Instructions to use peter2000/laya-vulnerability-groups-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use peter2000/laya-vulnerability-groups-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="peter2000/laya-vulnerability-groups-v2")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("peter2000/laya-vulnerability-groups-v2", device_map="auto") - Laya
How to use peter2000/laya-vulnerability-groups-v2 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
Download metrics.json from peter2000/laya-vulnerability-groups-v2: direct link, hf CLI and curl.
- Browser
- Download file 1.87 kB
-
https://huggingface.co/peter2000/laya-vulnerability-groups-v2/resolve/main/metrics.json
- Command line
-
hf download hf://peter2000/laya-vulnerability-groups-v2/metrics.json
-
curl -L -o metrics.json https://huggingface.co/peter2000/laya-vulnerability-groups-v2/resolve/main/metrics.json
1.87 kB
| { | |
| "zero_shot": { | |
| "macro_f1": 0.16297938893094602, | |
| "micro_f1": 0.14937759336099585, | |
| "ece": 0.06287950464396284, | |
| "subset_accuracy": 0.07368421052631578, | |
| "per_label_f1": { | |
| "Agricultural communities": 0.3077, | |
| "Coastal communities": 0.1176, | |
| "Ethnic, racial or other minorities": 0.0, | |
| "Fishery communities": 0.0, | |
| "Informal sector workers": 0.0, | |
| "Members of indigenous and local communities": 0.3333, | |
| "Migrants and displaced persons": 0.2857, | |
| "Older persons": 0.1667, | |
| "Other": 0.0667, | |
| "Persons living in poverty": 0.3333, | |
| "Persons with disabilities": 0.1818, | |
| "Persons with pre-existing health conditions": 0.3333, | |
| "Residents of drought-prone regions": 0.0, | |
| "Rural populations": 0.1333, | |
| "Sexual minorities (LGBTQI+)": 0.4, | |
| "Urban populations": 0.1111, | |
| "Women and other genders": 0.0 | |
| }, | |
| "eval_seconds": 10.5 | |
| }, | |
| "fine_tuned": { | |
| "macro_f1": 0.7038424916941541, | |
| "micro_f1": 0.7083333333333334, | |
| "ece": 0.02415609907120738, | |
| "subset_accuracy": 0.47368421052631576, | |
| "per_label_f1": { | |
| "Agricultural communities": 0.9167, | |
| "Coastal communities": 0.5714, | |
| "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": 0.6667, | |
| "Older persons": 0.7778, | |
| "Other": 0.0, | |
| "Persons living in poverty": 0.6087, | |
| "Persons with disabilities": 1.0, | |
| "Persons with pre-existing health conditions": 1.0, | |
| "Residents of drought-prone regions": 0.8, | |
| "Rural populations": 0.8889, | |
| "Sexual minorities (LGBTQI+)": 0.3333, | |
| "Urban populations": 0.5455, | |
| "Women and other genders": 0.9231 | |
| }, | |
| "eval_seconds": 8.6 | |
| } | |
| } |