Instructions to use DataMuncher-Labs/SC-600k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DataMuncher-Labs/SC-600k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="DataMuncher-Labs/SC-600k")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("DataMuncher-Labs/SC-600k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "model_type": "small_transformer", | |
| "hidden_size": 64, | |
| "num_attention_heads": 1, | |
| "num_hidden_layers": 3, | |
| "vocab_size": 30522, | |
| "param_estimate": 598178 | |
| } |