DeepChem / ChemBERTa-10M-MTR

Model Card for ChemBERTa-10M-MTR

Model Details

Model Description

More information needed

• Developed by: DeepChem

• Shared by [Optional]: DeepChem

• Model type: Token Classification

• Language(s) (NLP): More information needed

• License: More information needed

• Parent Model: RoBERTa

• Resources for more information: More information needed

Uses

Direct Use

More information needed.

Downstream Use [Optional]

More information needed.

Out-of-Scope Use

The model should not be used to intentionally create hostile or alienating environments for people.

Bias, Risks, and Limitations

Significant research has explored bias and fairness issues with language models (see, e.g., Sheng et al. (2021) and Bender et al. (2021) ). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.

Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

Training Details

Training Data

More information needed

Training Procedure

Preprocessing

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Speeds, Sizes, Times

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Evaluation

Testing Data, Factors & Metrics

Testing Data

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Factors

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Metrics

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Results

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Model Examination

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Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019) .

• 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

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Compute Infrastructure

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Hardware

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Software

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Citation

BibTeX:

@book{Ramsundar-et-al-2019,
    title={Deep Learning for the Life Sciences},
    author={Bharath Ramsundar and Peter Eastman and Patrick Walters and Vijay Pande and Karl Leswing and Zhenqin Wu},
    publisher={O'Reilly Media},
    note={\url{https://www.amazon.com/Deep-Learning-Life-Sciences-Microscopy/dp/1492039837}},
    year={2019}
}

APA:

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Glossary [optional]

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More Information [optional]

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Model Card Authors [optional]

DeepChem in collaboration with Ezi Ozoani and the Hugging Face team

Model Card Contact

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How to Get Started with the Model

Use the code below to get started with the model.

from transformers import AutoTokenizer, RobertaForRegression

tokenizer = AutoTokenizer.from_pretrained("DeepChem/ChemBERTa-10M-MTR")

model = RobertaForRegression.from_pretrained("DeepChem/ChemBERTa-10M-MTR")