This section is taken verbatim from the kaggle competition page
Twitter has become an important communication channel in times of emergency. The ubiquitousness of smartphones enables people to announce an emergency they’re observing in real-time. Because of this, more agencies are interested in programatically monitoring Twitter (i.e. disaster relief organizations and news agencies).
But, it’s not always clear whether a person’s words are actually announcing a disaster. Take this example:
The author explicitly uses the word “ABLAZE” but means it metaphorically. This is clear to a human right away, especially with the visual aid. But it’s less clear to a machine. In this competition, you’re challenged to build a machine learning model that predicts which Tweets are about real disasters and which one’s aren’t. You’ll have access to a dataset of 10,000 tweets that were hand classified. If this is your first time working on an NLP problem, we've created a quick tutorial to get you up and running. Disclaimer: The dataset for this competition contains text that may be considered profane, vulgar, or offensive.
This dataset was created by the company figure-eight and originally shared on their ‘Data For Everyone’ website here.
Tweet source: https://twitter.com/AnyOtherAnnaK/status/629195955506708480
Hierarchy
.
|-- [ 4096] data
| |-- [ 4096] external
| | |-- [ 4096] appen
| | | |-- [ 715427] disaster_response_messages_test.csv
| | | |-- [ 5746561] disaster_response_messages_training.csv
| | | `-- [ 739819] disaster_response_messages_validation.csv
| | |-- [ 4096] figureeight
| | | |-- [ 181] README.md
| | | `-- [ 2208398] socialmedia-disaster-tweets-DFE.csv
| | |-- [ 4096] kaggle
| | | |-- [ 68603] publicleaderboard.csv
| | | |-- [ 22746] sample_submission.csv
| | | |-- [ 420783] test.csv
| | | `-- [ 987712] train.csv
| | |-- [ 4096] sentiment140
| | | |-- [ 74326] testdata.manual.2009.06.14.csv
| | | `-- [ 85088272] training.1600000.processed.noemoticon.zip
| | |-- [ 4096] slang
| | | `-- [ 280] acronyms.json
| | `-- [ 4096] wikipedia
| | `-- [ 3473] emoticons.json
| |-- [ 4096] features
| | |-- [ 4096] meta_embeddings
| | |-- [ 365552] test_6_topics_15_iterations.npy
| | |-- [ 6682752] test_ae_embeddings.npy
| | |-- [ 13365376] test_bert_large_cased_whole_embeddings.npy
| | |-- [ 10024064] test_bert_sst2_embeddings.npy
| | |-- [ 10024064] test_bertweet_embeddings.npy
| | |-- [ 1670784] test_nnlm_en_128_norm_embeddings.npy
| | |-- [ 6682752] test_use4_embeddings.npy
| | |-- [ 365552] train_6_topics_15_iterations.npy
| | |-- [ 365552] train_6_topics_3_iterations.npy
| | |-- [ 15591552] train_ae_embeddings.npy
| | |-- [ 31182976] train_bert_large_cased_whole_embeddings.npy
| | |-- [ 23387264] train_bert_sst2_embeddings.npy
| | |-- [ 23387264] train_bertweet_embeddings.npy
| | |-- [ 3897984] train_nnlm_en_128_norm_embeddings.npy
| | |-- [ 15591552] train_use4_embeddings.npy
| | `-- [ 4096] tweet_sentiment
| | |-- [ 13180] test_sent_lr_nought_1.npy
| | |-- [ 13180] test_sent_lr_nought_3_400_round.npy
| | |-- [ 13180] test_sent_lr_nought_3.npy
| | |-- [ 30580] train_sent_lr_nought_1.npy
| | |-- [ 30580] train_sent_lr_nought_3_400_round.npy
| | `-- [ 30580] train_sent_lr_nought_3.npy
| `-- [ 4096] processed
| |-- [ 442585] bigrams_with_frequency.graphml
