Text Classification
Transformers
TensorBoard
Safetensors
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use SudiptoPramanik/RewardModel_RobertaBase with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SudiptoPramanik/RewardModel_RobertaBase with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="SudiptoPramanik/RewardModel_RobertaBase")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("SudiptoPramanik/RewardModel_RobertaBase") model = AutoModelForSequenceClassification.from_pretrained("SudiptoPramanik/RewardModel_RobertaBase", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from SudiptoPramanik/RewardModel_RobertaBase: direct link, hf CLI and curl.
- Browser
- Download file 5.37 kB
-
https://huggingface.co/SudiptoPramanik/RewardModel_RobertaBase/resolve/main/training_args.bin
- Command line
-
hf download hf://SudiptoPramanik/RewardModel_RobertaBase/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/SudiptoPramanik/RewardModel_RobertaBase/resolve/main/training_args.bin
5.37 kB
- Xet hash:
- 82453690bbea31b73597262e3f3ed1967e3ce1369f340cf08c196f51daea250e
- Size of remote file:
- 5.37 kB
- SHA256:
- e7a6d49997dbbe7638d553bf3988bb5d50f123d7e5396a797b1d05d1a009d041
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