Text Classification
Transformers
Safetensors
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use OliverHeine/roberta-base_fold_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OliverHeine/roberta-base_fold_2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="OliverHeine/roberta-base_fold_2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("OliverHeine/roberta-base_fold_2") model = AutoModelForSequenceClassification.from_pretrained("OliverHeine/roberta-base_fold_2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 12a19f3151fcdd82e114ef0a750d5a6e73ef9101ba61e052d7da9992f8d5adcd
- Size of remote file:
- 5.27 kB
- SHA256:
- a9f6fa19b419d1a8d338a1ad45f0d9d9533e45654e8c5c65e4327e8bfb82f1cc
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.