Text Classification
Transformers
PyTorch
English
deberta-v2
rlhf
Eval Results (legacy)
text-embeddings-inference
Instructions to use sileod/deberta-v3-large-tasksource-rlhf-reward-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sileod/deberta-v3-large-tasksource-rlhf-reward-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="sileod/deberta-v3-large-tasksource-rlhf-reward-model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("sileod/deberta-v3-large-tasksource-rlhf-reward-model") model = AutoModelForSequenceClassification.from_pretrained("sileod/deberta-v3-large-tasksource-rlhf-reward-model") - Notebooks
- Google Colab
- Kaggle
Can you share your test code? I cannot produce it
#3 opened over 2 years ago
by
heegyu
Adding `safetensors` variant of this model
#2 opened almost 3 years ago
by
SFconvertbot
where is the example ?
5
#1 opened about 3 years ago
by
chuangzhidian