Instructions to use shcapybara0925/distilroberta-base-finetuned-github_cybersecurity_READMEs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use shcapybara0925/distilroberta-base-finetuned-github_cybersecurity_READMEs with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="shcapybara0925/distilroberta-base-finetuned-github_cybersecurity_READMEs")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("shcapybara0925/distilroberta-base-finetuned-github_cybersecurity_READMEs") model = AutoModelForMaskedLM.from_pretrained("shcapybara0925/distilroberta-base-finetuned-github_cybersecurity_READMEs", device_map="auto") - Notebooks
- Google Colab
- Kaggle
distilroberta-base-finetuned-github_cybersecurity_READMEs
This model is a fine-tuned version of distilroberta-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.6235
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.7433 | 1.0 | 3863 | 1.7505 |
| 1.6444 | 2.0 | 7726 | 1.6638 |
| 1.5599 | 3.0 | 11589 | 1.6201 |
Framework versions
- Transformers 4.40.0.dev0
- Pytorch 2.2.1+cu118
- Datasets 2.18.0
- Tokenizers 0.15.2
- Downloads last month
- 25
Model tree for shcapybara0925/distilroberta-base-finetuned-github_cybersecurity_READMEs
Base model
distilbert/distilroberta-base