Text Classification
Transformers
TensorBoard
Safetensors
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use SudiptoPramanik/RewardModelSmallerQuestionWithTwoLabelsLengthJustified with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SudiptoPramanik/RewardModelSmallerQuestionWithTwoLabelsLengthJustified with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="SudiptoPramanik/RewardModelSmallerQuestionWithTwoLabelsLengthJustified")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("SudiptoPramanik/RewardModelSmallerQuestionWithTwoLabelsLengthJustified") model = AutoModelForSequenceClassification.from_pretrained("SudiptoPramanik/RewardModelSmallerQuestionWithTwoLabelsLengthJustified") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cc916bd29cee3c873790207d22d6bd01ef06a4df6649617a86e8d5a4b647333e
- Size of remote file:
- 499 MB
- SHA256:
- 8e00548ce4842c51154b42ae095126fa0cf79a268416768b9b26b9cc6f2d4c68
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.