| |-- [ 306981] bigrams_with_frequency_largest_component.graphml
| |-- [ 265055] cooccurence.gephi
| |-- [ 2353376] cooccurrence.gexf
| |-- [ 1222571] cooccurrence.json
| |-- [ 30730] duplicates.gephi
| |-- [ 48744] duplicates.graphml
| |-- [ 4096] kaggle
| | `-- [ 1024668] train.csv
| |-- [ 198800] network.pdf
| |-- [ 1686642] processed.graphml
| |-- [ 1691020] processed_normalized.graphml
| `-- [ 374524] subgraph_stats.csv
|-- [ 627] docker-compose.yml
|-- [ 4096] dockerfiles
| |-- [ 4096] gpu
| | `-- [ 534] Dockerfile
| `-- [ 4096] vanilla
| `-- [ 1344] Dockerfile
|-- [ 4096] docs
| |-- [ 489] commands.rst
| |-- [ 7820] conf.py
| |-- [ 256] getting-started.rst
| |-- [ 443] index.rst
| |-- [ 5114] make.bat
| `-- [ 5600] Makefile
|-- [ 11357] LICENSE
|-- [ 951] Makefile
|-- [ 4096] models
| |-- [ 22746] bertweet_finetuned.csv
| |-- [ 22746] bertweet_finetuned_v2.csv
| |-- [ 4096] bertweet_finetuning
| | `-- [ 4096] bertweet_kaggle
| | |-- [ 775] config.json
| | `-- [ 539876480] tf_model.h5
| |-- [ 2900673] model_2021-01-13_200919_Pipeline_1x10cv_0.75_bertweet.pck
| |-- [ 2898625] model_2021-01-13_201242_Pipeline_1x10cv_0.77_use4.pck
| |-- [ 2895551] model_2021-01-13_201437_Pipeline_1x10cv_0.75_nnlm_en_128_norm.pck
| |-- [ 2902721] model_2021-01-13_201659_Pipeline_1x10cv_0.72_bert_large_cased_whole.pck
| |-- [ 22746] submission_2021-01-13_200919_Pipeline_1x10cv_0.75_bertweet.csv
| |-- [ 22746] submission_2021-01-13_201242_Pipeline_1x10cv_0.77_use4.csv
| |-- [ 22746] submission_2021-01-13_201437_Pipeline_1x10cv_0.75_nnlm_en_128_norm.csv
| |-- [ 22746] submission_2021-01-13_201659_Pipeline_1x10cv_0.72_bert_large_cased_whole.csv
| |-- [ 22746] submission_2021-01-18_104600_Pipeline_1x10cv_0.78_bertweet.csv
| |-- [ 22746] submission_2021-01-18_105116_Pipeline_1x10cv_0.78_bertweet.csv
| |-- [ 22746] submission_2021-01-18_110410_Pipeline_1x10cv_0.78_bertweet.csv
| |-- [ 22746] submission_2021-01-18_110619_Pipeline_1x10cv_0.78_use4.csv
| |-- [ 22746] submission_2021-01-18_110839_Pipeline_1x10cv_0.78_use4.csv
| |-- [ 22746] submission_2021-01-18_110850_Pipeline_1x10cv_0.78_use4.csv
| |-- [ 22746] submission_2021-01-20_135736_Pipeline_1x10cv_0.76_bert_sst2.csv
| |-- [ 22746] submission_2021-01-20_144757_Pipeline_1x10cv_0.74_bertweet.csv
| |-- [ 22746] submission_2021-01-21_202752_Pipeline_1x10cv_0.77_ae.csv
| |-- [ 22746] submission_2021-01-21_204226_Pipeline_1x10cv_0.76_ae.csv
| |-- [ 22746] submission_2021-01-21_210502_Pipeline_1x10cv_0.76_ae.csv
| `-- [ 4096] xgboost
| |-- [ 609581] bst_lr_nought_1.dump
| |-- [ 582574] bst_lr_nought_1.save
| |-- [ 937710] bst_lr_nought_3_400_round.dump
| |-- [ 923222] bst_lr_nought_3_400_round.save
| |-- [ 535196] bst_lr_nought_3.dump
| `-- [ 520798] bst_lr_nought_3.save
|-- [ 4096] notebooks
| |-- [ 5031035] co_occurrence_analysis.ipynb
| |-- [ 17536] duplicates.ipynb
| |-- [ 43886] embeddings_with_linear_svc_eval_pipeline.ipynb
| |-- [ 16133088] exploratory_data_analysis.ipynb
| |-- [ 15852] feature_creation_with_large_language_models.ipynb
| |-- [ 50916] fine_tuning_transformers.ipynb
| |-- [ 226962] initial_data_analysis.ipynb
| |-- [ 700731] initial_data_exploration.ipynb
| |-- [ 3828887] lightgbm.ipynb
| |-- [ 62326] meta_embeddings.ipynb
| |-- [ 547534] meta_feature_exploration.ipynb
| |-- [ 56666] model_pipeline_CountVectorizer.ipynb
