Text Classification
Transformers
Safetensors
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use Keithsu/sawyer-reward with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Keithsu/sawyer-reward with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Keithsu/sawyer-reward")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Keithsu/sawyer-reward") model = AutoModelForSequenceClassification.from_pretrained("Keithsu/sawyer-reward", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Keithsu/sawyer-reward: direct link, hf CLI and curl.
- Browser
- Download file 5.27 kB
-
https://huggingface.co/Keithsu/sawyer-reward/resolve/main/training_args.bin
- Command line
-
hf download hf://Keithsu/sawyer-reward/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Keithsu/sawyer-reward/resolve/main/training_args.bin
5.27 kB
- Xet hash:
- 64add20a7cb4651e248ed4bc78bd6d162546e71d3df0d41955f8d70f8202c993
- Size of remote file:
- 5.27 kB
- SHA256:
- 59d165500d56043cf5f770085350fb030d6c1a2341dce2627e72499d6c670525
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