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
what score is high quality
#11 opened over 1 year ago
by
aj666
Hyperparameters training setting
#10 opened over 2 years ago
by
hyuk199
synthetic-instruct-gptj-pairwise pairwise data how to pre-process for train data
2
#9 opened over 2 years ago
by
chaochaoli
How to fine tune this model with the Trainer API?
👍 1
1
#8 opened over 2 years ago
by
duzm
How to score a <instruction, input, output> pair?
#7 opened over 2 years ago
by
qldu
Validation split indices?
👍 2
1
#6 opened almost 3 years ago
by
cmglaze
np.int deprecation issue
❤️ 1
#5 opened almost 3 years ago
by
whiteg671
Question about evaluating this reward model on Anthropic/hh-rlhf
1
#4 opened about 3 years ago
by
songff
Adding `safetensors` variant of this model
#3 opened about 3 years ago
by
SFconvertbot
How to optimize loss function?
1
#1 opened over 3 years ago
by
nidong