| |-- [ 363613] model_pipeline_tfidfVectorizer.ipynb
| |-- [ 7000] pipeline_spacy_VectorTransformer.ipynb
| |-- [ 15645857] presentation.ipynb
| |-- [ 3911] template_model_pipeline.ipynb
| |-- [ 9530] template_model_tutorial.ipynb
| |-- [ 12594651] topic_modeling.ipynb
| |-- [ 63956] tweet_sentiment.ipynb
| `-- [ 4096] wandb
| |-- [ 52] debug-internal.log -> run-20210111_171912-1phci8oe/logs/debug-internal.log
| |-- [ 43] debug.log -> run-20210111_171912-1phci8oe/logs/debug.log
| |-- [ 28] latest-run -> run-20210111_171912-1phci8oe
| `-- [ 4096] run-20210111_171912-1phci8oe
| |-- [ 4096] files
| | |-- [ 176] config.yaml
| | |-- [ 119781] output.log
| | |-- [ 2623] requirements.txt
| | |-- [ 765] wandb-metadata.json
| | `-- [ 156] wandb-summary.json
| |-- [ 4096] logs
| | |-- [ 14152320] debug-internal.log
| | `-- [ 6527] debug.log
| `-- [ 8212534] run-1phci8oe.wandb
|-- [ 6700] README.md
|-- [ 4096] references
| `-- [ 0] literature.bib
|-- [ 4096] reports
| |-- [ 4096] data
| | |-- [ 3312] meta_features_individual.pck
| | `-- [ 915251] meta_features_union.pck
| |-- [ 4096] figures
| | |-- [ 148066] ablaze.png
| | |-- [ 159152] ModalNet-21.png
| | |-- [ 155087] PLMfamily.jpg
| | |-- [ 188129] transformer.png
| | `-- [ 361452] twitter.png
| `-- [ 4096] gephi_reports
| |-- [ 4096] degree
| | |-- [ 12439] degree-distribution.png
| | `-- [ 154] report.html
| |-- [ 4096] diameter
| | |-- [ 10828] Betweenness Centrality Distribution.png
| | |-- [ 12389] Closeness Centrality Distribution.png
| | |-- [ 9938] Eccentricity Distribution.png
| | |-- [ 12727] Harmonic Closeness Centrality Distribution.png
| | `-- [ 662] report.html
| |-- [ 4096] exported
| | `-- [ 2014391] no_labels.png
| |-- [ 4096] HITS
| | |-- [ 10870] authorities.png
| | |-- [ 10327] hubs.png
| | `-- [ 366] report.html
| |-- [ 4096] modularity_10
| | |-- [ 15064] communities-size-distribution.png
| | `-- [ 715] report.html
| |-- [ 4096] modularity_5
| | |-- [ 15921] communities-size-distribution.png
| | `-- [ 715] report.html
| |-- [ 4096] Project1
| | |-- [ 2555] about.html
| | |-- [ 1634] estadisticas.json
| | |-- [ 4096] img
| | | |-- [ 2595] download.png
| | | |-- [ 12799] glyphicons-halflings.png
| | | |-- [ 4176] loading.gif
| | | |-- [ 21384] logo_final.png
| | | |-- [ 327] opendata.png
| | | |-- [ 22475] upm.png
| | | `-- [ 142574] utpl.jpg
| | |-- [ 5037] index.html
| | |-- [ 5187] info.html
| | |-- [ 4096] js
| | | |-- [ 28538] bootstrap.min.js
| | | |-- [ 93636] jquery-1.8.3.min.js
| | | |-- [ 4341] jquery.cookie.js
| | | |-- [ 110593] jquery-ui-1.10.3.custom.min.js
| | | |-- [ 12669] loxawebsite-0.9.1.js
| | | |-- [ 30907] sigma.min.js
| | | `-- [ 6173] sigma.parseGexf.js
| | |-- [ 4096] styles
| | | |-- [ 121689] bootstrap.css
| | | |-- [ 4096] images
| | | | |-- [ 1738] animated-overlay.gif
| | | | |-- [ 418] ui-bg_diagonals-thick_18_b81900_40x40.png
| | | | |-- [ 312] ui-bg_diagonals-thick_20_666666_40x40.png
| | | | |-- [ 205] ui-bg_flat_10_000000_40x100.png
| | | | |-- [ 262] ui-bg_glass_100_f6f6f6_1x400.png
| | | | |-- [ 348] ui-bg_glass_100_fdf5ce_1x400.png
| | | | |-- [ 207] ui-bg_glass_65_ffffff_1x400.png
| | | | |-- [ 5815] ui-bg_gloss-wave_35_f6a828_500x100.png
| | | | |-- [ 278] ui-bg_highlight-soft_100_eeeeee_1x100.png
| | | | |-- [ 328] ui-bg_highlight-soft_75_ffe45c_1x100.png
| | | | |-- [ 6922] ui-icons_222222_256x240.png
