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library_name: transformers
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# Model Card for
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<!-- Provide a quick summary of what the model is/does. -->
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## Model Details
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### Model Description
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- **Developed by:** [More Information Needed]
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- **Funded by [optional]:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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- **Model type:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
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- **License:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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### Model Sources [optional]
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<!-- Provide the basic links for the model. -->
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- **Repository:** [More Information Needed]
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- **Demo [optional]:** [More Information Needed]
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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### Direct Use
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[More Information Needed]
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### Downstream Use [optional]
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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[More Information Needed]
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### Out-of-Scope Use
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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[More Information Needed]
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## Bias, Risks, and Limitations
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[More Information Needed]
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### Training Procedure
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#### Preprocessing [optional]
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[More Information Needed]
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#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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[More Information Needed]
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## Evaluation
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<!-- This section describes the evaluation protocols and provides the results. -->
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### Testing Data, Factors & Metrics
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#### Testing Data
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<!-- This should link to a Dataset Card if possible. -->
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[More Information Needed]
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#### Factors
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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[More Information Needed]
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####
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## Model Examination [optional]
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<!-- Relevant interpretability work for the model goes here -->
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[More Information Needed]
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## Environmental Impact
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- **Hardware Type:** [More Information Needed]
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- **Hours used:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
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- **Compute Region:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
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## Technical Specifications [optional]
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### Model Architecture and Objective
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[More Information Needed]
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### Compute Infrastructure
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#### Hardware
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#### Software
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## Citation [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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**BibTeX:**
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[More Information Needed]
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**APA:**
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## Glossary [optional]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed]
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## More Information [optional]
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## Model Card Authors [optional]
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## Model Card Contact
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---
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library_name: transformers
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tags:
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- chemistry
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- molecule
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license: mit
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# Model Card for Roberta Zinc Compression Encoder
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### Model Description
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`roberta_zinc_compression_encoder` contains several MLP-style compression heads trained to compress
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molecule embeddings from the [roberta_zinc_480m](https://huggingface.co/entropy/roberta_zinc_480m)
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from the native dimension of 768 to smaller dimensions - 512, 256, 128, 64, 32
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- **Developed by:** Karl Heyer
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- **License:** MIT
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### Direct Use
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Usage examples. Note that input SMILES strings should be canonicalized.
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```python
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from sentence_transformers import models, SentenceTransformer
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from transformers import AutoModel
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transformer = models.Transformer("entropy/roberta_zinc_480m",
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max_seq_length=256,
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model_args={"add_pooling_layer": False})
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pooling = models.Pooling(transformer.get_word_embedding_dimension(),
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pooling_mode="mean")
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roberta_zinc = SentenceTransformer(modules=[transformer, pooling])
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compression_encoder = AutoModel.from_pretrained("entropy/roberta_zinc_compression_encoder",
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trust_remote_code=True)
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# smiles should be canonicalized
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smiles = [
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"Brc1cc2c(NCc3ccccc3)ncnc2s1",
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"Brc1cc2c(NCc3ccccn3)ncnc2s1",
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"Brc1cc2c(NCc3cccs3)ncnc2s1",
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"Brc1cc2c(NCc3ccncc3)ncnc2s1",
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"Brc1cc2c(Nc3ccccc3)ncnc2s1",
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]
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embeddings = roberta_zinc.encode(smiles, convert_to_tensor=True)
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print(embeddings.shape)
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# torch.Size([6, 768])
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compressed_embeddings = compression_encoder.compress(embeddings.cpu(),
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compression_sizes=[32, 64, 128, 256, 512])
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for k,v in compressed_embeddings.items():
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print(k, v.shape)
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# 32 torch.Size([6, 32])
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# 64 torch.Size([6, 64])
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# 128 torch.Size([6, 128])
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# 256 torch.Size([6, 256])
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# 512 torch.Size([6, 512])
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```
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### Training Procedure
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#### Preprocessing
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A dataset of 30m SMILES strings were assembled from the [ZINC Database](https://zinc.docking.org/)
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and the [Enamine](https://enamine.net/) real space. SMILES were canonicalized and embedded with the
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[roberta_zinc_480m](https://huggingface.co/entropy/roberta_zinc_480m) model.
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#### Training Hyperparameters
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The model was trained for 1 epoch with a learning rate of 1e-3, cosine scheduling, weight decay of 0.01
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and 10% warmup.
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#### Training Loss
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For training, the input batch of embeddings is compressed with all compression sizes via
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the encoder layers, the reconstructed via the decoder layers.
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For the encoder, we compute the pairwise similarities of the compressed embeddings and
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compare to the pairwise similarities of the input embeddings using row-wise pearson correlation.
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For the decoder, we compute the cosine similarity of the reconstructed embeddings to the inputs.
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## Model Card Authors
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Karl Heyer
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## Model Card Contact
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---
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license: mit
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---
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