Instructions to use Trung/gpt2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Trung/gpt2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Trung/gpt2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Trung/gpt2") model = AutoModelForSequenceClassification.from_pretrained("Trung/gpt2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a6f647ee0bd746658f6c80fdf2075f1aa35f1dd6945ed413b097e247f2b151ea
- Size of remote file:
- 510 MB
- SHA256:
- cdc6e055adbabcbd882050d539e9a54558730c1261a4e63535df50388673f5f7
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