Text Classification
Transformers
TensorBoard
Safetensors
deberta-v2
Generated from Trainer
text-embeddings-inference
Instructions to use selsar/target-abroad-de with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use selsar/target-abroad-de with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="selsar/target-abroad-de")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("selsar/target-abroad-de") model = AutoModelForSequenceClassification.from_pretrained("selsar/target-abroad-de", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from selsar/target-abroad-de: direct link, hf CLI and curl.
- Browser
- Download file 16 MB
-
https://huggingface.co/selsar/target-abroad-de/resolve/main/tokenizer.json
- Command line
-
hf download hf://selsar/target-abroad-de/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/selsar/target-abroad-de/resolve/main/tokenizer.json
16 MB
- Xet hash:
- e71c77d0ac0edbd185771c88b7db97c7c226b2f71d529760a43ddb5e77ba8b65
- Size of remote file:
- 16 MB
- SHA256:
- 6157112cd4c487f0cf0eafa49bf8395fe7199fdd12983d59380d1056800fa946
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