Text Classification
Transformers
PyTorch
TensorBoard
Safetensors
English
roberta
webgpt
regression
reward-model
text-embeddings-inference
Instructions to use theblackcat102/roberta-base-webgpt-rm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use theblackcat102/roberta-base-webgpt-rm with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="theblackcat102/roberta-base-webgpt-rm")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("theblackcat102/roberta-base-webgpt-rm") model = AutoModelForSequenceClassification.from_pretrained("theblackcat102/roberta-base-webgpt-rm", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 22d7d5ffb41de378c7f39b32d14d077f013ff81f8991c370afa596ba60985807
- Size of remote file:
- 499 MB
- SHA256:
- 5bfaaa6b92436e62cc8a050f66fa7a26ba83c4322ad4998636535b9c5763a0d8
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