Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Code for ACL 2019 Paper: "COMET: Commonsense Transformers for Automatic Knowledge Graph Construction" https://arxiv.org/abs/1906.05317
| Date | Stars |
|---|---|
| 2026-07-31 | 691 |
| 2026-08-01 | 690 |
| 2026-08-06 | 690 |
Today
— stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
To run a generation experiment (either conceptnet or atomic), follow these instructions: <h1>First Steps</h1> First clone, the repo: ``` git clone https://github.com/atcbosselut/comet-commonsense.git ``` Then run the setup scripts to acquire the pretrained model files from OpenAI, as well as the ATOMIC and ConceptNet datasets ``` bash scripts/setup/get_atomic_data.sh bash scripts/setup/get_conceptnet_data.sh bash scripts/setup/get_model_files.sh ``` Then install dependencies (assuming you already have Python 3.6 and Pytorch >= 1.0: ``` conda install tensorflow pip install ftfy==5.1 conda install -c conda-forge spacy python -m spacy download en pip install tensorboardX pip install tqdm pip install pandas pip install ipython ``` <h1> Making the Data Loaders </h1> Run the following scripts to pre-initialize a data loader for ATOMIC or ConceptNet: ``` python scripts/data/make_atomic_data_loader.py python scripts/data/make_conceptnet_data_loader.py ``` For the ATOMIC KG, if you'd like to make a data loader for only a subset of the relation types, comment out any relations in lines 17-25. For ConceptNet if you'd like to map the relations to natural language analogues, set ```opt.data.rel = "language"``` in line 26. If you want to initialize unpretrained relation tokens, set ```opt.data.rel = "relation"``` <h1> Setting the ATOMIC configuration files </h1> Open ```config/atomic/changes.json``` and set which categories you want to train, as well as any other details you find important. Check ```src/data/config.py``` for a description of different options. Variables you may want to change: batch_size, learning_rate, categories. See ```config/default.json``` and ```config/atomic/default.json``` for default settings of some of these variables. <h1> Setting the ConceptNet configuration files </h1> Open ```config/conceptnet/changes.json``` and set any changes to the degault configuration that you may want to vary in this experiment. Check ```src/data/config.py``` for a description of different options. Variables you may want to change: batch_size, learning_rate, etc. See ```config/default.json``` and ```config/conceptnet/default.json``` for default settings of some of these variables. <h1> Running the ATOMIC experiment </h1> <h3> Training </h3> For whichever experiment # you set in ```config/atomic/changes.json``` (e.g., 0, 1, 2, etc.), run: ``` python src/main.py --experiment_type atomic --experiment_num # ``` <h3> Evaluation </h3> Once you've trained a model, run the evaluation script: ``` python scripts/evaluate/evaluate_atomic_generation_model.py --split $DATASET_SPLIT --model_name /path/to/model/file ``` <h3> Generation </h3> Once you've trained a model, run the generation script for the type of decoding you'd like to do: ``` python scripts/generate/generate_atomic_beam_search.py --beam 10 --split $DATASET_SPLIT --model_name /path/to/model/file python scripts/generate/generate_atomic_greedy.py --split $DATASET_SPLIT --model_name /path/to/model/file python scripts/generate/generate_atomic_topk.py --k 10 --split $DATASET_SPLIT --model_name /path/to/model/file ``` <h1> Running the ConceptNet experiment </h1> <h3> Training </h3> For whichever experiment # you set in ```config/conceptnet/changes.json``` (e.g., 0, 1, 2, etc.), run: ``` python src/main.py --experiment_type conceptnet --experiment_num # ``` Development and Test set tuples are automatically evaluated and generated with greedy decoding during training <h3> Generation </h3> If you want to generate with a larger beam size, run the generation script ``` python scripts/generate/generate_conceptnet_beam_search.py --beam 10 --split $DATASET_SPLIT --model_name /path/to/model/file ``` <h3> Classifying Generated Tuples </h3> To run the classifier from Li et al., 2016 on your generated tuples to evaluate correctness, first download the pretrained model from: ``` wget https://ttic.uchicago.edu/~kgimpel/comsense_resources/ckbc-demo.tar.gz tar -xvzf ckbc-
Excerpt of 5,986 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:2d7ab692573476b5, desc:knowledge graph
