TrainingDynamics

Re-implementation of the paper Dataset Cartography: Mapping and Diagnosing Datasets with Training Dynamics (EMNLP-20). This project is mainly based on AllenAI's Dataset Cartography project, where the model outputs (logits) of each sample is recorded after every training epoch. Based on these records, training dynamics (prediction confidence, variability, etc.) are computed to plot the Data Cartography to visualize the distribution of all training samples. However, the original repo hasn't been maintained for a long time. In this repo, we use the latest version of packages to reimplement the Dataset Cartography, as well as some other extensions based on the training dynamics.

Basic requirements:

• transformers==4.18.0
• torch==1.7.0
• datasets==2.3.2
• accelerate==0.9.0

More requirements see requirements.txt.

Usage:

For example, we want to record the training dynamics of SST2 dataset (a sentiment classification task from GLUE), we do the following steps:

1. Run sh run_glue_and_record_td.sh to obtain the training dynamics.

Specify the TASK_NAME (here we choose sst2), MODEL you want to use and num of epochs to train the classifier.

The following infomation will be recorded during training:

• 'guid': the id of the sample
• 'logits_epoch_{epoch}': output logits vector of the current sample
• 'gold': the true label (index)

After training, we can find the log files in ./dy_log/{TASK_NAME}/{MODEL}/training_dynamics directory like:

dynamics_epoch_0.jsonl
dynamics_epoch_1.jsonl
dynamics_epoch_2.jsonl
...

each file contains records like:

{"guid": 50325, "logits_epoch_0": [2.943110942840576, -2.2836594581604004], "gold": 0, "device": "cuda:0"}
{"guid": 42123, "logits_epoch_0": [-2.7155513763427734, 3.249767541885376], "gold": 1, "device": "cuda:0"}
{"guid": 42936, "logits_epoch_0": [-1.1907235383987427, 2.1173453330993652], "gold": 1, "device": "cuda:0"}
...

2. Run sh plot.sh to plot the data cartography based the recorded training dynamics.

In plot.sh, we can specify the TASK_NAME and MODEL, which are used to determine the path of the training dynamics. First, the log files from each epoch are collected together, several metrics (confidence, variability, correctness, forgetfulness, etc.) are calculated and saved into a single file, named by 'td_metrics.jsonl' (in the save directory ./dy_log/{TASK}/{MODEL}/training_dynamics):

{"guid":50325,"index":0,"threshold_closeness":0.0039580798,"confidence":0.9960261285,"variability":0.0012847629,"correctness":4,"forgetfulness":0}
{"guid":42123,"index":1,"threshold_closeness":0.0012448987,"confidence":0.9987535477,"variability":0.0007707975,"correctness":4,"forgetfulness":0}
{"guid":42936,"index":2,"threshold_closeness":0.0396512556,"confidence":0.958637923,"variability":0.0095242939,"correctness":4,"forgetfulness":0}
...

Then, a data map (dataset cartography) is plotted based on these metrics:

Data Selection

After recording the training dynamics, we can re-train the model by selecting a subset (e.g. use only the ambiguous samples for training). For example, for sst2 task and bert-tiny model, just run:

python data_selection.py --task_name sst2 --model_name bert-tiny --burn_out 4

then you can get a json file at dy_log/sst2/bert-tiny/three_regions_data_indices.json

then, run sh run_glue.sh by adding --with_data_selection and --data_selection_region [region].

More details see comments in run_glue.sh.

Other Extensions:

Apart from the above usage, we can also compare the difference between two models (e.g. a strong model and a weak model) by computing the change of the dynamics. For example, we train a weak model (BERT-tiny) and strong model (RoBERTa-large) on SST2 dataset and plot their difference:

You can find more detailed usage of this repo in our notebook plot_demo.ipynb.

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Have fun and fell free to give your feedback :)

Citation:

@inproceedings{swayamdipta-etal-2020-dataset,
    title = "Dataset Cartography: Mapping and Diagnosing Datasets with Training Dynamics",
    author = "Swayamdipta, Swabha  and
      Schwartz, Roy  and
      Lourie, Nicholas  and
      Wang, Yizhong  and
      Hajishirzi, Hannaneh  and
      Smith, Noah A.  and
      Choi, Yejin",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2020.emnlp-main.746",
    doi = "10.18653/v1/2020.emnlp-main.746",
    pages = "9275--9293",
}