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:
- 3666ef6e7142e09c54be646a7e2c5a256251fd0ebfae3c122f4f0f208df98b18
- Size of remote file:
- 499 MB
- SHA256:
- 857ef817f0db5214e28852c563e709185764c56a72e0c3f9f7d43169fb17063c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.