Model Card for MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices

Model Details

Model Description

This model is the Multi-Genre Natural Language Inference (MNLI) fine-turned version of the uncased MobileBERT model .

• Developed by: Typeform
• Shared by [Optional]: Typeform
• Model type: Zero-Shot-Classification
• Language(s) (NLP): English
• License: More information needed
• Parent Model: uncased MobileBERT model .
• Resources for more information: More information needed

Uses

Direct Use

This model can be used for the task of zero-shot classification

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

See the multi_nli dataset card for more information.

Training Procedure

Preprocessing

More information needed

Speeds, Sizes, Times

More information needed

Evaluation

Testing Data, Factors & Metrics

Testing Data

See the multi_nli dataset card for more information.

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:

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Glossary [optional]

More information needed

More Information [optional]

More information needed

Model Card Authors [optional]

Typeform 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.

 from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("typeform/mobilebert-uncased-mnli")

model = AutoModelForSequenceClassification.from_pretrained("typeform/mobilebert-uncased-mnli")