Text Classification
Transformers
Safetensors
deberta-v2
Generated from Trainer
trl
reward-trainer
text-embeddings-inference
Instructions to use Christine-HiAiPerf/reward_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Christine-HiAiPerf/reward_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Christine-HiAiPerf/reward_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Christine-HiAiPerf/reward_model") model = AutoModelForSequenceClassification.from_pretrained("Christine-HiAiPerf/reward_model") - Notebooks
- Google Colab
- Kaggle
Model Card for reward_model
This model is a fine-tuned version of microsoft/deberta-v3-base. It has been trained using TRL.
Quick start
from transformers import pipeline
text = "The capital of France is Paris."
rewarder = pipeline(model="None", device="cuda")
output = rewarder(text)[0]
print(output["score"])
Training procedure
This model was trained with Reward.
Framework versions
- TRL: 0.24.0
- Transformers: 4.57.3
- Pytorch: 2.9.1
- Datasets: 4.3.0
- Tokenizers: 0.22.2
Citations
Cite TRL as:
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
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Model tree for Christine-HiAiPerf/reward_model
Base model
microsoft/deberta-v3-base