Update README.md

README.md

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----
-tags:
-- mobilebert
----
-# Model Card for sst_mobilebert-uncased
- 
-# Model Details
- 
-## Model Description
- 
- 
-- **Developed by:** Zhiqing Sun1∗ , Hongkun Yu2 , Xiaodan Song2 , Renjie Liu2 , Yiming Yang1 , Denny Zhou
-- **Shared by [Optional]:** [Vasily Shamporov](https://huggingface.co/vshampor)
-- **Model type:** Text Classification
-- **Language(s) (NLP):** More information needed
-- **License:** More information needed
-- **Related Models:** MobileBERT
-  - **Parent Model:** BERT
-- **Resources for more information:** 
- 	- [Associated Paper](https://arxiv.org/pdf/2004.02984.pdf)
- 
-# Uses
- 
- 
-## Direct Use
- 
-This model can be used for the task of SequenceClassification
- 
-## 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.
-MobileBERT is a model with absolute position embeddings so it’s usually advised to pad the inputs on the right rather than the left. See [MobileBERT model documentation](https://huggingface.co/docs/transformers/main/en/model_doc/mobilebert#transformers.MobileBertForSequenceClassification) for more information.
- 
- 
-# Training Details
- 
-## Training Data
- 
-More information needed
- 
-## Training Procedure
- 
- 
-### Preprocessing
- 
-More information needed
- 
-### Speeds, Sizes, Times
- 
-More information needed
- 
-# Evaluation
- 
- 
-## Testing Data, Factors & Metrics
- 
-### Testing Data
- 
-More information needed
- 
-### Factors
- 
- 
-### 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:**
- ```
+This model provides a MobileBERT [(Sun et al., 2020)](https://arxiv.org/abs/2004.02984) fine-tuned on the SST data with three sentiments (0 -- negative, 1 -- neutral, and 2 -- positive). 
+
+## Example Usage
+
+Below, we provide illustrations on how to use this model to make sentiment predictions. 
+
+
+```python
+import torch
+from transformers import AutoTokenizer, AutoConfig, MobileBertForSequenceClassification
+# load model
+model_name = r'cambridgeltl/sst_mobilebert-uncased'
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+config = AutoConfig.from_pretrained(model_name)
+model = MobileBertForSequenceClassification.from_pretrained(model_name, config=config)
+model.eval()
+'''
+    labels: 
+        0 -- negative
+        1 -- neutral
+        2 -- positive
+'''
+
+# prepare exemplar sentences
+batch_sentences = [
+    "in his first stab at the form , jacquot takes a slightly anarchic approach that works only sporadically .",
+    "a valueless kiddie paean to pro basketball underwritten by the nba .",
+    "a very well-made , funny and entertaining picture .",
+]
+
+# prepare input
+inputs = tokenizer(batch_sentences, max_length=256, truncation=True, padding=True, return_tensors='pt')
+input_ids, attention_mask = inputs.input_ids, inputs.attention_mask
+
+# make predictions
+outputs = model(input_ids=input_ids, attention_mask=attention_mask)
+predictions = torch.argmax(outputs.logits, dim = -1)
+print (predictions)
+# tensor([1, 0, 2])
+``` 
+
+## Citation:
+If you find this model useful, please kindly cite our model as
+
+```bibtex
 @misc{susstmobilebert,
   author = {Su, Yixuan},
   title = {A MobileBERT Fine-tuned on SST},
   howpublished = {\url{https://huggingface.co/cambridgeltl/sst_mobilebert-uncased}},
-  year = {2022}
+  year = 2022
 }
-
-@misc{https://doi.org/10.48550/arxiv.2004.02984,
-  doi = {10.48550/ARXIV.2004.02984},
-  url = {https://arxiv.org/abs/2004.02984},
-  author = {Sun, Zhiqing and Yu, Hongkun and Song, Xiaodan and Liu, Renjie and Yang, Yiming and Zhou, Denny},
-  keywords = {Computation and Language (cs.CL), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences},
-  title = {MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices},
-  publisher = {arXiv},
-  year = {2020}}
-```
- 
- 
-# Glossary [optional]
-More information needed
- 
-# More Information [optional]
- 
-More information needed
- 
-# Model Card Authors [optional]
- 
- 
-Language Technology Lab @University of Cambridge 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("cambridgeltl/sst_mobilebert-uncased")
- 
-model = AutoModelForSequenceClassification.from_pretrained("cambridgeltl/sst_mobilebert-uncased")
- 
-```
-</details>
+```