---
language: en
pipeline_tag: zero-shot-classification
tags:
- mobilebert
datasets:
- multi_nli

metrics:
- accuracy
---


# 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](https://huggingface.co/google/mobilebert-uncased).
   
- **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](https://huggingface.co/google/mobilebert-uncased).
- **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)](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.



## 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](https://huggingface.co/datasets/multi_nli) 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](https://huggingface.co/datasets/multi_nli) 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](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
 
- **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:**
 
More information needed 
 
 
 
# 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.
 
<details>
<summary> Click to expand </summary>

```python
 from transformers import AutoTokenizer, AutoModelForSequenceClassification

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

model = AutoModelForSequenceClassification.from_pretrained("typeform/mobilebert-uncased-mnli")
 ```
</details>