Text Classification
Transformers
PyTorch
English
deberta-v2
reward-model
reward_model
RLHF
text-embeddings-inference
Instructions to use OpenAssistant/reward-model-deberta-v3-large-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenAssistant/reward-model-deberta-v3-large-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="OpenAssistant/reward-model-deberta-v3-large-v2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("OpenAssistant/reward-model-deberta-v3-large-v2") model = AutoModelForSequenceClassification.from_pretrained("OpenAssistant/reward-model-deberta-v3-large-v2") - Inference
- Notebooks
- Google Colab
- Kaggle
synthetic-instruct-gptj-pairwise pairwise data how to pre-process for train data
#9
by chaochaoli - opened
All models are train on these dataset with a same split seed across datasets (if validation split wasn't available)
1、webgpt_comparisons
2、summarize_from_feedback
3、synthetic-instruct-gptj-pairwise
4、anthropic_hh-rlhf
all these data have different format,how to Processed into a unified form?
thks
hi
hi
oi