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MOSES-340M

Feature Extraction
English
chemistry
smiles
tokenization
dynamic-tokenization
h-net
hierarchical-networks
molecular-representation
polymer
mamba
transformer
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  1. README.md +120 -0
  2. metadata.json +0 -0
  3. run_purpose.txt +3 -0
  4. test_smiles.txt +5 -0
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README.md ADDED
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+ ---
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+ license: mit
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+ tags:
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+ - chemistry
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+ - smiles
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+ - tokenization
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+ - dynamic-tokenization
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+ - h-net
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+ - hierarchical-networks
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+ - molecular-representation
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+ - polymer
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+ - mamba
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+ - transformer
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+ datasets:
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+ - MOSES
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+ language:
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+ - en
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+ pipeline_tag: feature-extraction
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+ ---
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+
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+ # MOSES-340M
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+
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+ **H-Net model for dynamic SMILES tokenization**
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+
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+ MOSES molecular dataset, 340M bytes (~5 epochs), 10x concatenation, 1-stage architecture
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+
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+ ## Model Details
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+
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+ | Property | Value |
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+ |----------|-------|
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+ | **Architecture** | H-Net (Hierarchical Network) |
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+ | **Parameters** | ~350M |
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+ | **Dataset** | MOSES |
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+ | **Training Bytes** | 340M |
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+ | **Training Epochs** | 5 |
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+ | **Concatenation** | 10x SMILES per example |
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+ | **Architecture Variant** | 1-stage |
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+
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+ ### Architecture Layout
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+
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+ 1-stage: `['m4', ['T22'], 'm4']`
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+
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+ - **Encoder**: 4 Mamba blocks for byte-level encoding
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+ - **Core**: 22 Transformer blocks with boundary prediction
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+ - **Decoder**: 4 Mamba blocks for final decoding
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+
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+ ## Files
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+
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+ - `checkpoints/checkpoint_bytes_best.pt` - Best checkpoint (lowest validation loss)
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+ - `checkpoints/checkpoint_epoch_*.pt` - Epoch checkpoints
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+ - `metadata.json` - Training configuration and history
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+ - `test_smiles.txt` - Test SMILES used during training
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+ - `visualizations/` - Training evolution GIFs and prediction files
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+
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+ ## Usage
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+
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+ ```python
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+ import torch
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+ from pathlib import Path
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+
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+ # Load checkpoint
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+ checkpoint_path = "checkpoints/checkpoint_bytes_best.pt"
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+ checkpoint = torch.load(checkpoint_path, map_location="cpu")
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+
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+ # The checkpoint contains:
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+ # - 'model_state_dict': Model weights
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+ # - 'optimizer_state_dict': Optimizer state
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+ # - 'epoch': Training epoch
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+ # - 'metrics': Training metrics
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+ # - 'cumulative_training_bytes': Total bytes processed
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+
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+ # Load into your H-Net model
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+ # model.load_state_dict(checkpoint['model_state_dict'])
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+ ```
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+
