model documentation (#1)

- model documentation (a653578ae8913b6593b9eaedd0d7603e812d5ca1)


Co-authored-by: Nazneen Rajani <nazneen@users.noreply.huggingface.co>

README.md

@@ -5,10 +5,171 @@ tags:
 - mobilebert
 datasets:
 - multi_nli
+
 metrics:
 - accuracy
 ---
 
-# MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices
 
-This model is the Multi-Genre Natural Language Inference (MNLI) fine-turned version of the [uncased MobileBERT model](https://huggingface.co/google/mobilebert-uncased).
+# 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>