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  - mobilebert
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  datasets:
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  - multi_nli
 
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  metrics:
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  - accuracy
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- # MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices
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- This model is the Multi-Genre Natural Language Inference (MNLI) fine-turned version of the [uncased MobileBERT model](https://huggingface.co/google/mobilebert-uncased).
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  - mobilebert
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  datasets:
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  - multi_nli
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  metrics:
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  - accuracy
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+ # Model Card for MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices
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+ # Model Details
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+ ## Model Description
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+ This model is the Multi-Genre Natural Language Inference (MNLI) fine-turned version of the [uncased MobileBERT model](https://huggingface.co/google/mobilebert-uncased).
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+ - **Developed by:** Typeform
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+ - **Shared by [Optional]:** Typeform
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+ - **Model type:** Zero-Shot-Classification
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+ - **Language(s) (NLP):** English
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+ - **License:** More information needed
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+ - **Parent Model:** [uncased MobileBERT model](https://huggingface.co/google/mobilebert-uncased).
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+ - **Resources for more information:** More information needed
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+ # Uses
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+ ## Direct Use
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+ This model can be used for the task of zero-shot classification
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+ ## Downstream Use [Optional]
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+ More information needed.
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+ ## Out-of-Scope Use
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+ The model should not be used to intentionally create hostile or alienating environments for people.
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+ # Bias, Risks, and Limitations
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+ Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.
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+ ## Recommendations
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+ Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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+ # Training Details
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+ ## Training Data
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+ See [the multi_nli dataset card](https://huggingface.co/datasets/multi_nli) for more information.
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+ ## Training Procedure
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+ ### Preprocessing
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+ More information needed
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+ ### Speeds, Sizes, Times
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+ More information needed
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+ # Evaluation
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+ ## Testing Data, Factors & Metrics
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+ ### Testing Data
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+ See [the multi_nli dataset card](https://huggingface.co/datasets/multi_nli) for more information.
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+ ### Factors
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+ More information needed
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+ ### Metrics
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+ More information needed
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+ ## Results
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+ More information needed
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+ # Model Examination
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+ More information needed
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+ # Environmental Impact
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+ Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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+ - **Hardware Type:** More information needed
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+ - **Hours used:** More information needed
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+ - **Cloud Provider:** More information needed
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+ - **Compute Region:** More information needed
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+ - **Carbon Emitted:** More information needed
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+ # Technical Specifications [optional]
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+ ## Model Architecture and Objective
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+ More information needed
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+ ## Compute Infrastructure
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+ More information needed
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+ ### Hardware
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+ More information needed
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+ ### Software
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+ More information needed.
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+ # Citation
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+ **BibTeX:**
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+ More information needed
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+ # Glossary [optional]
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+ More information needed
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+ # More Information [optional]
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+ More information needed
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+ # Model Card Authors [optional]
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+ Typeform in collaboration with Ezi Ozoani and the Hugging Face team
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+ # Model Card Contact
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+ More information needed
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+ # How to Get Started with the Model
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+ Use the code below to get started with the model.
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+ <details>
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+ <summary> Click to expand </summary>
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+ ```python
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+ from transformers import AutoTokenizer, AutoModelForSequenceClassification
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+ tokenizer = AutoTokenizer.from_pretrained("typeform/mobilebert-uncased-mnli")
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+ model = AutoModelForSequenceClassification.from_pretrained("typeform/mobilebert-uncased-mnli")
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+ ```
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+ </details>