add confs and readme

README.md

@@ -40,12 +40,12 @@ We apply BERT to coreference resolution, achieving a new state of the art on the
 * Run `python predict.py <experiment> <input_file> <output_file>`, which outputs the input jsonlines with predicted clusters.
 
 ## Tune Hyperparameters
-* `python tune.py  --generate_configs --data_dir <coref_data_dir>`: This generates multiple configs for tuning (BERT and task) learning rates, embedding models, and `max_segment_len`. This modifies `experiments.conf`. Use `--trial` to print to stdout instead.
+* `python tune.py  --generate_configs --data_dir <coref_data_dir>`: This generates multiple configs for tuning (BERT and task) learning rates, embedding models, and `max_segment_len`. This modifies `experiments.conf`. Use `--trial` to print to stdout instead. If you need to generate this from scratch, refer to `basic.conf`.
 * `grep "\{best\}" experiments.conf | cut -d = -f 1 > torun.txt`: This creates a list of configs that can be used by the script to launch jobs. You can use a regexp to restrict the list of configs. For example, `grep "\{best\}" experiments.conf | grep "sl512*" | cut -d = -f 1 > torun.txt` will select configs with `max_segment_len = 512`.
 * `python tune_models.py --data_dir <coref_data_dir> --run_jobs`: This launches jobs from torun.txt on the slurm cluster.
 
 ## Important Config Keys
-* `log_root`: This is where all models and logs are stored.
+* `log_root`: This is where all models and logs are stored. Check this before running anything.
 * `bert_learning_rate`: The learning rate for the BERT parameters. Typically, `1e-5` and `2e-5` work well.
 * `task_learning_rate`: The learning rate for the other parameters. Typically, LRs between `0.0001` to `0.0003` work well.
 * `init_checkpoint`: The checkpoint file from which BERT parameters are initialized. Both TF and Pytorch checkpoints work as long as they use the same BERT architecture. Use `*ckpt` files for TF and `*pt` for Pytorch.