| | | | |-- [ 4549] ui-icons_228ef1_256x240.png
| | | | |-- [ 4549] ui-icons_ef8c08_256x240.png
| | | | |-- [ 4549] ui-icons_ffd27a_256x240.png
| | | | `-- [ 6299] ui-icons_ffffff_256x240.png
| | | `-- [ 18120] jquery-ui-1.10.3.custom.min.css
| | `-- [ 4096] Workspace1
| | |-- [ 9012123] Workspace1.csv
| | |-- [ 2353376] Workspace1.gexf
| | `-- [ 202070] Workspace1.pdf
| |-- [ 4096] screenshots
| | |-- [ 1567191] network.png
| | `-- [ 66383] screenshot_142429.png
| `-- [ 4096] weighted_degree
| |-- [ 174] report.html
| `-- [ 15232] w-degree-distribution.png
`-- [ 4096] src
|-- [ 8099] evaluation.py
|-- [ 4096] features
| |-- [ 24766] meta_features_spacy.py
| `-- [ 4096] __pycache__
| `-- [ 33319] meta_features_spacy.cpython-38.pyc
|-- [ 0] __init__.py
`-- [ 4096] __pycache__
|-- [ 4452] evaluation.cpython-36.pyc
`-- [ 4478] evaluation.cpython-38.pyc
50 directories, 187 files
Milestones
-
MS: Agree on core technologies & frameworks
- Description:
- Programming: Python3, scikit-learn, sphinx
- Infrastructure: github, docker, notebooks
- orga: discord, BBB
- Tasks:
- Supervisor Kick-Off: everyone
- Discord server setup: everyone
- Repo setup: Chris
- Project plan: Julian + Karl
- Deliverables: Project Plan, repository
- Due: 23.11.2020
- Description:
-
MS: Implement evaluation pipeline:
- Description:
- Shared evaluation pipeline: (1) read data -> (2) feed to model -> (3) generate score, plots + submission file
- Idea: Once pipeline stands, everyone plays with models in step (2)
- Tasks:
- Implement data assembly
- Implement K-Fold Cross-Validation
- Generate result graphs and scores
- Generate submission file
- Deliverables: Shared Evaluation Pipeline
- Description:
-
MS: First Model Iteration:
- Description:
- Baseline model: Integrate simple model from tutorial (https://www.kaggle.com/philculliton/nlp-getting-started-tutorial)
- Everyone plays with models in step (2)
- Tasks:
- Implement model from tutorial
- Implement own features & models
- Deliverables: Model dumps and their evaluation results
- Description:
-
MS: Second Model Iteration:
- Description:
- Sync: Share insights & features from first iteration in group and with supervisor
- Everyone attempts to improve their models from insights & features
- Tasks:
- Sync Meeting (with supervisor?): everyone (pending)
- Improve own features & models: everyone (pending)
- Deliverables: Model dumps and their evaluation results
- Description:
-
MS: Final Presentation
- Tasks:
- Intro slides
- Leaderboard stats slide
- Our result slides
- Deliverables: Presentation Slides
- Due: ? (end of january)
- Tasks:
-
MS: Final Report
- Tasks:
- LaTeX template ? (pending)
- Abstract: ? (pending)
- Intro: ? (pending)
- Related Work: ? (pending)
- Methods: ? (pending)
- Result: ? (pending)
- Discussion: ? (pending)
- Deliverables: Report Document
- Due: ? (end of february)
- Tasks:
foo@bar:/disaster-tweets$ make docker-build-imagefoo@bar:/disaster-tweets$ make docker-run-jupyterfoo@bar:/disaster-tweets$ make docker-build-image-gpufoo@bar:/disaster-tweets$ make docker-run-jupyter-gpufoo@bar:/disaster-tweets$ make docker-run-rapidsTo fire up the environment using docker-compose, run:
foo@bar:/disaster-tweets$ docker-compose upProject based on the cookiecutter data science project template. #cookiecutterdatascience