Instructions to use LACAI/roberta-base-PFG-progression with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LACAI/roberta-base-PFG-progression with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="LACAI/roberta-base-PFG-progression")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("LACAI/roberta-base-PFG-progression") model = AutoModelForSequenceClassification.from_pretrained("LACAI/roberta-base-PFG-progression", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5f3316f5a5794f69458314753d74f6de4493794c30bf04f217e94522809381eb
- Size of remote file:
- 499 MB
- SHA256:
- 1c60d38459d243f96d8eb100f1024b690a7a0128894a96b372a018d24f26d0a8
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