Text Classification
Transformers
TensorBoard
Safetensors
deberta-v2
Trained with AutoTrain
text-embeddings-inference
Instructions to use luukschmitz/Geodeberta2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use luukschmitz/Geodeberta2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="luukschmitz/Geodeberta2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("luukschmitz/Geodeberta2") model = AutoModelForSequenceClassification.from_pretrained("luukschmitz/Geodeberta2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5f8b90cf31323d82cf75990a8c62f275a9b4b5d65fe37b42baa76a436317ec51
- Size of remote file:
- 738 MB
- SHA256:
- b455d855ad607c9d001e2714b45efe7ac989414765a467165601716df6bb515c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.