model documentation

README.md

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+---
+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 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, AutoModelForQuestionAnswering
+
+tokenizer = AutoTokenizer.from_pretrained("Intel/dynamic_tinybert")
+
+model = AutoModelForQuestionAnswering.from_pretrained("Intel/dynamic_tinybert")
+ ```
+</details>
+