Instructions to use robvanderg/Sem-RemmmBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use robvanderg/Sem-RemmmBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="robvanderg/Sem-RemmmBERT")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("robvanderg/Sem-RemmmBERT") model = AutoModel.from_pretrained("robvanderg/Sem-RemmmBERT") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6b871214b3e8d278eeb01142c9e7bb1d72db8c11b8547ac6459befd131437f89
- Size of remote file:
- 2.3 GB
- SHA256:
- e0cd4011b2c65f303f937e943315bae387305488b9272056f5499f80ec158871
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