Instructions to use LACAI/roberta-large-adapted-PFG-progression with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LACAI/roberta-large-adapted-PFG-progression with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="LACAI/roberta-large-adapted-PFG-progression")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("LACAI/roberta-large-adapted-PFG-progression") model = AutoModelForSequenceClassification.from_pretrained("LACAI/roberta-large-adapted-PFG-progression", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7ce3331d8361d1f795fafff46a7dba4e1fb4d4dc84687fa5e89fb1568acf5a50
- Size of remote file:
- 1.42 GB
- SHA256:
- 197cc3f172570b449626413c24ec0ce768d339101b28827c3310b182576a50cb
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