Transformer

We offer a variety of agent implementations whose core model is the transformer, a self-attention based encoding mechanism first described in Vaswani et al 2017.

Agent Variations

• transformer/biencoder - A retrieval-based agent that encodes a context sequence and a candidate sequence with separate BERT-based Transformers. A candidate is chosen via the highest dot-product score between the context and candidate encodings. See Humeau et al 2019 for more details.

• transformer/classifier - A classifier agent with a Transformer as the model.

• transformer/crossencoder - A retrieval-based agent that jointly encodes a context and candidate sequence in a single BERT-based Transformer, with a final linear layer used to compute a score. A candidate is chosen via the highest scoring encoding. See Humeau et al 2019 for more details.

• transformer/generator - A generative-based agent that performs seq2seq encoding/decoding with transformer encoders/decoders.

• transformer/polyencoder - A retrieval-based agent that, similar to the bi-encoder agent, encodes context and candidate sequences with separate BERT-based Transformers. However, to compute a final score, the agent performs an additional layer of attention using global context vectors before computing the final dot product, thus incorporating the candidate encoding into the context encoding prior to producing a dot-product score. See Humeau et al 2019 for more details.

• transformer/ranker - A retrieval-based agent that encodes a context sequence and a candidate sequence with separate Transformers, before computing a dot-product to obtain a score for a candidate encoding.

TransformerClassifierAgent Options

optional arguments

Argument	Description
--share-word-embeddings	Share word embeddings table for candidate and contextin the memory network Default: True.
--load-from-pretrained-ranker	Load model from base transformer ranking model (used for pretraining) Default: False.

TorchRankerAgent

Argument	Description
--candidates, --cands	The source of candidates during training (see TorchRankerAgent._build_candidates() for details). Choices: batch, inline, fixed, batch-all-cands. Default: inline.
--eval-candidates, --ecands	The source of candidates during evaluation (defaults to the samevalue as –candidates if no flag is given) Choices: batch, inline, fixed, vocab, batch-all-cands. Default: inline.
--interactive-candidates, --icands	The source of candidates during interactive mode. Since in interactive mode, batchsize == 1, we cannot use batch candidates. Choices: fixed, inline, vocab. Default: fixed.
--repeat-blocking-heuristic	Block repeating previous utterances. Helpful for many models that score repeats highly, so switched on by default. Default: True.
--fixed-candidates-path, --fcp	A text file of fixed candidates to use for all examples, one candidate per line
--fixed-candidate-vecs	One of “reuse”, “replace”, or a path to a file with vectors corresponding to the candidates at –fixed-candidates-path. The default path is a /path/to/model-file.<cands_name>, where <cands_name> is the name of the file (not the full path) passed by the flag –fixed-candidates-path. By default, this file is created once and reused. To replace it, use the “replace” option. Default: reuse.
--encode-candidate-vecs	Cache and save the encoding of the candidate vecs. This might be used when interacting with the model in real time or evaluating on fixed candidate set when the encoding of the candidates is independent of the input. Default: True.
--init-model	Initialize model with weights from this file.
--train-predict	Get predictions and calculate mean rank during the train step. Turning this on may slow down training. Default: False.
--cap-num-predictions	Limit to the number of predictions in output.text_candidates Default: 100.
--ignore-bad-candidates	Ignore examples for which the label is not present in the label candidates. Default behavior results in RuntimeError. Default: False.
--rank-top-k	Ranking returns the top k results of k > 0, otherwise sorts every single candidate according to the ranking. Default: -1.
--inference	Final response output algorithm Choices: max, topk. Default: max.
--topk	K used in Top K sampling inference, when selected Default: 5.
--return-cand-scores	Return sorted candidate scores from eval_step Default: False.

