Text Classification
Transformers
Safetensors
xlm-roberta
Generated from Trainer
text-embeddings-inference
Instructions to use egerber1/classifier-de1_roberta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use egerber1/classifier-de1_roberta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="egerber1/classifier-de1_roberta")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("egerber1/classifier-de1_roberta") model = AutoModelForSequenceClassification.from_pretrained("egerber1/classifier-de1_roberta", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from egerber1/classifier-de1_roberta: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/egerber1/classifier-de1_roberta/resolve/main/tokenizer.json
- Command line
-
hf download hf://egerber1/classifier-de1_roberta/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/egerber1/classifier-de1_roberta/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- 78e0207f06be9d76f5db8bce779072abd26598f2bdd31075a0cefdfacf3398b2
- Size of remote file:
- 17.1 MB
- SHA256:
- 3ffb37461c391f096759f4a9bbbc329da0f36952f88bab061fcf84940c022e98
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.