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) 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)
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:
Click to expand
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 answerprint("Answer:", answer)
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)
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, and Bender et al., 2021). 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
@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},