Transformer Arguments

Argument	Description
--embedding-size, --esz	Size of all embedding layers. Must be a multiple of –n-heads. Default: 300.
--n-layers, --nl	Number of transformer layers. Default: 2.
--ffn-size, --hid	Hidden size of the FFN layers Default: 300.
--dropout	Dropout used around embeddings and before layer layer normalizations. This is used in Vaswani 2017 and works well on large datasets. Default: 0.0.
--attention-dropout	Dropout used after attention softmax. This is not used in Vaswani 2017. Default: 0.0.
--relu-dropout	Dropout used after the ReLU in the FFN. Not used in Vaswani 2017, but used in Tensor2Tensor. Default: 0.0.
--n-heads	Number of multihead attention heads Default: 2.
--learn-positional-embeddings	If off, sinusoidal embeddings are used. If on, position embeddings are learned from scratch. Default: False.
--embeddings-scale	Default: True.
--n-segments	The number of segments that support the model. If zero no segment and no langs_embedding. Default: 0.
--variant	Chooses locations of layer norms, etc. prelayernorm is used to match some fairseq models Choices: xlm, prelayernorm, bart, aiayn. Default: aiayn. Recommended: xlm.
--activation	Nonlinear activation to use. AIAYN uses relu, but more recent papers prefer gelu. Choices: gelu, relu. Default: relu. Recommended: gelu.
--output-scaling	Scale the output of every transformer by this quantity. Default: 1.0.
--n-encoder-layers, --nel	This will overidde the n-layers for asymmetrical transformers Default: -1.
--n-decoder-layers, --ndl	This will overidde the n-layers for asymmetrical transformers Default: -1.
--model-parallel	Shard the layers across multiple GPUs. Default: False.
--checkpoint-activations	Recompute activations on backward pass to conserve memory. Default: False.
--use-memories	Use memories: must implement the function _vectorize_memories to use this Default: False.
--wrap-memory-encoder	Wrap memory encoder with MLP Default: False.
--memory-attention	Similarity for basic attention mechanism when using transformer to encode memories Choices: cosine, dot, sqrt. Default: sqrt.
--normalize-sent-emb	Default: False.
--share-encoders	Default: True.
--learn-embeddings	Learn embeddings Default: True.
--reduction-type	Type of reduction at the end of transformer Choices: first, max, mean. Default: first.

TorchAgent Arguments

Argument	Description
--interactive-mode, --i	Whether in full interactive mode or not, which means generating text or retrieving from a full set of candidates, which is necessary to actually do full dialogue. However, during training or quick validation (e.g. PPL for generation or ranking a few candidates for ranking models) you might want these set to off. Typically, scripts can set their preferred default behavior at the start, e.g. eval scripts. Default: False.
--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.
--embedding-projection, --embp	If pretrained embeddings have a different dimensionality than your embedding size, strategy for projecting to the correct size. If the dimensions are the same, this is ignored unless you append “-force” to your choice. Default: random.
--fp16	Use fp16 computations. Default: False.
--fp16-impl	Implementation of FP16 to use Choices: safe, mem_efficient. Default: safe.
--rank-candidates, --rc	Whether the model should parse candidates for ranking. Default: False.
--truncate, --tr	Truncate input lengths to increase speed / use less memory. Default: -1.
--text-truncate	Text input truncation length: if not specified, this will default to truncate
--label-truncate	Label truncation length: if not specified, this will default to truncate
--history-reversed	Reverse the history Default: False.
--history-size, --histsz	Number of past dialog utterances to remember. Default: -1.
--person-tokens, --pt	Add person tokens to history. adds p1 in front of input text and p2 in front of past labels when available or past utterances generated by the model. these are added to the dictionary during initialization. Default: False.
--split-lines	Split the dialogue history on newlines and save in separate vectors Default: False.
--delimiter	Join history lines with this token, defaults to newline Default: \n.
--special-tok-lst	Comma separated list of special tokens. In case of ambiguous parses from special tokens, the ordering provided in this arg sets precedence.
-gpu, --gpu	Which GPU to use Default: -1.
--no-cuda	Disable GPUs even if available. otherwise, will use GPUs if available on the device. Default: False.

Optimizer Arguments

Argument	Description
--optimizer, --opt	Optimizer choice. Possible values: adadelta, adagrad, adam, adamw, sparseadam, adamax, asgd, sgd, radam, rprop, rmsprop, optimizer, nadam, lbfgs, mem_eff_adam, adafactor. Choices: adadelta, adagrad, adam, adamw, sparseadam, adamax, asgd, sgd, radam, rprop, rmsprop, optimizer, nadam, lbfgs, mem_eff_adam, adafactor. Default: sgd.
--learningrate, --lr	Learning rate Default: 1.
--gradient-clip, --clip	Gradient clipping using l2 norm Default: 0.1.
--adafactor-eps	Epsilon values for adafactor optimizer: regularization constants for square gradient and parameter scale respectively Default: 1e-30,1e-3. Recommended: 1e-30,1e-3.
--momentum, --mom	If applicable, momentum value for optimizer. Default: 0.
--nesterov	If applicable, whether to use nesterov momentum. Default: True.
--nus, --nu	If applicable, nu value(s) for optimizer. can use a single value like 0.7 or a comma-separated tuple like 0.7,1.0 Default: 0.7.
--betas, --beta	If applicable, beta value(s) for optimizer. can use a single value like 0.9 or a comma-separated tuple like 0.9,0.999 Default: 0.9,0.999.
--weight-decay, --wdecay	Weight decay on the weights.

