Codebase for the Summary Loop paper at ACL2020

Related tags

Deep Learningnewsreinforcement-learningsummarizationunsupervised-learningabstractive-summarizationacl2020fluency-model
Overview

Summary Loop

This repository contains the code for ACL2020 paper: The Summary Loop: Learning to Write Abstractive Summaries Without Examples.

Training Procedure

We provide pre-trained models for each component needed in the Summary Loop Release:

  • keyword_extractor.joblib: An sklearn pipeline that will extract can be used to compute tf-idf scores of words according to the BERT vocabulary, which is used by the Masking Procedure,
  • bert_coverage.bin: A bert-base-uncased finetuned model on the task of Coverage for the news domain,
  • fluency_news_bs32.bin: A GPT2 (base) model finetuned on a large corpus of news articles, used as the Fluency model,
  • gpt2_copier23.bin: A GPT2 (base) model that can be used as an initial point for the Summarizer model.

In the release, we also provide:

  • pretrain_coverage.py script to train a coverage model from scratch,
  • train_generator.py to train a fluency model from scratch (we recommend Fluency model on domain of summaries, such as news, legal, etc.)

Once all the pretraining models are ready, training a summarizer can be done using the train_summary_loop.py:

python train_summary_loop.py --experiment wikinews_test --dataset_file data/wikinews.db

Scorer Models

The Coverage and Fluency model and Guardrails scores can be used separately for analysis, evaluation, etc. They are respectively in model_coverage.py and model_guardrails.py, each model is implemented as a class with a score(document, summary) function. The Fluency model is a Language model, which is also the generator (in model_generator.py). Examples of how to run each model are included in the class files, at the bottom of the files.

Bringing in your own data

Want to test out the Summary Loop on a different language/type of text? A Jupyter Notebook can help you bring your own data into the SQLite format we use in the pre-training scripts. Otherwise you can modify the scripts' data loading (DataLoader) and collate function (collate_fn).

Cite the work

If you make use of the code, models, or algorithm, please cite our paper:

@inproceedings{laban2020summary,
  title={The Summary Loop: Learning to Write Abstractive Summaries Without Examples},
  author={Laban, Philippe and Hsi, Andrew and Canny, John and Hearst, Marti A},
  booktitle={Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics},
  volume={1},
  year={2020}
}

Contributing

If you'd like to contribute, or have questions or suggestions, you can contact us at [email protected]. All contributions welcome! For example, if you have a type of text data on which you want to apply the Summary Loop.

Comments
  • Error Loading Model RuntimeError: Error(s) in loading state_dict for GPT2LMHeadModel:

    Error Loading Model RuntimeError: Error(s) in loading state_dict for GPT2LMHeadModel: 

    Traceback (most recent call last):
  File "train_summary_loop.py", line 59, in <module>
    summarizer = GeneTransformer(max_output_length=args.max_output_length, device=args.device, tokenizer_type='gpt2', starter_model=summarizer_model_start)
  File "/home/tait-dev-0/summary_loop/summary_loop/model_generator.py", line 30, in __init__
    self.reload(starter_model)
  File "/home/tait-dev-0/summary_loop/summary_loop/model_generator.py", line 39, in reload
    print(self.model.load_state_dict(torch.load(from_file)))
  File "/home/tait-dev-0/anaconda2/envs/summary_loop/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1045, in load_state_dict
    self.__class__.__name__, "\n\t".join(error_msgs)))
RuntimeError: Error(s) in loading state_dict for GPT2LMHeadModel:
	Missing key(s) in state_dict: "transformer.h.0.attn.masked_bias", "transformer.h.1.attn.masked_bias", "transformer.h.2.attn.masked_bias", "transformer.h.3.attn.masked_bias", "transformer.h.4.attn.masked_bias", "transformer.h.5.attn.masked_bias", "transformer.h.6.attn.masked_bias", "transformer.h.7.attn.masked_bias", "transformer.h.8.attn.masked_bias", "transformer.h.9.attn.masked_bias", "transformer.h.10.attn.masked_bias", "transformer.h.11.attn.masked_bias".
    opened by raviolli 6
  • Missing models for training

    Missing models for training

    Dear author, I tried to load the fluency_news_model_file models but failed. It seems that the "news_gpt2_bs32.bin" is not provided in the release.

