Resources for the "Evaluating the Factual Consistency of Abstractive Text Summarization" paper

Overview

Evaluating the Factual Consistency of Abstractive Text Summarization

Authors: Wojciech Kryściński, Bryan McCann, Caiming Xiong, and Richard Socher

Introduction

Currently used metrics for assessing summarization algorithms do not account for whether summaries are factually consistent with source documents. We propose a weakly-supervised, model-based approach for verifying factual consistency and identifying conflicts between source documents and a generated summary. Training data is generated by applying a series of rule-based transformations to the sentences of source documents. The factual consistency model is then trained jointly for three tasks:

  1. identify whether sentences remain factually consistent after transformation,
  2. extract a span in the source documents to support the consistency prediction,
  3. extract a span in the summary sentence that is inconsistent if one exists. Transferring this model to summaries generated by several state-of-the art models reveals that this highly scalable approach substantially outperforms previous models, including those trained with strong supervision using standard datasets for natural language inference and fact checking. Additionally, human evaluation shows that the auxiliary span extraction tasks provide useful assistance in the process of verifying factual consistency.

Paper link: https://arxiv.org/abs/1910.12840

Table of Contents

  1. Updates
  2. Citation
  3. License
  4. Usage
  5. Get Involved

Updates

1/27/2020

Updated manually annotated data files - fixed filepaths in misaligned examples.

Updated model checkpoint files - recomputed evaluation metrics for fixed examples.

Citation

@article{kryscinskiFactCC2019,
  author    = {Wojciech Kry{\'s}ci{\'n}ski and Bryan McCann and Caiming Xiong and Richard Socher},
  title     = {Evaluating the Factual Consistency of Abstractive Text Summarization},
  journal   = {arXiv preprint arXiv:1910.12840},
  year      = {2019},
}

License

The code is released under the BSD-3 License (see LICENSE.txt for details), but we also ask that users respect the following:

This software should not be used to promote or profit from violence, hate, and division, environmental destruction, abuse of human rights, or the destruction of people's physical and mental health.

Usage

Code repository uses Python 3. Prior to running any scripts please make sure to install required Python packages listed in the requirements.txt file.

Example call: pip3 install -r requirements.txt

Training and Evaluation Datasets

Generated training data can be found here.

Manually annotated validation and test data can be found here.

Both generated and manually annotated datasets require pairing with the original CNN/DailyMail articles.

To recreate the datasets follow the instructions:

  1. Download CNN Stories and Daily Mail Stories from https://cs.nyu.edu/~kcho/DMQA/
  2. Create a cnndm directory and unpack downloaded files into the directory
  3. Download and unpack FactCC data (do not rename directory)
  4. Run the pair_data.py script to pair the data with original articles

Example call:

python3 data_pairing/pair_data.py <dir-with-factcc-data> <dir-with-stories>

Generating Data

Synthetic training data can be generated using code available in the data_generation directory.

The data generation script expects the source documents input as one jsonl file, where each source document is embedded in a separate json object. The json object is required to contain an id key which stores an example id (uniqness is not required), and a text field that stores the text of the source document.

Certain transformations rely on NER tagging, thus for best results use source documents with original (proper) casing.

The following claim augmentations (transformations) are available:

  • backtranslation - Paraphrasing claim via backtranslation (requires Google Translate API key; costs apply)
  • pronoun_swap - Swapping a random pronoun in the claim
  • date_swap - Swapping random date/time found in the claim with one present in the source article
  • number_swap - Swapping random number found in the claim with one present in the source article
  • entity_swap - Swapping random entity name found in the claim with one present in the source article
  • negation - Negating meaning of the claim
  • noise - Injecting noise into the claim sentence

For a detailed description of available transformations please refer to Section 3.1 in the paper.

To authenticate with the Google Cloud API follow these instructions.

Example call:

python3 data_generation/create_data.py <source-data-file> [--augmentations list-of-augmentations]

Model Code

FactCC and FactCCX models can be trained or initialized from a checkpoint using code available in the modeling directory.

Quickstart training, fine-tuning, and evaluation scripts are shared in the scripts directory. Before use make sure to update *_PATH variables with appropriate, absolute paths.