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.

Learning Rate Scheduler

Argument	Description
--lr-scheduler	Learning rate scheduler. Choices: reduceonplateau, none, fixed, invsqrt, cosine, linear. Default: reduceonplateau.
--lr-scheduler-patience	LR scheduler patience. In number of validation runs. If using fixed scheduler, LR is decayed every validations. Default: 3.
--lr-scheduler-decay	Decay factor for LR scheduler, or how much LR is multiplied by when it is lowered. Default: 0.5.
--invsqrt-lr-decay-gamma	Constant used only to find the lr multiplier for the invsqrt scheduler. Must be set for –lr-scheduler invsqrt Default: -1.

Torch Classifier Arguments

Argument	Description
--classes	The name of the classes.
--class-weights	Weight of each of the classes for the softmax
--threshold	During evaluation, threshold for choosing ref class; only applies to binary classification Default: 0.5.
--print-scores	Print probability of chosen class during interactive mode Default: False.
--data-parallel	Uses nn.DataParallel for multi GPU Default: False.
--classes-from-file	Loads the list of classes from a file
--ignore-labels	Ignore labels provided to model
--update-classifier-head-only	Freeze the encoder and update the classifier head only Default: False.

TransformerGeneratorAgent Options

optional arguments

Argument	Description
--gpu-beam-blocking	Set to use CUDA kernel for beam search ngram blocking Default: False.
--verbose-topk	Return the topk logits in the act message, if verbose mode is set. Default: -1.

Transformer Arguments

Argument	Description
--embedding-size, --esz	Size of all embedding layers. Must be a multiple of –n-heads. Default: 300.
--n-layers, --nl	Number of transformer layers. Default: 2.
--ffn-size, --hid	Hidden size of the FFN layers Default: 300.
--dropout	Dropout used around embeddings and before layer layer normalizations. This is used in Vaswani 2017 and works well on large datasets. Default: 0.0.
--attention-dropout	Dropout used after attention softmax. This is not used in Vaswani 2017. Default: 0.0.
--relu-dropout	Dropout used after the ReLU in the FFN. Not used in Vaswani 2017, but used in Tensor2Tensor. Default: 0.0.
--n-heads	Number of multihead attention heads Default: 2.
--learn-positional-embeddings	If off, sinusoidal embeddings are used. If on, position embeddings are learned from scratch. Default: False.
--embeddings-scale	Default: True.
--n-segments	The number of segments that support the model. If zero no segment and no langs_embedding. Default: 0.
--variant	Chooses locations of layer norms, etc. prelayernorm is used to match some fairseq models Choices: xlm, prelayernorm, bart, aiayn. Default: aiayn. Recommended: xlm.
--activation	Nonlinear activation to use. AIAYN uses relu, but more recent papers prefer gelu. Choices: gelu, relu. Default: relu. Recommended: gelu.
--output-scaling	Scale the output of every transformer by this quantity. Default: 1.0.
--share-word-embeddings	Share word embeddings table for candidate and contextin the memory network Default: True.
--n-encoder-layers, --nel	This will overidde the n-layers for asymmetrical transformers Default: -1.
--n-decoder-layers, --ndl	This will overidde the n-layers for asymmetrical transformers Default: -1.
--model-parallel	Shard the layers across multiple GPUs. Default: False.
--checkpoint-activations	Recompute activations on backward pass to conserve memory. Default: False.

