Text Classification
Transformers
Safetensors
English
qwen2
reward-model
code-generation
rlhf
text-embeddings-inference
Instructions to use Rishubi/CodeRM-NT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Rishubi/CodeRM-NT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Rishubi/CodeRM-NT")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Rishubi/CodeRM-NT") model = AutoModelForSequenceClassification.from_pretrained("Rishubi/CodeRM-NT") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b3712895c6d34f1fd429ef8ff302635bcbe895659754c7c31be636b28f7a546f
- Size of remote file:
- 11.4 MB
- SHA256:
- 694f1174c5bdf94e2fc50796c0f1733a5a3945ff110b0dfa40ea0701cc9c9c42
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