Instructions to use johannes-garstenauer/distilbert_masking_heaps with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use johannes-garstenauer/distilbert_masking_heaps with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="johannes-garstenauer/distilbert_masking_heaps")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("johannes-garstenauer/distilbert_masking_heaps") model = AutoModelForMaskedLM.from_pretrained("johannes-garstenauer/distilbert_masking_heaps") - Notebooks
- Google Colab
- Kaggle
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Check out the documentation for more information.
DistilBERT for masked language modelling trained on OpenSSH heap data structures dataset for the purpose of generating representations. This model was created for the thesis "Generating Robust Representations of Structures in OpenSSH Heap Dumps" by Johannes Garstenauer.
Model Description
- Developed by: Johannes Garstenauer
- Funded by [optional]: Universität Passau
Model Sources [optional]
- Repository: https://zenodo.org/records/10053730
Training Data
Training data: https://huggingface.co/datasets/johannes-garstenauer/structs_token_size_4_reduced_labelled_train Validation data: https://huggingface.co/datasets/johannes-garstenauer/structs_token_size_4_reduced_labelled_eval
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