Add documentation for help flags (#81)

add description for flags

README.md

@@ -75,13 +75,23 @@ There are various flags for controlling the samples:
 python3 src/generate_unconditional_samples.py --top_k 40 --temperature 0.7 | tee /tmp/samples
 ```
 
+To check flag descriptions, use:
+```
+python3 src/generate_unconditional_samples.py -- --help
+```
+
 ### Conditional sample generation
 
 To give the model custom prompts, you can use:
 ```
 python3 src/interactive_conditional_samples.py --top_k 40
 ```
 
+To check flag descriptions, use:
+```
+python3 src/interactive_conditional_samples.py -- --help
+```
+
 ## GPT-2 samples
 
 | WARNING: Samples are unfiltered and may contain offensive content. |

src/generate_unconditional_samples.py

@@ -17,6 +17,26 @@ def sample_model(
  temperature=1,
  top_k=0,
 ):
+ """
+ Run the sample_model
+ :model_name=117M : String, which model to use
+ :seed=None : Integer seed for random number generators, fix seed to
+ reproduce results
+ :nsamples=0 : Number of samples to return, if 0, continues to
+ generate samples indefinately.
+ :batch_size=1 : Number of batches, model runs nsamples//batch_size
+ times, each batch run is independent of previous run.
+ :length=None : Number of tokens in generated text, if None (default), is
+ determined by model hyperparameters
+ :temperature=1 : Float value controlling randomness in boltzmann
+ distribution. Lower temperature results in less random completions. As the
+ temperature approaches zero, the model will become deterministic and
+ repetitive. Higher temperature results in more random completions.
+ :top_k=0 : Integer value controlling diversity. 1 means only 1 word is
+ considered for each step (token), resulting in deterministic completions,
+ while 40 means 40 words are considered at each step. 0 (default) is a
+ special setting meaning no restrictions. 40 generally is a good value.
+ """
  enc = encoder.get_encoder(model_name)
  hparams = model.default_hparams()
  with open(os.path.join('models', model_name, 'hparams.json')) as f:

src/interactive_conditional_samples.py

@@ -12,11 +12,30 @@ def interact_model(
  model_name='117M',
  seed=None,
  nsamples=1,
- batch_size=None,
+ batch_size=1,
  length=None,
  temperature=1,
  top_k=0,
 ):
+ """
+ Interactively run the model
+ :model_name=117M : String, which model to use
+ :seed=None : Integer seed for random number generators, fix seed to reproduce
+ results
+ :nsamples=1 : Number of samples to return
+ :batch_size=1 : Number of batches, model runs nsamples//batch_size
+ times, each batch run is independent of previous run.
+ :length=None : Number of tokens in generated text, if None (default), is
+ determined by model hyperparameters
+ :temperature=1 : Float value controlling randomness in boltzmann
+ distribution. Lower temperature results in less random completions. As the
+ temperature approaches zero, the model will become deterministic and
+ repetitive. Higher temperature results in more random completions.
+ :top_k=0 : Integer value controlling diversity. 1 means only 1 word is
+ considered for each step (token), resulting in deterministic completions,
+ while 40 means 40 words are considered at each step. 0 (default) is a
+ special setting meaning no restrictions. 40 generally is a good value.
+ """
  if batch_size is None:
  batch_size = 1
  assert nsamples % batch_size == 0