Torch Generator Agent

Argument	Description
--beam-size	Beam size, if 1 then greedy search Default: 1.
--beam-min-length	Minimum length of prediction to be generated by the beam search Default: 1.
--beam-context-block-ngram	Size n-grams to block in beam search from the context. val <= 0 implies no blocking Default: -1.
--beam-block-ngram	Size n-grams to block in beam search. val <= 0 implies no blocking Default: -1.
--beam-block-full-context	Block n-grams from the full history context. Specify False to block up to m tokens in the past, where m is truncation parameter for agent Default: True.
--beam-length-penalty	Applies a length penalty. Set to 0 for no penalty. Default: 0.65.
--inference	Generation algorithm Choices: beam, nucleus, delayedbeam, greedy, delayednucleusbeam, topk, factual_nucleus. Default: greedy.
--topk	K used in Top K sampling Default: 10.
--topp	P used in nucleus sampling Default: 0.9.
--beam-delay	Used in delayedbeam search Default: 30.
--lambda-decay	Decay factor in factual nucleus sampling Default: 0.9.
--omega-bound	Lower bound in factual nucleus sampling Default: 0.3.
--p-reset	Whether to reset p value in factual nucleus at full stops Default: True.
--beam-block-list-filename	Load a text file of hard blocks for beam search to never say.
--temperature	Temperature to add during decoding Default: 1.0.
--compute-tokenized-bleu	If true, compute tokenized bleu scores Default: False.

TorchAgent Arguments

Argument	Description
--interactive-mode, --i	Whether in full interactive mode or not, which means generating text or retrieving from a full set of candidates, which is necessary to actually do full dialogue. However, during training or quick validation (e.g. PPL for generation or ranking a few candidates for ranking models) you might want these set to off. Typically, scripts can set their preferred default behavior at the start, e.g. eval scripts. Default: False.
--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.
--embedding-projection, --embp	If pretrained embeddings have a different dimensionality than your embedding size, strategy for projecting to the correct size. If the dimensions are the same, this is ignored unless you append “-force” to your choice. Default: random.
--fp16	Use fp16 computations. Default: False.
--fp16-impl	Implementation of FP16 to use Choices: safe, mem_efficient. Default: safe.
--rank-candidates, --rc	Whether the model should parse candidates for ranking. Default: False.
--truncate, --tr	Truncate input lengths to increase speed / use less memory. Default: -1.
--text-truncate	Text input truncation length: if not specified, this will default to truncate
--label-truncate	Label truncation length: if not specified, this will default to truncate
--history-reversed	Reverse the history Default: False.
--history-size, --histsz	Number of past dialog utterances to remember. Default: -1.
--person-tokens, --pt	Add person tokens to history. adds p1 in front of input text and p2 in front of past labels when available or past utterances generated by the model. these are added to the dictionary during initialization. Default: False.
--split-lines	Split the dialogue history on newlines and save in separate vectors Default: False.
--delimiter	Join history lines with this token, defaults to newline Default: \n.
--special-tok-lst	Comma separated list of special tokens. In case of ambiguous parses from special tokens, the ordering provided in this arg sets precedence.
-gpu, --gpu	Which GPU to use Default: -1.
--no-cuda	Disable GPUs even if available. otherwise, will use GPUs if available on the device. Default: False.

Optimizer Arguments

Argument	Description
--optimizer, --opt	Optimizer choice. Possible values: adadelta, adagrad, adam, adamw, sparseadam, adamax, asgd, sgd, radam, rprop, rmsprop, optimizer, nadam, lbfgs, mem_eff_adam, adafactor. Choices: adadelta, adagrad, adam, adamw, sparseadam, adamax, asgd, sgd, radam, rprop, rmsprop, optimizer, nadam, lbfgs, mem_eff_adam, adafactor. Default: sgd.
--learningrate, --lr	Learning rate Default: 1.
--gradient-clip, --clip	Gradient clipping using l2 norm Default: 0.1.
--adafactor-eps	Epsilon values for adafactor optimizer: regularization constants for square gradient and parameter scale respectively Default: 1e-30,1e-3. Recommended: 1e-30,1e-3.
--momentum, --mom	If applicable, momentum value for optimizer. Default: 0.
--nesterov	If applicable, whether to use nesterov momentum. Default: True.
--nus, --nu	If applicable, nu value(s) for optimizer. can use a single value like 0.7 or a comma-separated tuple like 0.7,1.0 Default: 0.7.
--betas, --beta	If applicable, beta value(s) for optimizer. can use a single value like 0.9 or a comma-separated tuple like 0.9,0.999 Default: 0.9,0.999.
--weight-decay, --wdecay	Weight decay on the weights.

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.

Learning Rate Scheduler

Argument	Description
--lr-scheduler	Learning rate scheduler. Choices: reduceonplateau, none, fixed, invsqrt, cosine, linear. Default: reduceonplateau.
--lr-scheduler-patience	LR scheduler patience. In number of validation runs. If using fixed scheduler, LR is decayed every validations. Default: 3.
--lr-scheduler-decay	Decay factor for LR scheduler, or how much LR is multiplied by when it is lowered. Default: 0.5.
--invsqrt-lr-decay-gamma	Constant used only to find the lr multiplier for the invsqrt scheduler. Must be set for –lr-scheduler invsqrt Default: -1.

