Text Classification
Transformers
Safetensors
deberta-v2
Generated from Trainer
text-embeddings-inference
Instructions to use selsar/outputs_target_abroad_de_final with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use selsar/outputs_target_abroad_de_final with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="selsar/outputs_target_abroad_de_final")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("selsar/outputs_target_abroad_de_final") model = AutoModelForSequenceClassification.from_pretrained("selsar/outputs_target_abroad_de_final", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from selsar/outputs_target_abroad_de_final: direct link, hf CLI and curl.
- Browser
- Download file 16 MB
-
https://huggingface.co/selsar/outputs_target_abroad_de_final/resolve/main/tokenizer.json
- Command line
-
hf download hf://selsar/outputs_target_abroad_de_final/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/selsar/outputs_target_abroad_de_final/resolve/main/tokenizer.json
16 MB
- Xet hash:
- 4f3fe12064ea828a76b4b41d723af03eeb0d22b45bee3dd9be101874ee0541d9
- Size of remote file:
- 16 MB
- SHA256:
- a66269b2b7b04009c9e482ef6a99accee231b6822b83e7eb6df36626b61db43a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.