    I tried to replace it with "fluency_news_bs32.bin", but it does not seem to match the GeneTransformer. I.e. when I tried to load the fluency model using modelf=GeneTransformer(max_output_length=args.max_output_length, device=args.device, starter_model=fluency_news_model_file) it shows "IncompatibleKeys(missing_keys=['transformer.h.0.attn.masked_bias', 'transformer.h.1.attn.masked_bias', 'transformer.h.2.attn.masked_bias', 'transformer.h.3.attn.masked_bias', 'transformer.h.4.attn.masked_bias', 'transformer.h.5.attn.masked_bias', 'transformer.h.6.attn.masked_bias', 'transformer.h.7.attn.masked_bias', 'transformer.h.8.attn.masked_bias', 'transformer.h.9.attn.masked_bias', 'transformer.h.10.attn.masked_bias', 'transformer.h.11.attn.masked_bias'], unexpected_keys=[]) "

    Is this fine?

    In addition, when I tried to load the key word coverage model, the keys do not match either I.e. When running modelc = KeywordCoverage(args.device, keyword_model_file=coverage_keyword_model_file, model_file=coverage_model_file)} It shows IncompatibleKeys(missing_keys=['bert.embeddings.position_ids', 'cls.predictions.decoder.bias'], unexpected_keys=[])

    Wondering how I could deal with this situation

    opened by pengshancai 2
  • IndexError when decode with beam_size > 1

    IndexError when decode with beam_size > 1

    Followed the instruction from here and changed the beam_size to more than 1. IndexError occur:

    ~/summary_loop/model_generator.py in decode(self, bodies, max_output_length, max_batch_size, beam_size, return_scores, sample, progress)
    232             with torch.no_grad():
    233                 if beam_size > 1:
--> 234                     batch_outputs = self.decode_beam_batch(batch_bodies, beam_size=beam_size, max_output_length=max_output_length, sample=sample)
    235                 else:
    236                     batch_outputs = self.decode_batch(batch_bodies, max_output_length=max_output_length, sample=sample, return_scores=return_scores)

~/summary_loop/model_generator.py in decode_beam_batch(self, bodies, beam_size, max_output_length, sample)
    200             if build_up is not None:
    201                 build_up = build_up[tracks, :]
--> 202             past = [p[:, tracks, :] for p in past]
    203 
    204             # Update the latest scores, and the current_build

~/summary_loop/model_generator.py in <listcomp>(.0)
    200             if build_up is not None:
    201                 build_up = build_up[tracks, :]
--> 202             past = [p[:, tracks, :] for p in past]
    203 
    204             # Update the latest scores, and the current_build

IndexError: tensors used as indices must be long, byte or bool tensors
    opened by s103321048 2
  • cannot reshape tensor of 0 elements into shape [-1, 0]

    cannot reshape tensor of 0 elements into shape [-1, 0]

    I followed the instruction training model with the provided example wikinews.db: python train_summary_loop.py --experiment wikinews_test --dataset_file data/wikinews.db

    It did start training but later stop due to Runtimeerror:

    Traceback (most recent call last):
  File "train_summary_loop.py", line 138, in <module>
    sampled_summaries, sampled_logprobs, sampled_tokens, input_past, sampled_end_idxs = summarizer.decode_batch(bodies, max_output_length=args.max_output_length, return_logprobs=True, sample=True)
  File "/home/robin/TrySomethingNew/summary_loop/model_generator.py", line 100, in decode_batch
    _, input_past = self.model(input_ids=inputs, past_key_values=None)
  File "/home/robin/virtual-env/summary-loop/lib/python3.6/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
    result = self.forward(*input, **kwargs)
  File "/home/robin/virtual-env/summary-loop/lib/python3.6/site-packages/transformers/modeling_gpt2.py", line 731, in forward
    return_dict=return_dict,
  File "/home/robin/virtual-env/summary-loop/lib/python3.6/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
    result = self.forward(*input, **kwargs)
  File "/home/robin/virtual-env/summary-loop/lib/python3.6/site-packages/transformers/modeling_gpt2.py", line 533, in forward
    input_ids = input_ids.view(-1, input_shape[-1])
RuntimeError: cannot reshape tensor of 0 elements into shape [-1, 0] because the unspecified
dimension size -1 can be any value and is ambiguous
    opened by s103321048 2
  • Code for summary generation from the given model is not provided

    Code for summary generation from the given model is not provided

    You mentioned "Releasing the 11,490 summaries generated by the Summary Loop model (summary_loop_length46.bin) on the CNN/DM test set." and provided the json file "cnndm_test_summary_loop.json". Is there any code to get the json file(summaries) from the given model(.bin). If you have such code, then please share.

    opened by tarunyadav 1
  • Resuming training

    Resuming training

    Is resuming training simply starting from the checkpoint instead of the gpt3 copier bin?