To customize training or evaluation settings please refer to the flags in the run.py file.

To utilize Weights&Biases dashboards login to the service using the following command: wandb login <API KEY>.

Trained FactCC model checkpoint can be found here.

Trained FactCCX model checkpoint can be found here.

IMPORTANT: Due to data pre-processing, the first run of training or evaluation code on a large dataset can take up to a few hours before the actual procedure starts.

Running on other data

To run pretrained FactCC or FactCCX models on your data follow the instruction:

  1. Download pre-trained model checkpoint, linked above
  2. Prepare your data in jsonl format. Each example should be a separate json object with id, text, claim keys representing example id, source document, and claim sentence accordingly. Name file as data-dev.jsonl
  3. Update corresponding *-eval.sh script

Get Involved

Please create a GitHub issue if you have any questions, suggestions, requests or bug-reports. We welcome PRs!

Comments
  • how to do inference when data not posesses 'label' ?

    how to do inference when data not posesses 'label' ?

    I want to predict the fact consistency of text and claim, what is the command to inference label of sample ? Can the model print the level of consistency when getting one pair of text and claim ?

    opened by zide05 3
  • Bump protobuf from 3.11.2 to 3.15.0

    Bump protobuf from 3.11.2 to 3.15.0

    Bumps protobuf from 3.11.2 to 3.15.0.

    Release notes

    Sourced from protobuf's releases.

    Protocol Buffers v3.15.0

    Protocol Compiler

    • Optional fields for proto3 are enabled by default, and no longer require the --experimental_allow_proto3_optional flag.

    C++

    • MessageDifferencer: fixed bug when using custom ignore with multiple unknown fields
    • Use init_seg in MSVC to push initialization to an earlier phase.
    • Runtime no longer triggers -Wsign-compare warnings.
    • Fixed -Wtautological-constant-out-of-range-compare warning.
    • DynamicCastToGenerated works for nullptr input for even if RTTI is disabled
    • Arena is refactored and optimized.
    • Clarified/specified that the exact value of Arena::SpaceAllocated() is an implementation detail users must not rely on. It should not be used in unit tests.
    • Change the signature of Any::PackFrom() to return false on error.
    • Add fast reflection getter API for strings.
    • Constant initialize the global message instances
    • Avoid potential for missed wakeup in UnknownFieldSet
    • Now Proto3 Oneof fields have "has" methods for checking their presence in C++.
    • Bugfix for NVCC
    • Return early in _InternalSerialize for empty maps.
    • Adding functionality for outputting map key values in proto path logging output (does not affect comparison logic) and stop printing 'value' in the path. The modified print functionality is in the MessageDifferencer::StreamReporter.
    • Fixed protocolbuffers/protobuf#8129
    • Ensure that null char symbol, package and file names do not result in a crash.
    • Constant initialize the global message instances
    • Pretty print 'max' instead of numeric values in reserved ranges.
    • Removed remaining instances of std::is_pod, which is deprecated in C++20.
    • Changes to reduce code size for unknown field handling by making uncommon cases out of line.
    • Fix std::is_pod deprecated in C++20 (#7180)
    • Fix some -Wunused-parameter warnings (#8053)
    • Fix detecting file as directory on zOS issue #8051 (#8052)
    • Don't include sys/param.h for _BYTE_ORDER (#8106)
    • remove CMAKE_THREAD_LIBS_INIT from pkgconfig CFLAGS (#8154)
    • Fix TextFormatMapTest.DynamicMessage issue#5136 (#8159)
    • Fix for compiler warning issue#8145 (#8160)
    • fix: support deprecated enums for GCC < 6 (#8164)
    • Fix some warning when compiling with Visual Studio 2019 on x64 target (#8125)

    Python

    • Provided an override for the reverse() method that will reverse the internal collection directly instead of using the other methods of the BaseContainer.
    • MessageFactory.CreateProtoype can be overridden to customize class creation.