TransformerRankerAgent Options

optional arguments

Argument	Description
--share-word-embeddings	Share word embeddings table for candidate and contextin the memory network Default: True.

TorchAgent Arguments

Argument	Description
--interactive-mode, --i	Whether in full interactive mode or not, which means generating text or retrieving from a full set of candidates, which is necessary to actually do full dialogue. However, during training or quick validation (e.g. PPL for generation or ranking a few candidates for ranking models) you might want these set to off. Typically, scripts can set their preferred default behavior at the start, e.g. eval scripts. Default: False.
--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.
--embedding-projection, --embp	If pretrained embeddings have a different dimensionality than your embedding size, strategy for projecting to the correct size. If the dimensions are the same, this is ignored unless you append “-force” to your choice. Default: random.
--fp16	Use fp16 computations. Default: False.
--fp16-impl	Implementation of FP16 to use Choices: safe, mem_efficient. Default: safe.
--rank-candidates, --rc	Whether the model should parse candidates for ranking. Default: False.
--truncate, --tr	Truncate input lengths to increase speed / use less memory. Default: 1024.
--text-truncate	Text input truncation length: if not specified, this will default to truncate
--label-truncate	Label truncation length: if not specified, this will default to truncate
--history-reversed	Reverse the history Default: False.
--history-size, --histsz	Number of past dialog utterances to remember. Default: -1.
--person-tokens, --pt	Add person tokens to history. adds p1 in front of input text and p2 in front of past labels when available or past utterances generated by the model. these are added to the dictionary during initialization. Default: False.
--split-lines	Split the dialogue history on newlines and save in separate vectors Default: False.
--delimiter	Join history lines with this token, defaults to newline Default: \n.
--special-tok-lst	Comma separated list of special tokens. In case of ambiguous parses from special tokens, the ordering provided in this arg sets precedence.
-gpu, --gpu	Which GPU to use Default: -1.
--no-cuda	Disable GPUs even if available. otherwise, will use GPUs if available on the device. Default: False.

Optimizer Arguments

Argument	Description
--optimizer, --opt	Optimizer choice. Possible values: adadelta, adagrad, adam, adamw, sparseadam, adamax, asgd, sgd, radam, rprop, rmsprop, optimizer, nadam, lbfgs, mem_eff_adam, adafactor. Choices: adadelta, adagrad, adam, adamw, sparseadam, adamax, asgd, sgd, radam, rprop, rmsprop, optimizer, nadam, lbfgs, mem_eff_adam, adafactor. Default: adamax.
--learningrate, --lr	Learning rate Default: 0.0001.
--gradient-clip, --clip	Gradient clipping using l2 norm Default: 0.1.
--adafactor-eps	Epsilon values for adafactor optimizer: regularization constants for square gradient and parameter scale respectively Default: 1e-30,1e-3. Recommended: 1e-30,1e-3.
--momentum, --mom	If applicable, momentum value for optimizer. Default: 0.
--nesterov	If applicable, whether to use nesterov momentum. Default: True.
--nus, --nu	If applicable, nu value(s) for optimizer. can use a single value like 0.7 or a comma-separated tuple like 0.7,1.0 Default: 0.7.
--betas, --beta	If applicable, beta value(s) for optimizer. can use a single value like 0.9 or a comma-separated tuple like 0.9,0.999 Default: 0.9,0.999.
--weight-decay, --wdecay	Weight decay on the weights.

Learning Rate Scheduler

Argument	Description
--lr-scheduler	Learning rate scheduler. Choices: reduceonplateau, none, fixed, invsqrt, cosine, linear. Default: reduceonplateau.
--lr-scheduler-patience	LR scheduler patience. In number of validation runs. If using fixed scheduler, LR is decayed every validations. Default: 3.
--lr-scheduler-decay	Decay factor for LR scheduler, or how much LR is multiplied by when it is lowered. Default: 0.5.
--invsqrt-lr-decay-gamma	Constant used only to find the lr multiplier for the invsqrt scheduler. Must be set for –lr-scheduler invsqrt Default: -1.

