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
File size: 680 Bytes
bbc7ddf | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 | {
"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"
}
|