Instructions to use hamingsi/SpikingLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hamingsi/SpikingLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="hamingsi/SpikingLM")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("hamingsi/SpikingLM") model = AutoModelForMaskedLM.from_pretrained("hamingsi/SpikingLM") - Notebooks
- Google Colab
- Kaggle
| from .modeling_spiking_bert import ( | |
| BertForMaskedLM, | |
| BertForSequenceClassification, | |
| BertModel, | |
| BertPreTrainedModel, | |
| ) | |
| __all__ = [ | |
| "BertForMaskedLM", | |
| "BertForSequenceClassification", | |
| "BertModel", | |
| "BertPreTrainedModel", | |
| ] | |