| eval_every |
|
int |
| passes |
Passes parameter. Default: 1 |
int |
| num_topics |
Number of topics for the trained model to have. Default: 100 |
int |
| decay |
Decay parameter. Default: 0.5 |
float |
| minimum_phi_value |
|
float |
| distributed |
Turn on distributed computing. Default: False |
bool |
| update_every |
Model’s update_every parameter. Default: 1 |
int |
| tfidf |
Transform word counts using TF-IDF when presenting documents to the model for training. Default: False |
bool |
| ignore_terms |
Ignore any of these terms in the bags of words when iterating over the corpus to train the model. Typically, you’ll want to include an OOV term here if your corpus has one, and any other special terms that are not part of a document’s content |
comma-separated list of strings |
| eta |
Eta prior of word distribution. May be one of special values ‘auto’ and ‘symmetric’, or a float. Default: symmetric |
<function eta_opt at 0x7f7a8db26500> |
| iterations |
Max number of iterations in each update. Default: 50 |
int |
| offset |
Offset parameter. Default: 1.0 |
float |
| gamma_threshold |
|
float |
| alpha |
Alpha prior over topic distribution. May be one of special values ‘symmetric’, ‘asymmetric’ and ‘auto’, or a single float, or a list of floats. Default: symmetric |
<function alpha_opt at 0x7f7a8db262a8> |
| minimum_probability |
|
float |
| chunksize |
Model’s chunksize parameter. Chunk size to use for distributed computing. Default: 2000 |
int |