Text Classification
Transformers
TensorBoard
Safetensors
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use SudiptoPramanik/RewardModelSmallerQuestionWithTwoLabelsLengthJustified with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SudiptoPramanik/RewardModelSmallerQuestionWithTwoLabelsLengthJustified with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="SudiptoPramanik/RewardModelSmallerQuestionWithTwoLabelsLengthJustified")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("SudiptoPramanik/RewardModelSmallerQuestionWithTwoLabelsLengthJustified") model = AutoModelForSequenceClassification.from_pretrained("SudiptoPramanik/RewardModelSmallerQuestionWithTwoLabelsLengthJustified", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download adapter_model.safetensors from SudiptoPramanik/RewardModelSmallerQuestionWithTwoLabelsLengthJustified: direct link, hf CLI and curl.
- Browser
- Download file 5.01 MB
-
https://huggingface.co/SudiptoPramanik/RewardModelSmallerQuestionWithTwoLabelsLengthJustified/resolve/main/adapter_model.safetensors
- Command line
-
hf download hf://SudiptoPramanik/RewardModelSmallerQuestionWithTwoLabelsLengthJustified/adapter_model.safetensors
-
curl -L -o adapter_model.safetensors https://huggingface.co/SudiptoPramanik/RewardModelSmallerQuestionWithTwoLabelsLengthJustified/resolve/main/adapter_model.safetensors
5.01 MB
- Xet hash:
- 4ec01a175f7433233075c7a570bc51e9b6f1b9f6a92ad1391dd45aa22d3437d4
- Size of remote file:
- 5.01 MB
- SHA256:
- 138b66bdbcf6f7c9196c3fdb6fc1d4d6c2e1a5c1cd2decfc6f6895976f39fcac
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