Text Classification
Transformers
ONNX
Safetensors
GGUF
Transformers.js
English
modernbert
shell
security
coding-agents
multi-label-classification
text-embeddings-inference
Instructions to use jensilo/acclass with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jensilo/acclass with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="jensilo/acclass")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("jensilo/acclass") model = AutoModelForSequenceClassification.from_pretrained("jensilo/acclass", device_map="auto") - Transformers.js
How to use jensilo/acclass with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('text-classification', 'jensilo/acclass'); - Notebooks
- Google Colab
- Kaggle
Download thresholds.json from jensilo/acclass: direct link, hf CLI and curl.
- Browser
- Download file 227 Bytes
-
https://huggingface.co/jensilo/acclass/resolve/main/thresholds.json
- Command line
-
hf download hf://jensilo/acclass/thresholds.json
-
curl -L -o thresholds.json https://huggingface.co/jensilo/acclass/resolve/main/thresholds.json
227 Bytes
| {"local_state_change": 0.5, "outbound": 0.5, "egress": 0.5, "remote_state_change": 0.5, "delegated_execution": 0.5, "exposes_secrets": 0.5, "destructive": 0.5, "host_control_change": 0.5, "fetched_or_generated_execution": 0.5} | |