patentdeberta_base_spec_1024_pwi

Model Card for patentdeberta_base_spec_1024_pwi

Model Details

Model Description

More information needed

• Developed by: More information needed
• Shared by [Optional]: tanapatentlm
• Model type: Fill Mask
• Language(s) (NLP): More information needed
• License: More information needed
• Parent Model: DeBERTa
• Resources for more information: More information needed

Uses

Direct Use

This model can be used for the task of Fill Mask.

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:

More information needed

@inproceedings{
he2021deberta,
title={DEBERTA: DECODING-ENHANCED BERT WITH DISENTANGLED ATTENTION},
author={Pengcheng He and Xiaodong Liu and Jianfeng Gao and Weizhu Chen},
booktitle={International Conference on Learning Representations},
year={2021},
url={https://openreview.net/forum?id=XPZIaotutsD}
}

APA:

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Glossary [optional]

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More Information [optional]

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Model Card Authors [optional]

Tanapatentlm 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.

<details> <summary> Click to expand </summary>

from transformers import AutoTokenizer, AutoModelForMaskedLM

tokenizer = AutoTokenizer.from_pretrained("tanapatentlm/patentdeberta_base_spec_1024_pwi")

model = AutoModelForMaskedLM.from_pretrained("tanapatentlm/patentdeberta_base_spec_1024_pwi")

</details>