TorchRankerAgent

Argument	Description
--candidates, --cands	The source of candidates during training (see TorchRankerAgent._build_candidates() for details). Choices: batch, inline, fixed, batch-all-cands. Default: inline.
--eval-candidates, --ecands	The source of candidates during evaluation (defaults to the samevalue as –candidates if no flag is given) Choices: batch, inline, fixed, vocab, batch-all-cands. Default: inline.
--interactive-candidates, --icands	The source of candidates during interactive mode. Since in interactive mode, batchsize == 1, we cannot use batch candidates. Choices: fixed, inline, vocab. Default: fixed.
--repeat-blocking-heuristic	Block repeating previous utterances. Helpful for many models that score repeats highly, so switched on by default. Default: True.
--fixed-candidates-path, --fcp	A text file of fixed candidates to use for all examples, one candidate per line
--fixed-candidate-vecs	One of “reuse”, “replace”, or a path to a file with vectors corresponding to the candidates at –fixed-candidates-path. The default path is a /path/to/model-file.<cands_name>, where <cands_name> is the name of the file (not the full path) passed by the flag –fixed-candidates-path. By default, this file is created once and reused. To replace it, use the “replace” option. Default: reuse.
--encode-candidate-vecs	Cache and save the encoding of the candidate vecs. This might be used when interacting with the model in real time or evaluating on fixed candidate set when the encoding of the candidates is independent of the input. Default: True.
--init-model	Initialize model with weights from this file.
--train-predict	Get predictions and calculate mean rank during the train step. Turning this on may slow down training. Default: False.
--cap-num-predictions	Limit to the number of predictions in output.text_candidates Default: 100.
--ignore-bad-candidates	Ignore examples for which the label is not present in the label candidates. Default behavior results in RuntimeError. Default: False.
--rank-top-k	Ranking returns the top k results of k > 0, otherwise sorts every single candidate according to the ranking. Default: -1.
--inference	Final response output algorithm Choices: max, topk. Default: max.
--topk	K used in Top K sampling inference, when selected Default: 5.
--return-cand-scores	Return sorted candidate scores from eval_step Default: False.

Transformer Arguments

Argument	Description
--embedding-size, --esz	Size of all embedding layers. Must be a multiple of –n-heads. Default: 300.
--n-layers, --nl	Number of transformer layers. Default: 2.
--ffn-size, --hid	Hidden size of the FFN layers Default: 300.
--dropout	Dropout used around embeddings and before layer layer normalizations. This is used in Vaswani 2017 and works well on large datasets. Default: 0.0.
--attention-dropout	Dropout used after attention softmax. This is not used in Vaswani 2017. Default: 0.0.
--relu-dropout	Dropout used after the ReLU in the FFN. Not used in Vaswani 2017, but used in Tensor2Tensor. Default: 0.0.
--n-heads	Number of multihead attention heads Default: 2.
--learn-positional-embeddings	If off, sinusoidal embeddings are used. If on, position embeddings are learned from scratch. Default: False.
--embeddings-scale	Default: True.
--n-segments	The number of segments that support the model. If zero no segment and no langs_embedding. Default: 0.
--variant	Chooses locations of layer norms, etc. prelayernorm is used to match some fairseq models Choices: xlm, prelayernorm, bart, aiayn. Default: aiayn. Recommended: xlm.
--activation	Nonlinear activation to use. AIAYN uses relu, but more recent papers prefer gelu. Choices: gelu, relu. Default: relu. Recommended: gelu.
--output-scaling	Scale the output of every transformer by this quantity. Default: 1.0.
--n-encoder-layers, --nel	This will overidde the n-layers for asymmetrical transformers Default: -1.
--n-decoder-layers, --ndl	This will overidde the n-layers for asymmetrical transformers Default: -1.
--model-parallel	Shard the layers across multiple GPUs. Default: False.
--checkpoint-activations	Recompute activations on backward pass to conserve memory. Default: False.
--use-memories	Use memories: must implement the function _vectorize_memories to use this Default: False.
--wrap-memory-encoder	Wrap memory encoder with MLP Default: False.
--memory-attention	Similarity for basic attention mechanism when using transformer to encode memories Choices: cosine, dot, sqrt. Default: sqrt.
--normalize-sent-emb	Default: False.
--share-encoders	Default: True.
--learn-embeddings	Learn embeddings Default: True.
--data-parallel	Use model in data parallel, requires multiple gpus Default: False.
--reduction-type	Type of reduction at the end of transformer Choices: first, max, mean. Default: mean.

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.