Text Classification
Transformers
Safetensors
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use Yuvrajg2107/roberta-base-cpp1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Yuvrajg2107/roberta-base-cpp1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Yuvrajg2107/roberta-base-cpp1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Yuvrajg2107/roberta-base-cpp1") model = AutoModelForSequenceClassification.from_pretrained("Yuvrajg2107/roberta-base-cpp1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9e3e6f1368d7ddcb7a4857d2a46539d9122636ca85d84cd7b156d6ce2b4a07c3
- Size of remote file:
- 499 MB
- SHA256:
- ba0cfcfae75cac3b1e3a9de6399eb42004558382974cabded94c595bf6d208de
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.