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

More information needed

Speeds, Sizes, Times

More information needed

Evaluation

Testing Data, Factors & Metrics

Testing Data

More information needed

Factors

More information needed

Metrics

More information needed

Results

More information needed

Model Examination

More information needed

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

More information needed

Compute Infrastructure

More information needed

Hardware

More information needed

Software

More information needed.

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:

More information needed

Glossary [optional]

More information needed

More Information [optional]

More information needed

Model Card Authors [optional]

DeepChem in collaboration with Ezi Ozoani and the Hugging Face team

Model Card Contact

More information needed

How to Get Started with the Model

Use the code below to get started with the model.

Click to expand

from transformers import AutoTokenizer, RobertaForRegression

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

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