Feature Extraction
Transformers
Safetensors
roberta_zinc_compression_encoder
chemistry
molecule
custom_code
Instructions to use entropy/roberta_zinc_compression_encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use entropy/roberta_zinc_compression_encoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="entropy/roberta_zinc_compression_encoder", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("entropy/roberta_zinc_compression_encoder", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "RZCompressionModel" | |
| ], | |
| "auto_map": { | |
| "AutoConfig": "configuration_roberta_zinc_compression_encoder.RZCompressionConfig", | |
| "AutoModel": "modeling_roberta_zinc_compression_encoder.RZCompressionModel" | |
| }, | |
| "compression_sizes": [ | |
| 512, | |
| 256, | |
| 128, | |
| 64, | |
| 32 | |
| ], | |
| "decoder_cosine_weight": 1.0, | |
| "decoder_layers": 4, | |
| "dropout": 0.1, | |
| "encoder_layers": 4, | |
| "input_size": 768, | |
| "layer_norm_eps": 1e-12, | |
| "model_type": "roberta_zinc_compression_encoder", | |
| "mse_loss_weight": 0.0, | |
| "pearson_loss_weight": 1.0, | |
| "topk_values": [ | |
| 10, | |
| 100, | |
| 256 | |
| ], | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.51.3" | |
| } | |