Text Classification
Transformers
Safetensors
German
electra
historical-newspapers
ocr
job-advertisements
digital-humanities
Instructions to use Var3n/jobads-identification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Var3n/jobads-identification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Var3n/jobads-identification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Var3n/jobads-identification") model = AutoModelForSequenceClassification.from_pretrained("Var3n/jobads-identification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download optimizer.pt from Var3n/jobads-identification: direct link, hf CLI and curl.
- Browser
- Download file 1.08 GB
-
https://huggingface.co/Var3n/jobads-identification/resolve/main/optimizer.pt
- Command line
-
hf download hf://Var3n/jobads-identification/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/Var3n/jobads-identification/resolve/main/optimizer.pt
1.08 GB
- Xet hash:
- 3b05e5526125890b080172f3ac52ee4fc25da8288b9b8ac262c4376ccbdb4d56
- Size of remote file:
- 1.08 GB
- SHA256:
- 24bbf74b413753f71a3b51565e14333546da912b8a116dd1df1af5dc90e41f50
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.