude toward the technique, perceived efficiency of the techniques in discovering software defects, and the ability of the technique to locate errors in the code beyond simply indicating their presence. The results of this work show that the students applied the learned techniques successfully and their attitudes towards software verification improved."
    ]
    embeddings = model.encode(sentences)
    
    similarities = model.similarity(embeddings, embeddings)
    print(similarities.shape)
    # [4, 4]
  • Notebooks
  • Google Colab
  • Kaggle
discipline-tuned_specter_2_024
4.62 GB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 3 commits
m7n's picture
m7n
Added datafiles for opealexmapper
dcc9a69 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
  • 100k_filtered_OA_sample_cluster_and_positions_supervised.pkl
    1.19 GB
    xet
    Added datafiles for opealexmapper over 1 year ago
  • README.md
    79.7 kB
    Add new SentenceTransformer model over 1 year ago
  • config.json
    757 Bytes
    Add new SentenceTransformer model over 1 year ago
  • config_sentence_transformers.json
    210 Bytes
    Add new SentenceTransformer model over 1 year ago
  • model.safetensors
    440 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
    717 kB
    Add new SentenceTransformer model over 1 year ago
  • tokenizer_config.json
    1.27 kB
    Add new SentenceTransformer model over 1 year ago
  • umap_mapper_250k_random_OA_discipline_tuned_specter_2_params.pkl
    2.99 GB
    xet
    Added datafiles for opealexmapper over 1 year ago
  • vocab.txt
    228 kB
    Add new SentenceTransformer model over 1 year ago