Instructions to use peter2000/laya-vulnerability-groups with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use peter2000/laya-vulnerability-groups with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="peter2000/laya-vulnerability-groups")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("peter2000/laya-vulnerability-groups", device_map="auto") - Laya
How to use peter2000/laya-vulnerability-groups with Laya:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
File size: 1,853 Bytes
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"zero_shot": {
"macro_f1": 0.10825267159446408,
"micro_f1": 0.11764705882352941,
"ece": 0.036188173374613,
"subset_accuracy": 0.08421052631578947,
"per_label_f1": {
"Agricultural communities": 0.087,
"Coastal communities": 0.125,
"Ethnic, racial or other minorities": 0.0,
"Fishery communities": 0.0,
"Informal sector workers": 0.2857,
"Members of indigenous and local communities": 0.1429,
"Migrants and displaced persons": 0.0,
"Older persons": 0.1818,
"Other": 0.0588,
"Persons living in poverty": 0.381,
"Persons with disabilities": 0.0,
"Persons with pre-existing health conditions": 0.25,
"Residents of drought-prone regions": 0.0,
"Rural populations": 0.1176,
"Sexual minorities (LGBTQI+)": 0.0,
"Urban populations": 0.2105,
"Women and other genders": 0.0
},
"eval_seconds": 10.3
},
"fine_tuned": {
"macro_f1": 0.6543859533629355,
"micro_f1": 0.6694214876033058,
"ece": 0.026282352941176475,
"subset_accuracy": 0.47368421052631576,
"per_label_f1": {
"Agricultural communities": 0.9565,
"Coastal communities": 0.5,
"Ethnic, racial or other minorities": 0.6,
"Fishery communities": 0.4,
"Informal sector workers": 1.0,
"Members of indigenous and local communities": 0.9333,
"Migrants and displaced persons": 0.5714,
"Older persons": 0.8889,
"Other": 0.0,
"Persons living in poverty": 0.3333,
"Persons with disabilities": 1.0,
"Persons with pre-existing health conditions": 0.8,
"Residents of drought-prone regions": 0.5714,
"Rural populations": 0.8,
"Sexual minorities (LGBTQI+)": 0.3333,
"Urban populations": 0.5714,
"Women and other genders": 0.8649
},
"eval_seconds": 8.4
}
} |