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模型:
Intel/dynamic_tinybert
中文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) |
| Paper or Other Resources | Paper ; Poster ; GitHub Repo |
| License | Apache 2.0 |
| Questions or Comments | Community Tab and Intel Developers Discord |
| 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:
Click to expandfrom transformers import AutoTokenizer, AutoModelForQuestionAnswering
tokenizer = AutoTokenizer.from_pretrained("Intel/dynamic_tinybert")
model = AutoModelForQuestionAnswering.from_pretrained("Intel/dynamic_tinybert")
| 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},