|
--- |
|
tags: |
|
- question-answering |
|
- bert |
|
license: apache-2.0 |
|
datasets: |
|
- squad |
|
language: |
|
- en |
|
model-index: |
|
- name: dynamic-tinybert |
|
results: |
|
- task: |
|
type: question-answering |
|
name: question-answering |
|
metrics: |
|
- type: f1 |
|
value: 88.71 |
|
|
|
--- |
|
|
|
## Model Details: Dynamic-TinyBERT: Boost TinyBERT's Inference Efficiency by Dynamic Sequence Length |
|
|
|
Dynamic-TinyBERT has been fine-tuned for the NLP task of question answering, trained on the SQuAD 1.1 dataset. [Guskin et al. (2021)](https://neurips2021-nlp.github.io/papers/16/CameraReady/Dynamic_TinyBERT_NLSP2021_camera_ready.pdf) note: |
|
|
|
> Dynamic-TinyBERT is a TinyBERT model that utilizes sequence-length reduction and Hyperparameter Optimization for enhanced inference efficiency per any computational budget. Dynamic-TinyBERT is trained only once, performing on-par with BERT and achieving an accuracy-speedup trade-off superior to any other efficient approaches (up to 3.3x with <1% loss-drop). |
|
|
|
|
|
|
|
| Model Detail | Description | |
|
| ----------- | ----------- | |
|
| Model Authors - Company | Intel | |
|
| Model Card Authors | Intel in collaboration with Hugging Face | |
|
| Date | November 22, 2021 | |
|
| Version | 1 | |
|
| Type | NLP - Question Answering | |
|
| Architecture | "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." [Guskin et al. (2021)](https://gyuwankim.github.io/publication/dynamic-tinybert/poster.pdf) | |
|
| Paper or Other Resources | [Paper](https://neurips2021-nlp.github.io/papers/16/CameraReady/Dynamic_TinyBERT_NLSP2021_camera_ready.pdf); [Poster](https://gyuwankim.github.io/publication/dynamic-tinybert/poster.pdf); [GitHub Repo](https://github.com/IntelLabs/Model-Compression-Research-Package) | |
|
| License | Apache 2.0 | |
|
| Questions or Comments | [Community Tab](https://huggingface.co/Intel/dynamic_tinybert/discussions) and [Intel Developers Discord](https://discord.gg/rv2Gp55UJQ)| |
|
|
|
| Intended Use | Description | |
|
| ----------- | ----------- | |
|
| Primary intended uses | You can use the model for the NLP task of question answering: given a corpus of text, you can ask it a question about that text, and it will find the answer in the text. | |
|
| Primary intended users | Anyone doing question answering | |
|
| Out-of-scope uses | The model should not be used to intentionally create hostile or alienating environments for people.| |
|
|
|
### How to use |
|
|
|
Here is how to import this model in Python: |
|
|
|
<details> |
|
<summary> Click to expand </summary> |
|
|
|
```python |
|
import torch |
|
from transformers import AutoTokenizer, AutoModelForQuestionAnswering |
|
|
|
tokenizer = AutoTokenizer.from_pretrained("Intel/dynamic_tinybert") |
|
model = AutoModelForQuestionAnswering.from_pretrained("Intel/dynamic_tinybert") |
|
|
|
context = "remember the number 123456, I'll ask you later." |
|
question = "What is the number I told you?" |
|
|
|
# Tokenize the context and question |
|
tokens = tokenizer.encode_plus(question, context, return_tensors="pt", truncation=True) |
|
|
|
# Get the input IDs and attention mask |
|
input_ids = tokens["input_ids"] |
|
attention_mask = tokens["attention_mask"] |
|
|
|
# Perform question answering |
|
outputs = model(input_ids, attention_mask=attention_mask) |
|
start_scores = outputs.start_logits |
|
end_scores = outputs.end_logits |
|
|
|
# Find the start and end positions of the answer |
|
answer_start = torch.argmax(start_scores) |
|
answer_end = torch.argmax(end_scores) + 1 |
|
answer = tokenizer.convert_tokens_to_string(tokenizer.convert_ids_to_tokens(input_ids[0][answer_start:answer_end])) |
|
|
|
# Print the answer |
|
print("Answer:", answer) |
|
``` |
|
</details> |
|
|
|
|
|
| Factors | Description | |
|
| ----------- | ----------- | |
|
| Groups | Many Wikipedia articles with question and answer labels are contained in the training data | |
|
| Instrumentation | - | |
|
| Environment | Training was completed on a Titan GPU. | |
|
| Card Prompts | Model deployment on alternate hardware and software will change model performance | |
|
|
|
| Metrics | Description | |
|
| ----------- | ----------- | |
|
| Model performance measures | F1 | |
|
| Decision thresholds | - | |
|
| Approaches to uncertainty and variability | - | |
|
|
|
| Training and Evaluation Data | Description | |
|
| ----------- | ----------- | |
|
| Datasets | SQuAD1.1: "Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable." (https://huggingface.co/datasets/squad)| |
|
| Motivation | To build an efficient and accurate model for the question answering task. | |
|
| Preprocessing | "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." ([Guskin et al., 2021](https://neurips2021-nlp.github.io/papers/16/CameraReady/Dynamic_TinyBERT_NLSP2021_camera_ready.pdf))| |
|
|
|
Model Performance Analysis: |
|
|
|
| Model | Max F1 (full model) | Best Speedup within BERT-1% | |
|
|------------------|---------------------|-----------------------------| |
|
| Dynamic-TinyBERT | 88.71 | 3.3x | |
|
|
|
| Ethical Considerations | Description | |
|
| ----------- | ----------- | |
|
| Data | The training data come from Wikipedia articles | |
|
| Human life | The model is not intended to inform decisions central to human life or flourishing. It is an aggregated set of labelled Wikipedia articles. | |
|
| Mitigations | No additional risk mitigation strategies were considered during model development. | |
|
| Risks and harms | 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. Beyond this, the extent of the risks involved by using the model remain unknown.| |
|
| Use cases | - | |
|
|
|
|
|
| Caveats and Recommendations | |
|
| ----------- | |
|
| Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. There are no additional caveats or recommendations for this model. | |
|
|
|
|
|
### BibTeX entry and citation info |
|
```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}, |
|
``` |
