Text Classification
Transformers
Safetensors
German
electra
historical-newspapers
job-advertisements
digital-humanities
Instructions to use Var3n/jobads-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Var3n/jobads-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Var3n/jobads-classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Var3n/jobads-classification") model = AutoModelForSequenceClassification.from_pretrained("Var3n/jobads-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download label_encoder.pkl from Var3n/jobads-classification: direct link, hf CLI and curl.
- Browser
- Download file 307 Bytes
-
https://huggingface.co/Var3n/jobads-classification/resolve/main/label_encoder.pkl
- Command line
-
hf download hf://Var3n/jobads-classification/label_encoder.pkl
-
curl -L -o label_encoder.pkl https://huggingface.co/Var3n/jobads-classification/resolve/main/label_encoder.pkl
307 Bytes
- Xet hash:
- 2e71183c8865694db6ffc45c1a8c45b4e7aef968f1408c2cc5bea7a00fc2b367
- Size of remote file:
- 307 Bytes
- SHA256:
- fc6e0c903244e45707a0681be81fcda6216ddc23feb1fec0ee177b1b4a69aceb
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