

# 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>

{"language": "en", "tags": ["mobilebert"], "datasets": ["multi_nli"], "metrics": ["accuracy"], "pipeline_tag": "zero-shot-classification"}
transformers pytorch safetensors mobilebert text-classification zero-shot-classification en dataset:multi_nli arxiv:1910.09700 autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #safetensors #mobilebert #text-classification #zero-shot-classification #en #dataset-multi_nli #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #has_space #region-us 



# 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.
 
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>


</details>

# 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.
 
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>


</details>
TAGS
#transformers #pytorch #safetensors #mobilebert #text-classification #zero-shot-classification #en #dataset-multi_nli #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #has_space #region-us 
 # 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.
 
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>


</details>
TAGS
#transformers #pytorch #safetensors #mobilebert #text-classification #zero-shot-classification #en #dataset-multi_nli #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #has_space #region-us 
# 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.
 
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>


</details>