model documentation

README.md

@@ -1,51 +1,169 @@
-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. 
+---
+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:**
+ ```
+@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
-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
-}
-```
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+ 
+tokenizer = AutoTokenizer.from_pretrained("cambridgeltl/sst_mobilebert-uncased")
+ 
+model = AutoModelForSequenceClassification.from_pretrained("cambridgeltl/sst_mobilebert-uncased")
+ 
+```
+</details>