Instructions to use wiorz/gpt2_small_summarized with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use wiorz/gpt2_small_summarized with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="wiorz/gpt2_small_summarized")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("wiorz/gpt2_small_summarized") model = AutoModelForSequenceClassification.from_pretrained("wiorz/gpt2_small_summarized", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 12ca0d8556e20a813bf6b0ef5ba9097362ce7de78d459a30c7cd7337e0e5b720
- Size of remote file:
- 3.58 kB
- SHA256:
- 59a70046b1e5dcfb70622be96e40ff3e6999cd8ebf290d88f9553a5828f0a694
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.