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README.md
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tags:
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- chemistry
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- smiles
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---
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# Model Card for Model hogru/MolReactGen-GuacaMol-Molecules
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The model can be used in a Hugging Face text generation pipeline. For the intended use case a wrapper around the raw text generation pipeline is needed. This is the [`generate.py` from the repository](https://github.com/hogru/MolReactGen/blob/main/src/molreactgen/generate.py).
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The model has a default `GenerationConfig()` (`generation_config.json`) which can be overwritten. Depending on the number of molecules to be generated (`num_return_sequences` in the `JSON` file) this might take a while. The generation code above shows a progress bar during generation.
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## Bias, Risks, and Limitations
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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The default Hugging Face `Trainer()` has been used, with an `EarlyStoppingCallback()`.
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The training data was pre-processed with a `PreTrainedTokenizerFast()` trained on the training data with a character level pre-tokenizer and Unigram as the sub-word tokenization algorithm with a vocabulary size of 88. Other tokenizers can be configured.
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- **Batch size:** 64
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- **Gradient accumulation steps:** 4
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Please see the slides / the poster mentioned above.
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<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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tags:
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- chemistry
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- smiles
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widget:
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- text: "^"
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example_title: "Sample molecule | SMILES"
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---
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# Model Card for Model hogru/MolReactGen-GuacaMol-Molecules
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The model can be used in a Hugging Face text generation pipeline. For the intended use case a wrapper around the raw text generation pipeline is needed. This is the [`generate.py` from the repository](https://github.com/hogru/MolReactGen/blob/main/src/molreactgen/generate.py).
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The model has a default `GenerationConfig()` (`generation_config.json`) which can be overwritten. Depending on the number of molecules to be generated (`num_return_sequences` in the `JSON` file) this might take a while. The generation code above shows a progress bar during generation.
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## Bias, Risks, and Limitations
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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The default Hugging Face `Trainer()` has been used, with an `EarlyStoppingCallback()`.
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### Preprocessing
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The training data was pre-processed with a `PreTrainedTokenizerFast()` trained on the training data with a character level pre-tokenizer and Unigram as the sub-word tokenization algorithm with a vocabulary size of 88. Other tokenizers can be configured.
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### Training Hyperparameters
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- **Batch size:** 64
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- **Gradient accumulation steps:** 4
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Please see the slides / the poster mentioned above.
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### Metrics
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<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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