Starspace

This agent contains a simple implementation of the starspace algorithm, slightly adapted for dialogue. To learn more about the starspace algorithm, see this paper.

Basic Examples

Train a starspace model on the “sentence SQuAD” task.

parlai train_model --task squad:sentence --model starspace -lr 0.01 -esz 512 -k 10 -mf /tmp/starspacesquad

DictionaryAgent Options

BPEHelper Arguments

Argument	Description
--bpe-vocab	Path to pre-trained tokenizer vocab
--bpe-merge	Path to pre-trained tokenizer merge
--bpe-dropout	Use BPE dropout during training.

StarspaceAgent Options

StarSpace Arguments

Argument	Description
--embedding-type, --emb	Choose between different strategies for initializing word embeddings. Default is random, but can also preinitialize from Glove or Fasttext. Preinitialized embeddings can also be fixed so they are not updated during training. Choices: random, glove, glove-fixed, fasttext, fasttext-fixed, fasttext_cc, fasttext_cc-fixed. Default: random.
--embeddingsize, --esz	Size of the token embeddings Default: 128.
--embeddingnorm, --enorm	Max norm of word embeddings Default: 10.
--share-embeddings, --shareEmb	Whether LHS and RHS share embeddings Default: True.
--lins	If set to 1, add a linear layer between lhs and rhs. Default: 0.
--learningrate, --lr	Learning rate Default: 0.1.
--margin, --margin	Margin Default: 0.1.
--input-dropout	Fraction of input/output features to dropout during training Default: 0.
--optimizer, --opt	Choose between pytorch optimizers. Any member of torch.optim is valid and will be used with default params except learning rate (as specified by -lr). Choices: adadelta, adagrad, adam, adamax, asgd, lbfgs, rmsprop, rprop, sgd. Default: sgd.
--truncate, --tr	Truncate input & output lengths to speed up training (may reduce accuracy). This fixes all input and output to have a maximum length. Default: -1.
--neg-samples, --k	Number k of negative samples per example Default: 10.
--parrot-neg	Include query as a negative Default: 0.
--tfidf	Use frequency based normalization for embeddings. Default: False.
--cache-size, --cs	Size of negative sample cache to draw from Default: 1000.
--history-length, --hist	Number of past tokens to remember. Default: 10000.
--history-replies, --histr	Keep replies in the history, or not. Choices: none, model, label, label_else_model. Default: label_else_model.
--fixed-candidates-file, --fixedCands	File of cands to use for prediction

BPEHelper Arguments

Argument	Description
--bpe-vocab	Path to pre-trained tokenizer vocab
--bpe-merge	Path to pre-trained tokenizer merge
--bpe-dropout	Use BPE dropout during training.