    ... (truncated)

    Commits
    • ae50d9b Update protobuf version
    • 8260126 Update protobuf version
    • c741c46 Resovled issue in the .pb.cc files
    • eef2764 Resolved an issue where NO_DESTROY and CONSTINIT were in incorrect order
    • 0040102 Updated collect_all_artifacts.sh for Ubuntu Xenial
    • 26cb6a7 Delete root-owned files in Kokoro builds
    • 1e924ef Update port_def.inc
    • 9a80cf1 Update coded_stream.h
    • a97c4f4 Merge pull request #8276 from haberman/php-warning
    • 44cd75d Merge pull request #8282 from haberman/changelog
    • Additional commits viewable in compare view

    Dependabot compatibility score

    Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting @dependabot rebase.


    Dependabot commands and options

    You can trigger Dependabot actions by commenting on this PR:

    • @dependabot rebase will rebase this PR
    • @dependabot recreate will recreate this PR, overwriting any edits that have been made to it
    • @dependabot merge will merge this PR after your CI passes on it
    • @dependabot squash and merge will squash and merge this PR after your CI passes on it
    • @dependabot cancel merge will cancel a previously requested merge and block automerging
    • @dependabot reopen will reopen this PR if it is closed
    • @dependabot close will close this PR and stop Dependabot recreating it. You can achieve the same result by closing it manually
    • @dependabot ignore this major version will close this PR and stop Dependabot creating any more for this major version (unless you reopen the PR or upgrade to it yourself)
    • @dependabot ignore this minor version will close this PR and stop Dependabot creating any more for this minor version (unless you reopen the PR or upgrade to it yourself)
    • @dependabot ignore this dependency will close this PR and stop Dependabot creating any more for this dependency (unless you reopen the PR or upgrade to it yourself)
    • @dependabot use these labels will set the current labels as the default for future PRs for this repo and language
    • @dependabot use these reviewers will set the current reviewers as the default for future PRs for this repo and language
    • @dependabot use these assignees will set the current assignees as the default for future PRs for this repo and language
    • @dependabot use this milestone will set the current milestone as the default for future PRs for this repo and language

    You can disable automated security fix PRs for this repo from the Security Alerts page.

    dependencies 
    opened by dependabot[bot] 1
  • Usage for consistency prediction

    Usage for consistency prediction

    Hi,

    I'm looking to use factCC to try and evaluate summaries produced by a summarization system. I've seen the scripts for evaluation (factCC-eval.sh) and it reports eval_results (bacc, f1 and loss).

    But is there a way to get the output of the algorithm, so for each of the inputs a label "consistent" or "inconsistent" and the span that either supports the claim (in case of a correct claim) or the span that contradicts the claim (in the case that the claim was incorrect).

    I could not find this in the Usage part of the readme. I'd be happy to hear how to do this.

    Regards,

    Michel Botros

    opened by michelbotros 1
  • When running pretrained model FastCCX on annotated_data, it has debug

    When running pretrained model FastCCX on annotated_data, it has debug

    when i use the pretrained model FastCCX provided by the link to evalute the annotated data, i have met the debug like this:

    03/24/2021 20:54:10 - INFO - main - Loading model from checkpoint. =========== <class 'model.BertPointer'> ============== 03/24/2021 20:54:15 - INFO - pytorch_transformers.modeling_utils - loading weights file https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased-pytorch_model.bin from cache at /home/zggao/.cache/torch/pytorch_transformers/aa1ef1aede4482d0dbcd4d52baad8ae300e60902e88fcb0bebdec09afd232066.36ca03ab34a1a5d5fa7bc3d03d55c4fa650fed07220e2eeebc06ce58d0e9a157 Traceback (most recent call last): File "/home/zggao/document-summarization/abs-sum-fact-problem/factCC-master/modeling//run.py", line 520, in main() File "/home/zggao/document-summarization/abs-sum-fact-problem/factCC-master/modeling//run.py", line 460, in main model = model_class.from_pretrained(args.model_name_or_path, from_tf=bool('.ckpt' in args.model_name_or_path), config=config) File "/home/zggao/anaconda3/envs/testtransformers/lib/python3.6/site-packages/pytorch_transformers/modeling_utils.py", line 536, in from_pretrained model = cls(config, *model_args, **model_kwargs) File "/home/zggao/document-summarization/abs-sum-fact-problem/factCC-master/modeling/model.py", line 52, in init self.apply(self.init_weights) File "/home/zggao/anaconda3/envs/testtransformers/lib/python3.6/site-packages/torch/nn/modules/module.py", line 440, in apply module.apply(fn) File "/home/zggao/anaconda3/envs/testtransformers/lib/python3.6/site-packages/torch/nn/modules/module.py", line 440, in apply module.apply(fn) File "/home/zggao/anaconda3/envs/testtransformers/lib/python3.6/site-packages/torch/nn/modules/module.py", line 440, in apply module.apply(fn) File "/home/zggao/anaconda3/envs/testtransformers/lib/python3.6/site-packages/torch/nn/modules/module.py", line 441, in apply fn(self) TypeError: init_weights() takes 1 positional argument but 2 were given

