Text Classification
Transformers
Safetensors
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use OliverHeine/roberta-base_fold_3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OliverHeine/roberta-base_fold_3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="OliverHeine/roberta-base_fold_3")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("OliverHeine/roberta-base_fold_3") model = AutoModelForSequenceClassification.from_pretrained("OliverHeine/roberta-base_fold_3") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a19ba99955a3447935670a44a6956cdc53f034a6e3b19c00ba2656eeec39756f
- Size of remote file:
- 5.27 kB
- SHA256:
- 3a691304151cf49dd2aecb150ca5edc0f538927d7e476f8c243eae51a060552a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.