[ICLR 2022] Pretraining Text Encoders with Adversarial Mixture of Training Signal Generators

Related tags

Deep Learningnatural-language-processingdeep-learningtransformersrepresentation-learninglanguage-modelnatural-language-understandingadversarial-machine-learningpretrainingpretrained-language-model
Overview

AMOS

This repository contains the scripts for fine-tuning AMOS pretrained models on GLUE and SQuAD 2.0 benchmarks.

Paper: Pretraining Text Encoders with Adversarial Mixture of Training Signal Generators

Overview

We provide the scripts in two versions, based on two widely-used open-source codebases, the Fairseq Library and the Huggingface Transformers Library. The two code versions are mostly equivalent in functionality, and you are free to use either of them. However, we note that the fairseq version is what we used in our experiments, and it will best reproduce the results in the paper; the huggingface version is implemented later to provide compatibility with the Huggingface Transformers Library, and may yield slightly different results.

Please follow the README files under the two directories for running the code.

GLUE Fine-Tuning Results

The General Language Understanding Evaluation (GLUE) benchmark is a collection of sentence- or sentence-pair language understanding tasks for evaluating and analyzing natural language understanding systems.

GLUE dev set results of AMOS base++ model are as follows (median of 5 different random seeds):

Model MNLI-m/mm QQP QNLI SST-2 CoLA RTE MRPC STS-B AVG
AMOS base++ 90.5/90.4 92.4 94.4 95.5 71.8 86.6 91.7 92.0 89.4

GLUE test set results of AMOS base++ model are as follows (no ensemble, task-specific tricks, etc.):

Model MNLI-m/mm QQP QNLI SST-2 CoLA RTE MRPC STS-B AVG
AMOS base++ 90.4/89.9 90.2 94.6 96.8 69.2 83.6 88.9 91.3 88.1

SQuAD 2.0 Fine-Tuning Results

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.

SQuAD 2.0 dev set results of AMOS base++ and large++ models are as follows (median of 5 different random seeds):

Model EM F1
AMOS base++ 85.0 87.9

Citation

If you find the code and models useful for your research, please cite the following paper:

@inproceedings{meng2022amos,
  title={Pretraining Text Encoders with Adversarial Mixture of Training Signal Generators},
  author={Meng, Yu and Xiong, Chenyan and Bajaj, Payal and Tiwary, Saurabh and Bennett, Paul and Han, Jiawei and Song, Xia},
  booktitle={International Conference on Learning Representations},
  year={2022}
}

Contributing

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.

When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact [email protected] with any additional questions or comments.

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Comments
  • Training loss and acc/auc curve

    Training loss and acc/auc curve

    Hi, I'm using amos now. My amos model (small size, discriminator ) have a low recall (70-80% percision while 40% recall). 60% mlm acc of generator. I would just like to ask if you can post the loss of both base and large models (or even share the loss training curve, acc curve or auc curve ) so that i have a kind of reference point when training own models. This will help me a lot!

    Thank u.

    opened by wwx13 1
  • Bump numpy from 1.21.2 to 1.22.0 in /huggingface

    Bump numpy from 1.21.2 to 1.22.0 in /huggingface

    Bumps numpy from 1.21.2 to 1.22.0.

    Release notes

    Sourced from numpy's releases.

    v1.22.0

    NumPy 1.22.0 Release Notes

    NumPy 1.22.0 is a big release featuring the work of 153 contributors spread over 609 pull requests. There have been many improvements, highlights are:

    • Annotations of the main namespace are essentially complete. Upstream is a moving target, so there will likely be further improvements, but the major work is done. This is probably the most user visible enhancement in this release.
    • A preliminary version of the proposed Array-API is provided. This is a step in creating a standard collection of functions that can be used across application such as CuPy and JAX.
    • NumPy now has a DLPack backend. DLPack provides a common interchange format for array (tensor) data.
    • New methods for quantile, percentile, and related functions. The new methods provide a complete set of the methods commonly found in the literature.
    • A new configurable allocator for use by downstream projects.

    These are in addition to the ongoing work to provide SIMD support for commonly used functions, improvements to F2PY, and better documentation.

    The Python versions supported in this release are 3.8-3.10, Python 3.7 has been dropped. Note that 32 bit wheels are only provided for Python 3.8 and 3.9 on Windows, all other wheels are 64 bits on account of Ubuntu, Fedora, and other Linux distributions dropping 32 bit support. All 64 bit wheels are also linked with 64 bit integer OpenBLAS, which should fix the occasional problems encountered by folks using truly huge arrays.

    Expired deprecations

    Deprecated numeric style dtype strings have been removed

    Using the strings "Bytes0", "Datetime64", "Str0", "Uint32", and "Uint64" as a dtype will now raise a TypeError.

    (gh-19539)

    Expired deprecations for loads, ndfromtxt, and mafromtxt in npyio

    numpy.loads was deprecated in v1.15, with the recommendation that users use pickle.loads instead. ndfromtxt and mafromtxt were both deprecated in v1.17 - users should use numpy.genfromtxt instead with the appropriate value for the usemask parameter.

    (gh-19615)

    ... (truncated)

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