    how can i fix this?

    opened by gaozhiguang 1
  • Modeling directory?

    Modeling directory?

    In the *-eval.sh scripts, what exactly does Modeling Directory refer to?

    export CODE_PATH= # absolute path to modeling directory export DATA_PATH= # absolute path to data directory export CKPT_PATH= # absolute path to model checkpoint

    opened by penatbater 1
  • may be a bug in data_generation/create_data.py ?

    may be a bug in data_generation/create_data.py ?

    in line 95:data_numswp = apply_transformation(data_positive, dateswap_op) may should be data_dateswp = apply_transformation(data_positive, dateswap_op)?

    opened by zide05 1
  • Bump protobuf from 3.11.2 to 3.18.3

    Bump protobuf from 3.11.2 to 3.18.3

    Bumps protobuf from 3.11.2 to 3.18.3.

    Release notes

    Sourced from protobuf's releases.

    Protocol Buffers v3.18.3

    C++

    Protocol Buffers v3.16.1

    Java

    • Improve performance characteristics of UnknownFieldSet parsing (#9371)

    Protocol Buffers v3.18.2

    Java

    • Improve performance characteristics of UnknownFieldSet parsing (#9371)

    Protocol Buffers v3.18.1

    Python

    • Update setup.py to reflect that we now require at least Python 3.5 (#8989)
    • Performance fix for DynamicMessage: force GetRaw() to be inlined (#9023)

    Ruby

    • Update ruby_generator.cc to allow proto2 imports in proto3 (#9003)

    Protocol Buffers v3.18.0

    C++

    • Fix warnings raised by clang 11 (#8664)
    • Make StringPiece constructible from std::string_view (#8707)
    • Add missing capability attributes for LLVM 12 (#8714)
    • Stop using std::iterator (deprecated in C++17). (#8741)
    • Move field_access_listener from libprotobuf-lite to libprotobuf (#8775)
    • Fix #7047 Safely handle setlocale (#8735)
    • Remove deprecated version of SetTotalBytesLimit() (#8794)
    • Support arena allocation of google::protobuf::AnyMetadata (#8758)
    • Fix undefined symbol error around SharedCtor() (#8827)
    • Fix default value of enum(int) in json_util with proto2 (#8835)
    • Better Smaller ByteSizeLong
    • Introduce event filters for inject_field_listener_events
    • Reduce memory usage of DescriptorPool
    • For lazy fields copy serialized form when allowed.
    • Re-introduce the InlinedStringField class
    • v2 access listener
    • Reduce padding in the proto's ExtensionRegistry map.
    • GetExtension performance optimizations
    • Make tracker a static variable rather than call static functions
    • Support extensions in field access listener
    • Annotate MergeFrom for field access listener
    • Fix incomplete types for field access listener
    • Add map_entry/new_map_entry to SpecificField in MessageDifferencer. They record the map items which are different in MessageDifferencer's reporter.
    • Reduce binary size due to fieldless proto messages
    • TextFormat: ParseInfoTree supports getting field end location in addition to start.

    ... (truncated)

    Commits

    Dependabot compatibility score

    Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting @dependabot rebase.