    For example:

    #summarizer_model_start = os.path.join(models_folder, "gpt2_copier23.bin")
summarizer_model_start = os.path.join(models_folder, "summarizer_wikinews_test_0_ckpt.bin")
    opened by RevanthRameshkumar 1
  • Encoding error in bin file

    Encoding error in bin file

    (dlenv) D:\summary loop\summary_loop-0.1>python summary_loop_length10.bin --experiment wikinews_test --dataset_file data/wikinews.db File "summary_loop_length10.bin", line 1 SyntaxError: Non-UTF-8 code starting with '\x80' in file summary_loop_length10.bin on line 1, but no encoding declared; see http://python.org/dev/peps/pep-0263/ for details

    Anyone else get this issue? Currently debugging

    opened by RevanthRameshkumar 1
  • a sample of data in hdf5 format

    a sample of data in hdf5 format

    Hi,

    I'm trying to train the models from scratch since I'd like to use them on a different language. It seems that one needs a dataset in hdf5 format instead of SQL to do that. Can you please release a sample of data in hdf5 format?

    Thanks

    opened by azagsam 1
  • Missing Model to run example

    Missing Model to run example

    I'm trying to run the example:

    python train_summary_loop.py --experiment wikinews_test --dataset_file ../data/wikinews.db --root_folder ../ --device cuda

    but it seems I'm missing the ../models/fluency_news_bs32.bin

    it doesn't seem to be in the list of downloadable models. Mistake??

    opened by raviolli 1
  • Error running training_summary example

    Error running training_summary example

    python train_summary_loop.py --experiment wikinews_test --dataset_file ../data/wikinews.db

    Traceback (most recent call last):
  File "train_summary_loop.py", line 56, in <module>
    bert_tokenizer = utils_tokenizer.BERTCacheTokenizer()
  File "/home/tait-dev-0/summary_loop/summary_loop/utils_tokenizer.py", line 88, in __init__
    self.tokenizer.max_len = 10000
AttributeError: can't set attribute

    transformers 3.0.2 py_0 conda-forge

    I created a separate conda environment. Is this a transformer version issue?

    opened by raviolli 1
  • updated torch.load params

    updated torch.load params

    Updated occurrences of torch.load to include map_location parameter. When attempting to train with --device set to cpu, torch.load may attempt to load a file with GPU tensors, which will lead to loading to GPU by default (see: https://pytorch.org/docs/stable/generated/torch.load.html). If --device is set to cpu, this will error on a cpu-only machine. Otherwise, it will go against desired functionality. This pull request resolves this issue.

    opened by bsh98 0
Releases(0.3)

  • 0.3(Jun 11, 2021)

    Releasing the 11,490 summaries generated by the Summary Loop model (summary_loop_length46.bin) on the CNN/DM test set. Each summary is released attached with the CNN/DM id. The following code snippet can be used to evaluate ROUGE scores:

    from datasets import load_dataset, load_metric
import json
with open("/home/phillab/data/cnndm_test_summary_loop.json", "r") as f:
    summary_loop_gens = json.load(f)
rouge = load_metric("rouge")
dataset_test = load_dataset("cnn_dailymail", "3.0.0")["test"]
id2summary_loop = {d["id"]: d["summary_loop_gen"] for d in summary_loop_gens}
candidates, references = [], []
for d in dataset_test:
    references.append(d["highlights"])
    candidates.append(id2summary_loop[d["id"]])
print(len(references), len(candidates))
print(rouge.compute(predictions=candidates, references=references))

    Notes: (1) this relies on HuggingFace's datasets repository (https://github.com/huggingface/datasets) to load the CNN/DM dataset, and the ROUGE metric. (2) The ROUGE metric implementation used in the above example is not the original, PERL-based implementation of ROUGE used for official numbers in the paper. This serves for demonstration purposes to show how to use the file.

    Source code(tar.gz)
    Source code(zip)
    cnndm_test_summary_loop.json(3.40 MB)

  • 0.2(Sep 8, 2020)

    We release an upgraded set of initial models for the training script that are compatible with transformers==3.1.0 to make it easier to get started. The original release (0.1) used version 2.8.0 of transformers, and there were some breaking changed introduced since, which leads to some model loading failing. The requirements.txt in the latest release has been updated with compatible library versions to simplify installation.

