Instructions to use hamishivi/fixed-roberta-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hamishivi/fixed-roberta-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hamishivi/fixed-roberta-large")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hamishivi/fixed-roberta-large") model = AutoModel.from_pretrained("hamishivi/fixed-roberta-large") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 66d61b6c6bc01119c6c0c518a3c0855af2fd70fe1490abc6de67c22edb700694
- Size of remote file:
- 1.42 GB
- SHA256:
- 3a5698e59ed7110dc0d32d45eedc234e165914efbf342a1b90d9123fa55d8b14
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