    Dependabot commands and options

    You can trigger Dependabot actions by commenting on this PR:

    • @dependabot rebase will rebase this PR
    • @dependabot recreate will recreate this PR, overwriting any edits that have been made to it
    • @dependabot merge will merge this PR after your CI passes on it
    • @dependabot squash and merge will squash and merge this PR after your CI passes on it
    • @dependabot cancel merge will cancel a previously requested merge and block automerging
    • @dependabot reopen will reopen this PR if it is closed
    • @dependabot close will close this PR and stop Dependabot recreating it. You can achieve the same result by closing it manually
    • @dependabot ignore this major version will close this PR and stop Dependabot creating any more for this major version (unless you reopen the PR or upgrade to it yourself)
    • @dependabot ignore this minor version will close this PR and stop Dependabot creating any more for this minor version (unless you reopen the PR or upgrade to it yourself)
    • @dependabot ignore this dependency will close this PR and stop Dependabot creating any more for this dependency (unless you reopen the PR or upgrade to it yourself)
    • @dependabot use these labels will set the current labels as the default for future PRs for this repo and language
    • @dependabot use these reviewers will set the current reviewers as the default for future PRs for this repo and language
    • @dependabot use these assignees will set the current assignees as the default for future PRs for this repo and language
    • @dependabot use this milestone will set the current milestone as the default for future PRs for this repo and language

    You can disable automated security fix PRs for this repo from the Security Alerts page.

    dependencies 
    opened by dependabot[bot] 0
  • Could you please provide an example for input

    Could you please provide an example for input

    Dear author, I am trying to use your model directly on a downstream task. Would you please provide me the format of the input. E.g. source document target document to check factual consistency

    Thanks!

    opened by pengshancai 0
  • Bump numpy from 1.17.4 to 1.22.0

    Bump numpy from 1.17.4 to 1.22.0

    Bumps numpy from 1.17.4 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)

    Commits

    Dependabot compatibility score

    Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting @dependabot rebase.


    Dependabot commands and options

    You can trigger Dependabot actions by commenting on this PR:

    • @dependabot rebase will rebase this PR
    • @dependabot recreate will recreate this PR, overwriting any edits that have been made to it
    • @dependabot merge will merge this PR after your CI passes on it
    • @dependabot squash and merge will squash and merge this PR after your CI passes on it
    • @dependabot cancel merge will cancel a previously requested merge and block automerging
    • @dependabot reopen will reopen this PR if it is closed
    • @dependabot close will close this PR and stop Dependabot recreating it. You can achieve the same result by closing it manually
    • @dependabot ignore this major version will close this PR and stop Dependabot creating any more for this major version (unless you reopen the PR or upgrade to it yourself)
    • @dependabot ignore this minor version will close this PR and stop Dependabot creating any more for this minor version (unless you reopen the PR or upgrade to it yourself)
    • @dependabot ignore this dependency will close this PR and stop Dependabot creating any more for this dependency (unless you reopen the PR or upgrade to it yourself)
    • @dependabot use these labels will set the current labels as the default for future PRs for this repo and language
    • @dependabot use these reviewers will set the current reviewers as the default for future PRs for this repo and language
    • @dependabot use these assignees will set the current assignees as the default for future PRs for this repo and language
    • @dependabot use this milestone will set the current milestone as the default for future PRs for this repo and language

    You can disable automated security fix PRs for this repo from the Security Alerts page.

    dependencies 
    opened by dependabot[bot] 0
  • How to evaluate my summary

    How to evaluate my summary

    If I want to evaluate the summary generated by my model about cnn dailymail dataset, can I use the factcc model in the README.md directly. Should I set the label of my summary ‘CORRECT’?

    opened by XuelinLuu 1
Owner
Salesforce
A variety of vendor agnostic projects which power Salesforce
Salesforce
Related resources for our EMNLP 2021 paper

Plan-then-Generate: Controlled Data-to-Text Generation via Planning Authors: Yixuan Su, David Vandyke, Sihui Wang, Yimai Fang, and Nigel Collier Code

Yixuan Su 61 Jan 3, 2023
Code and data for ACL2021 paper Cross-Lingual Abstractive Summarization with Limited Parallel Resources.

