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Correct *configs.py locations in README #34

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Correct *configs.py locations in README #34

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8 changes: 4 additions & 4 deletions README.md
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Expand Up @@ -138,7 +138,7 @@ We created `scripts/generate.sh` to generate programs on the APPS benchmark. You
| `temp` | temperature for sampling generation | 0.6 |
| `output_path` | Path to save generated programs | outputs/codes/ |

Other parameters are defined in the file `utils/generate_configs.py`.
Other parameters are defined in the file `configs/generate_configs.py`.

Running the generation script will output programs, each of which is saved into a `json` file, including data fields `code` (list of output programs) and `prompt` (constructed input sequence to the LM model).

Expand Down Expand Up @@ -192,7 +192,7 @@ We created `scripts/train_critic.sh` and `scripts/train_critic_deepspeed.sh` to
| `deepspeed` | If using deepspeed, set this parameter to the configuration file for deepspeed training | configs/deepspeed_configs.json |
| `db` | Enable this to train in debugging mode i.e. with small dummy data split and only 1 data worker | N/A |

Other parameters are defined in the file `utils/train_configs.py`.
Other parameters are defined in the file `configs/train_configs.py`.

Running the script will train a critic model as a classifier that receives inputs as a problem description + a generated program and returns an output as one of 4 test outcomes: compile error, runtime error, failed tests, and passed tests. The model checkpoints are saved in a folder under `exps/`.

Expand All @@ -206,7 +206,7 @@ We created `scripts/generate_critic_scores.sh` to generate critic scores for syn
| `gt_solutions` | Enable this to run inference on ground-truth programs; else, synthetic programs are used by default | N/A |
| `binary_prediction` | Enable this to predict in binary classification i.e. passed tests or failed tests only | N/A |

Other parameters are defined in the file `utils/generate_configs.py`.
Other parameters are defined in the file `configs/generate_configs.py`.

Running the generation script will output predictions of the critic model.
For each data sample, the prediction is saved into a `pkl` (pickle) file, including data fields `code` (list of programs), `prompt` (constructed input sequence to the critic model), `gt_error_type` (ground-truth test outcomes), `pred_error_type` (predicted test outcomes by critic), `error_hidden_states` (hidden states returned by critic).
Expand All @@ -230,7 +230,7 @@ We use the parameters as defined above in the [critic training process](#trainin
| `model_path` | Path to a finetuned model checkpoint e.g. from warm-up training | models/codet5_finetuned_codeRL |
| `relative_returns` | Enable this to consider a baseline to compute relative return estimates rather than absolute return restimates in the RL loss| N/A |

Other parameters are defined in the file `utils/train_configs.py`.
Other parameters are defined in the file `configs/train_configs.py`.


Running the script will load a finetuned CodeT5-large model and continue to train it with both generated programs as well as ground-truth programs in alternative training steps.
Expand Down