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+ ## Performance
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+
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+ ### Tokenization Metrics (from paper)
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+
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+ | Metric | Value |
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+ |--------|-------|
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+ | Bits-per-byte (BPB) | 0.69 |
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+ | Mean token length | 2.0 |
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+
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+ ### Property Prediction (embeddings)
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+
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+ H-Net embeddings outperform RDKit descriptors on classification tasks:
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+ - BBBP: 0.950 AUC (vs 0.927 for RDKit)
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+ - HIV: 0.788 AUC (vs 0.760 for RDKit)
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+
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+ ## Citation
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+
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+ ```bibtex
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+ @inproceedings{hnet_smiles_2026,
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+ title={Learning Chemical Grammar: Dynamic Tokenization for SMILES with Hierarchical Networks},
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+ author={Anonymous},
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+ booktitle={International Conference on Machine Learning (ICML)},
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+ year={2026}
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+ }
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+ ```
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+
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+ ## Related Models
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+
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+ All models from the paper are available:
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+
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+ **Polymer (PI1M) Models:**
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+ - [PI1M-68M](https://huggingface.co/jordiferrero/PI1M-68M) - 1 epoch, with concatenation
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+ - [PI1M-340M](https://huggingface.co/jordiferrero/PI1M-340M) - 5 epochs, with concatenation
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+ - [PI1M-1B](https://huggingface.co/jordiferrero/PI1M-1B) - 22 epochs, with concatenation (best compression)
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+ - [PI1M-nocat](https://huggingface.co/jordiferrero/PI1M-nocat) - 5 epochs, no concatenation
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+ - [PI1M-2stg](https://huggingface.co/jordiferrero/PI1M-2stg) - 5 epochs, 2-stage architecture
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+
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+ **Molecular (MOSES) Models:**
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+ - [MOSES-340M](https://huggingface.co/jordiferrero/MOSES-340M) - 5 epochs, with concatenation
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+ - [MOSES-nocat](https://huggingface.co/jordiferrero/MOSES-nocat) - 5 epochs, no concatenation
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+ - [MOSES-2stg](https://huggingface.co/jordiferrero/MOSES-2stg) - 5 epochs, 2-stage architecture
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+
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+ ## License
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+
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+ MIT License
metadata.json ADDED
The diff for this file is too large to render. See raw diff
 
run_purpose.txt ADDED
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+ data: molecule
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+ epoch: 5
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+ concatenation: 10
test_smiles.txt ADDED
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+ Cc1cc[nH]c(=NS(=O)(=O)c2ccc(C)s2)c1 COc1c(NC(=O)Nc2ccc(SC)c(Cl)c2)c(C)nn1C COCCNC(=O)C1CCCN(c2ncnc3oc(C)c(C)c23)C1 CCC1CCc2c(sc(=O)n2Cc2nccn2C(F)F)C1 O=C(NC1CC2CCC1C2)c1cccc(CN2CCOCC2)c1 CCC(=O)N(C)C1Oc2ccc(O)cc2C1(C)c1cc(O)ccc1O O=C(Nc1cccc2ccccc12)N1CCCCC1CO CCCC(=O)Nc1cc(OC)c(NC(=O)C(C)C)cc1OC CN(C)C(=O)c1ccc(-c2ccc(OCc3ccccc3)nc2)nc1 Cc1ccc(-c2nccn2CCc2nc3ccccc3[nH]2)o1
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+ O=C(NCc1ccncc1)c1csc(-c2ccccc2)n1 Cn1ccc(-c2cc(C(=O)N3CCCCC3)c3ccccn23)n1 COc1ccc(C)cc1NC(=O)c1nnn(Cc2ccccc2)c1N CCNC(=O)NCc1ccccc1CN1CCCC1=O CN(C(=O)CSc1nnc(C(C)(C)C)n1N)c1ccccc1 Cc1cc(N2CC34C=CC(O3)C(C(=O)NCCC(C)C)C4C2=O)no1 CCN(CC(=O)Nc1cccc(OC)c1)C(=O)c1cccc(C)c1O Cc1cc(C)cc(CCNS(=O)(=O)c2cn(C)c(C)n2)c1 COc1ccccc1NC(=O)Cn1c(C)nc2ccccc2c1=O O=C(NCc1ccccc1)c1cnn2c3c(cnc12)CCCCC3
3
+ CC(=O)c1cc2ccc3c(c2oc1=O)CN(CC(C)C)CO3 Cc1ccsc1C(=O)OCC(=O)Nc1cc(F)ccc1F O=C(Nc1ccc(-c2cn3c(n2)SCC3)cc1)C1CCCC1 CCCOc1cccc2c1CCNC2c1ccncc1 CSc1ccc(-c2nc(Cn3cnc(C)n3)c(C)o2)cc1 Cc1nc(C(C)(C)NC(=O)c2cnn(-c3ccccc3)n2)sc1C CCNc1ncccc1S(=O)(=O)Nc1cccnc1 O=c1ccccn1CCCCNS(=O)(=O)c1cccnc1 CC(=O)Nc1ccc(NC(=O)Cc2ccc3ccccc3c2)cc1 CC(C)N(C1CCC1)S(=O)(=O)N1CCCCC1
4
+ COC1CCN(C(=O)C2CC2(Cl)Cl)CC1 CNC(=O)C1CCCCN1C(=O)Cn1c(=O)n(C)c2ccccc21 Nc1cc(NC2CCC(O)CC2)nc(SCc2ccccc2)n1 COC(=O)Nc1ccc(CNC(=O)N2CC(C)CC(C)C2)cc1 COc1ccc(-c2noc(CCC(=O)NC(C)C)n2)cc1OC CCN(C(=O)C1CCN(c2ccc(Cl)c(Cl)c2)C1=O)C1CC1 O=C(NCc1cc2ccccn2n1)C1(c2ccccc2)CCOCC1 CCCCNC(=O)C1CCN(c2ccccc2)C1=O CCOc1ccc(Nc2ncccc2C(=O)NCCO)cc1 CC(=O)c1c(C)[nH]c(C(=O)Nc2cc(C)cc3cccnc23)c1C
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+ Cc1cc(C)n(CC2CCCN2C(=O)c2cnc(C3CC3)s2)n1 O=C(C1CCCC1)N1CCCC1c1nnc2ccccn12 CCOc1ccc(NC(=O)c2cnc3sc(C)cn3c2=O)cc1 CCOc1nnc(CN2CCOC(Cn3cc(C)cn3)C2)s1 CC(C)CC1COCCN1C(=O)CSc1ccccc1 COC(=O)Cc1ccc2c(Cl)ccc(OC)c2n1 O=C(c1ccc(OCC(F)F)nc1)N1CCC(n2cncn2)CC1 CC(C)(C)OC(=O)NCC(C)(NC(=O)Nc1ccncc1)C1CC1 Cc1ccc(C(C)(C)C)cc1S(=O)(=O)NCc1cccnc1 CCN(C(=O)CCn1cnc2ccccc2c1=O)C1CCCC1
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