readme: ---
  tags:
  - question-answering
  - bert
  ---
  
  # Model Card for dynamic_tinybert   
   
  # Model Details
   
  ## Model Description
   
  Dynamic-TinyBERT: Boost TinyBERT’s Inference Efficiency by Dynamic Sequence Length
  
   
  - **Developed by:** Intel
  - **Shared by [Optional]:** Intel
  - **Model type:** Question Answering
  - **Language(s) (NLP):** More information needed
  - **License:** More information needed
  - **Parent Model:** BERT
  - **Resources for more information:** 
     - [Associated Paper](https://neurips2021-nlp.github.io/papers/16/CameraReady/Dynamic_TinyBERT_NLSP2021_camera_ready.pdf)
   	  
  
  
  
  # Uses
   
  
  ## Direct Use
  This model can be used for the task of question answering. 
   
  ## 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
   
  The model authors note in the [associated paper](https://neurips2021-nlp.github.io/papers/16/CameraReady/Dynamic_TinyBERT_NLSP2021_camera_ready.pdf):
  > All our experiments are evaluated on the challenging question-answering benchmark SQuAD1.1 [11].
   
   
  ## Training Procedure
  
   
  ### Preprocessing
  
  The model authors note in the [associated paper](https://neurips2021-nlp.github.io/papers/16/CameraReady/Dynamic_TinyBERT_NLSP2021_camera_ready.pdf):
  > We start with a pre-trained general-TinyBERT student, which was trained to learn the general knowledge of BERT using the general-distillation method presented by TinyBERT. We perform transformer distillation from a fine- tuned BERT teacher to the student, following the same training steps used in the original TinyBERT: (1) **intermediate-layer distillation (ID)** — learning the knowledge residing in the hidden states and attentions matrices, and (2) **prediction-layer distillation (PD)** — fitting the predictions of the teacher. 
   
  
  
   
  ### Speeds, Sizes, Times
   
  The model authors note in the [associated paper](https://neurips2021-nlp.github.io/papers/16/CameraReady/Dynamic_TinyBERT_NLSP2021_camera_ready.pdf):
  >For our Dynamic-TinyBERT model we use the architecture of TinyBERT6L: a small BERT model with 6 layers, a hidden size of 768, a feed forward size of 3072 and 12 heads. 
   
  # Evaluation
   
   
  ## Testing Data, Factors & Metrics
   
  ### Testing Data
   
  More information needed
   
  ### Factors
  More information needed
   
  ### Metrics
   
  More information needed
   
   
  ## Results 
  
  The model authors note in the [associated paper](https://neurips2021-nlp.github.io/papers/16/CameraReady/Dynamic_TinyBERT_NLSP2021_camera_ready.pdf):
   
  | Model            | Max F1 (full model) | Best Speedup within BERT-1% |
  |------------------|---------------------|-----------------------------|
  | Dynamic-TinyBERT | 88.71               | 3.3x                        |
  
  
  
   
  # 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:** Titan GPU
  - **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:**
  
  ```bibtex
  @misc{https://doi.org/10.48550/arxiv.2111.09645,
    doi = {10.48550/ARXIV.2111.09645},
    
    url = {https://arxiv.org/abs/2111.09645},
    
    author = {Guskin, Shira and Wasserblat, Moshe and Ding, Ke and Kim, Gyuwan},
    
    keywords = {Computation and Language (cs.CL), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences},
    
    title = {Dynamic-TinyBERT: Boost TinyBERT's Inference Efficiency by Dynamic Sequence Length},
    
    publisher = {arXiv},
    
    year = {2021},
  ```
  
  
  
  **APA:**
  
  More information needed
    
  # Glossary [optional]
   
  More information needed
  
  # More Information [optional]
  More information needed 
  
  # Model Card Authors [optional]
   
  Intel  in collaboration with Ezi Ozoani and the Hugging Face team
  
  # Model Card Cont