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:
- 8160b5c45f5cdaadb5d19dfaf294303a1709b7b3e403e77d55df9803ace64edc
- Size of remote file:
- 3.45 kB
- SHA256:
- 849fe5fe05af44a24077d48324fe67aa00af51d071c6053d06180dabedce4b9c
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