    Initial Models

    These sets of models work using Python 3.6.10, Transformers 3.1.0 and Sklearn 0.22.1:

    • keyword_extractor.joblib: An sklearn pipeline that will extract can be used to compute tf-idf scores of words according to the BERT vocabulary, which is used by the Masking Procedure,
    • bert_coverage.bin: A bert-base-uncased finetuned model on the task of Coverage for the news domain,
    • fluency_news_bs32.bin: A GPT2 (base) model finetuned on a large corpus of news articles, used as the Fluency model,
    • gpt2_copier23.bin: A GPT2 (base) model that can be used as an initial point for the Summarizer model.

    Final Models

    Unfortunately, the three final models (trained summarizers) released in v0.1 do not work anymore in the latest transformers library, and only work in versions 2.8.0 and before. Once we retrain these models, we will reupload them. If this is of interest, feel free to add an issue or contact us.

    Source code(tar.gz)
    Source code(zip)
    bert_coverage.bin(420.06 MB)
    fluency_news_bs32.bin(486.73 MB)
    gpt2_copier23.bin(633.97 MB)
    keyword_extractor.joblib(667.33 KB)

  • v0.1(Jun 25, 2020)

    We release models and data needed to run the Summary Loop and use the models we trained.

    Initial models

    Here are the models needed to run the train_summary_loop.py:

    • keyword_extractor.joblib: An sklearn pipeline that will extract can be used to compute tf-idf scores of words according to the BERT vocabulary, which is used by the Masking Procedure,
    • bert_coverage.bin: A bert-base-uncased finetuned model on the task of Coverage for the news domain,
    • fluency_news_bs32.bin: A GPT2 (base) model finetuned on a large corpus of news articles, used as the Fluency model,
    • gpt2_copier23.bin: A GPT2 (base) model that can be used as an initial point for the Summarizer model.

    Sample dataset

    We release a sample dataset of Wikinews news articles to get researchers started using the Summary Loop: wikinews.db. We cannot release the full dataset we used for copyright reasons. We note that we do not expect this to be enough to train to best performance, and recommend finding larger datasets (such as Newsroom or CNN/DM) for full-fledged training.

    Final models

    We release 3 Summarizer models obtained through the Summary Loop procedure for 3 target lengths: summary_loop_length_12.bin, summary_loop_length_27.bin, summary_loop_length_61.bin

    Source code(tar.gz)
    Source code(zip)
    bert_coverage.bin(420.06 MB)
    fluency_news_bs32.bin(522.73 MB)
    gpt2_copier23.bin(633.97 MB)
    keyword_extractor.joblib(667.33 KB)
    summary_loop_length10.bin(522.73 MB)
    summary_loop_length24.bin(522.73 MB)
    summary_loop_length46.bin(522.73 MB)
    wikinews.db(91.20 MB)
Owner
Canny Lab @ The University of California, Berkeley
Canny Lab @ The University of California, Berkeley
GitHub Repository
GDR-Net: Geometry-Guided Direct Regression Network for Monocular 6D Object Pose Estimation. (CVPR 2021)

GDR-Net This repo provides the PyTorch implementation of the work: Gu Wang, Fabian Manhardt, Federico Tombari, Xiangyang Ji. GDR-Net: Geometry-Guided

169 Jan 07, 2023
YOLOv5 detection interface - PyQt5 implementation

所有代码已上传，直接clone后，运行yolo_win.py即可开启界面。 2021/9/29：加入置信度选择 界面是在ultralytics的yolov5基础上建立的，界面使用pyqt5实现，内容较简单，娱乐而已。 功能： 模型选择 本地文件选择(视频图片均可) 开关摄像头

487 Dec 27, 2022
PyTorch Implementations for DeeplabV3 and PSPNet

Pytorch-segmentation-toolbox DOC Pytorch code for semantic segmentation. This is a minimal code to run PSPnet and Deeplabv3 on Cityscape dataset. Shor

Zilong Huang 746 Dec 15, 2022
Calculates JMA (Japan Meteorological Agency) seismic intensity (shindo) scale from acceleration data recorded in NumPy array

shindo.py Calculates JMA (Japan Meteorological Agency) seismic intensity (shindo) scale from acceleration data stored in NumPy array Introduction Japa

