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:
- f73d5ee132eed971026f3454350d42b35d31c6898201a6f9800f8dd1cd224969
- Size of remote file:
- 499 MB
- SHA256:
- 062d8026ff9cbfa5c979897faa5e7e871d9078b62a393d8abd53388bd050a420
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.