Text Classification
Transformers
PyTorch
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use Tristan/distilbert_summarization_reward_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Tristan/distilbert_summarization_reward_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Tristan/distilbert_summarization_reward_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Tristan/distilbert_summarization_reward_model") model = AutoModelForSequenceClassification.from_pretrained("Tristan/distilbert_summarization_reward_model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 52617a83d4dfe26441c5fab581e8f1450319a8c0982a0e337de7634dd4952451
- Size of remote file:
- 268 MB
- SHA256:
- 636c939d0a39909487e487ec279f6f32d6612664ee57fd274040b6021fa07258
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.