RR_Inyo 3 Sep 23, 2022
A reimplementation of DCGAN in PyTorch

DCGAN in PyTorch A reimplementation of DCGAN in PyTorch. Although there is an abundant source of code and examples found online (as well as an officia

Diego Porres 6 Jan 08, 2022
social humanoid robots with GPGPU and IoT

Social humanoid robots with GPGPU and IoT Social humanoid robots with GPGPU and IoT Paper Authors Mohsen Jafarzadeh, Stephen Brooks, Shimeng Yu, Balak

0 Jan 07, 2022
PyTorch implementation of Advantage Actor Critic (A2C), Proximal Policy Optimization (PPO), Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation (ACKTR) and Generative Adversarial Imitation Learning (GAIL).

pytorch-a2c-ppo-acktr Update (April 12th, 2021) PPO is great, but Soft Actor Critic can be better for many continuous control tasks. Please check out

Ilya Kostrikov 3k Jan 09, 2023
African language Speech Recognition - Speech-to-Text

Swahili-Speech-To-Text Table of Contents Swahili-Speech-To-Text Overview Scenario Approach Project Structure data: models: notebooks: scripts tests: l

2 Jan 05, 2023
A simplistic and efficient pure-python neural network library from Phys Whiz with CPU and GPU support.

A simplistic and efficient pure-python neural network library from Phys Whiz with CPU and GPU support.

Manas Sharma 19 Feb 28, 2022
Semantic Image Synthesis with SPADE

Semantic Image Synthesis with SPADE New implementation available at imaginaire repository We have a reimplementation of the SPADE method that is more

NVIDIA Research Projects 7.3k Jan 07, 2023
Implementation of PersonaGPT Dialog Model

PersonaGPT An open-domain conversational agent with many personalities PersonaGPT is an open-domain conversational agent cpable of decoding personaliz

ILLIDAN Lab 42 Jan 01, 2023
Human POSEitioning System (HPS): 3D Human Pose Estimation and Self-localization in Large Scenes from Body-Mounted Sensors, CVPR 2021

Human POSEitioning System (HPS): 3D Human Pose Estimation and Self-localization in Large Scenes from Body-Mounted Sensors Human POSEitioning System (H

Aymen Mir 66 Dec 21, 2022
Tensorflow implementation of Human-Level Control through Deep Reinforcement Learning

Human-Level Control through Deep Reinforcement Learning Tensorflow implementation of Human-Level Control through Deep Reinforcement Learning. This imp

Devsisters Corp. 2.4k Dec 26, 2022
Application of the L2HMC algorithm to simulations in lattice QCD.

l2hmc-qcd 📊 Slides Recent talk on Training Topological Samplers for Lattice Gauge Theory from the Machine Learning for High Energy Physics, on and of

Sam Foreman 37 Dec 14, 2022
Rewrite ultralytics/yolov5 v6.0 opencv inference code based on numpy, no need to rely on pytorch

Rewrite ultralytics/yolov5 v6.0 opencv inference code based on numpy, no need to rely on pytorch; pre-processing and post-processing using numpy instead of pytroch.

炼丹去了 21 Dec 12, 2022
A curated list of Generative Deep Art projects, tools, artworks, and models

Generative Deep Art A curated list of Generative Deep Art projects, tools, artworks, and models Inbox Get started with making AI art in 2022 – deeplea

Filipe Calegario 251 Jan 03, 2023
Autoencoders pretraining using clustering

Autoencoders pretraining using clustering

IITiS PAN 2 Dec 16, 2021
Official PyTorch implementation of StyleGAN3

Modified StyleGAN3 Repo Changes Made tied to python 3.7 syntax .jpgs instead of .pngs for training sample seeds to recreate the 1024 training grid wit

Derrick Schultz (he/him) 83 Dec 15, 2022
Code repository for paper `Skeleton Merger: an Unsupervised Aligned Keypoint Detector`.

Skeleton Merger Skeleton Merger, an Unsupervised Aligned Keypoint Detector. The paper is available at https://arxiv.org/abs/2103.10814. A map of the r

北海若 48 Nov 14, 2022
Reverse engineering recurrent neural networks with Jacobian switching linear dynamical systems

Reverse engineering recurrent neural networks with Jacobian switching linear dynamical systems This repository is the official implementation of Rever

6 Aug 25, 2022