entropy
/

roberta_zinc_compression_encoder

@@ -1,199 +1,99 @@
 ---
 library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
 ### Model Description
-<!-- Provide a longer summary of what this model is. -->
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-### Model Sources [optional]
-<!-- Provide the basic links for the model. -->
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-## Uses
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
 ### Direct Use
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-[More Information Needed]
-### Downstream Use [optional]
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-[More Information Needed]
-### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-[More Information Needed]
-## Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-[More Information Needed]
-### Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-## How to Get Started with the Model
-Use the code below to get started with the model.
-[More Information Needed]
-## Training Details
-### Training Data
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-[More Information Needed]
 ### Training Procedure
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-#### Preprocessing [optional]
-[More Information Needed]
 #### Training Hyperparameters
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-#### Speeds, Sizes, Times [optional]
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-[More Information Needed]
-## Evaluation
-<!-- This section describes the evaluation protocols and provides the results. -->
-### Testing Data, Factors & Metrics
-#### Testing Data
-<!-- This should link to a Dataset Card if possible. -->
-[More Information Needed]
-#### Factors
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-[More Information Needed]
-#### Metrics
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-[More Information Needed]
-### Results
-[More Information Needed]
-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
-[More Information Needed]
-## Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-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).
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-## Technical Specifications [optional]
-### Model Architecture and Objective
-[More Information Needed]
-### Compute Infrastructure
-[More Information Needed]
-#### Hardware
-[More Information Needed]
-#### Software
-[More Information Needed]
-## Citation [optional]
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-**BibTeX:**
-[More Information Needed]
-**APA:**
-[More Information Needed]
-## Glossary [optional]
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-[More Information Needed]
-## More Information [optional]
-[More Information Needed]
-## Model Card Authors [optional]
-[More Information Needed]
 ## Model Card Contact
-[More Information Needed]

 ---
 library_name: transformers
+tags:
+- chemistry
+- molecule
+license: mit
 ---
+# Model Card for Roberta Zinc Compression Encoder
 ### Model Description
+`roberta_zinc_compression_encoder` contains several MLP-style compression heads trained to compress
+molecule embeddings from the [roberta_zinc_480m](https://huggingface.co/entropy/roberta_zinc_480m)
+from the native dimension of 768 to smaller dimensions - 512, 256, 128, 64, 32
+- **Developed by:** Karl Heyer
+- **License:** MIT
 ### Direct Use
+Usage examples. Note that input SMILES strings should be canonicalized.
+```python
+from sentence_transformers import models, SentenceTransformer
+from transformers import AutoModel
+transformer = models.Transformer("entropy/roberta_zinc_480m",
+                                 max_seq_length=256,
+                                 model_args={"add_pooling_layer": False})
+pooling = models.Pooling(transformer.get_word_embedding_dimension(),
+                         pooling_mode="mean")
+roberta_zinc = SentenceTransformer(modules=[transformer, pooling])
+compression_encoder = AutoModel.from_pretrained("entropy/roberta_zinc_compression_encoder",
+                                                trust_remote_code=True)
+# smiles should be canonicalized
+smiles = [
+    "Brc1cc2c(NCc3ccccc3)ncnc2s1",
+    "Brc1cc2c(NCc3ccccn3)ncnc2s1",
+    "Brc1cc2c(NCc3cccs3)ncnc2s1",
+    "Brc1cc2c(NCc3ccncc3)ncnc2s1",
+    "Brc1cc2c(Nc3ccccc3)ncnc2s1",
+]
+embeddings = roberta_zinc.encode(smiles, convert_to_tensor=True)
+print(embeddings.shape)
+# torch.Size([6, 768])
+compressed_embeddings = compression_encoder.compress(embeddings.cpu(),
+                                                    compression_sizes=[32, 64, 128, 256, 512])
+for k,v in compressed_embeddings.items():
+    print(k, v.shape)
+# 32 torch.Size([6, 32])
+# 64 torch.Size([6, 64])
+# 128 torch.Size([6, 128])
+# 256 torch.Size([6, 256])
+# 512 torch.Size([6, 512])
+```
 ### Training Procedure
+#### Preprocessing
+A dataset of 30m SMILES strings were assembled from the [ZINC Database](https://zinc.docking.org/)
+and the [Enamine](https://enamine.net/) real space. SMILES were canonicalized and embedded with the
+[roberta_zinc_480m](https://huggingface.co/entropy/roberta_zinc_480m) model.
 #### Training Hyperparameters
+The model was trained for 1 epoch with a learning rate of 1e-3, cosine scheduling, weight decay of 0.01
+and 10% warmup.
+#### Training Loss
+For training, the input batch of embeddings is compressed with all compression sizes via
+the encoder layers, the reconstructed via the decoder layers.
+For the encoder, we compute the pairwise similarities of the compressed embeddings and
+compare to the pairwise similarities of the input embeddings using row-wise pearson correlation.
+For the decoder, we compute the cosine similarity of the reconstructed embeddings to the inputs.
+## Model Card Authors
+Karl Heyer
 ## Model Card Contact
+karl@darmatterai.xyz
+---
+license: mit
+---