Multi-Task Framework for Cross-Lingual Abstractive Summarization (MCLAS) The code for ACL2021 paper Cross-Lingual Abstractive Summarization with Limit

Yu Bai 43 Nov 7, 2022
All the essential resources and template code needed to understand and practice data structures and algorithms in python with few small projects to demonstrate their practical application.

Data Structures and Algorithms Python INDEX 1. Resources - Books Data Structures - Reema Thareja competitiveCoding Big-O Cheat Sheet DAA Syllabus Inte

Shushrut Kumar 129 Dec 15, 2022
A curated list of neural network pruning resources.

A curated list of neural network pruning and related resources. Inspired by awesome-deep-vision, awesome-adversarial-machine-learning, awesome-deep-learning-papers and Awesome-NAS.

Yang He 1.7k Jan 9, 2023
A collection of resources on GAN Inversion.

This repo is a collection of resources on GAN inversion, as a supplement for our survey

null 857 Dec 29, 2022
A curated list of programmatic weak supervision papers and resources

A curated list of programmatic weak supervision papers and resources

Jieyu Zhang 118 Jan 2, 2023
A curated list of awesome resources related to Semantic Search🔎 and Semantic Similarity tasks.

A curated list of awesome resources related to Semantic Search?? and Semantic Similarity tasks.

null 224 Jan 4, 2023
A list of awesome PyTorch scholarship articles, guides, blogs, courses and other resources.

Awesome PyTorch Scholarship Resources A collection of awesome PyTorch and Python learning resources. Contributions are always welcome! Course Informat

Arnas Gečas 302 Dec 3, 2022
Code for ACL 21: Generating Query Focused Summaries from Query-Free Resources

marge This repository releases the code for Generating Query Focused Summaries from Query-Free Resources. Please cite the following paper [bib] if you

Yumo Xu 28 Nov 10, 2022
Resources for the Ki testnet challenge

Ki Testnet Challenge This repository hosts ki-testnet-challenge. A set of scripts and resources to be used for the Ki Testnet Challenge What is the te

Ki Foundation 23 Aug 8, 2022
A curated list of resources for Image and Video Deblurring

A curated list of resources for Image and Video Deblurring

Subeesh Vasu 1.7k Jan 1, 2023
Convolutional 2D Knowledge Graph Embeddings resources

ConvE Convolutional 2D Knowledge Graph Embeddings resources. Paper: Convolutional 2D Knowledge Graph Embeddings Used in the paper, but do not use thes

Tim Dettmers 586 Dec 24, 2022
A curated (most recent) list of resources for Learning with Noisy Labels

A curated (most recent) list of resources for Learning with Noisy Labels

Jiaheng Wei 321 Jan 9, 2023
A curated list of neural rendering resources.

Awesome-of-Neural-Rendering A curated list of neural rendering and related resources. Please feel free to pull requests or open an issue to add papers

Zhiwei ZHANG 43 Dec 9, 2022
Minecraft agent to farm resources using reinforcement learning

BarnyardBot CS 175 group project using Malmo download BarnyardBot.py into the python examples directory and run 'python BarnyardBot.py' in the console

null 0 Jul 26, 2022
Arxiv harvester - Poor man's simple harvester for arXiv resources

Poor man's simple harvester for arXiv resources This modest Python script takes

Patrice Lopez 5 Oct 18, 2022
GenshinMapAutoMarkTools - Tools To add/delete/refresh resources mark in Genshin Impact Map

使用说明 适配 windows7以上 64位 原神1920x1080窗口(其他分辨率后续适配) 待更新渊下宫 English version is to be

Zero_Circle 209 Dec 28, 2022
The LaTeX and Python code for generating the paper, experiments' results and visualizations reported in each paper is available (whenever possible) in the paper's directory

This repository contains the software implementation of most algorithms used or developed in my research. The LaTeX and Python code for generating the

João Fonseca 3 Jan 3, 2023
Inference code for "StylePeople: A Generative Model of Fullbody Human Avatars" paper. This code is for the part of the paper describing video-based avatars.

NeuralTextures This is repository with inference code for paper "StylePeople: A Generative Model of Fullbody Human Avatars" (CVPR21). This code is for

Visual Understanding Lab @ Samsung AI Center Moscow 18 Oct 6, 2022