Update word-level LM arguments in README (Taeung#786)

sim1st · Jun 8, 2020 · 7016919 · 7016919
1 parent 59423c5
commit 7016919
Showing 1 changed file with 22 additions and 23 deletions.
diff --git a/word_language_model/README.md b/word_language_model/README.md
@@ -25,29 +25,28 @@ The `main.py` script accepts the following arguments:
 
 ```bash
 optional arguments:
-  -h, --help                       show this help message and exit
-  --data DATA                      location of the data corpus
-  --model MODEL                    type of recurrent net (RNN_TANH, RNN_RELU, LSTM, GRU)
-  --emsize EMSIZE                  size of word embeddings
-  --nhid NHID                      number of hidden units per layer
-  --nlayers NLAYERS                number of layers
-  --lr LR                          initial learning rate
-  --clip CLIP                      gradient clipping
-  --epochs EPOCHS                  upper epoch limit
-  --batch_size N                   batch size
-  --bptt BPTT                      sequence length
-  --dropout DROPOUT                dropout applied to layers (0 = no dropout)
-  --decay DECAY                    learning rate decay per epoch
-  --tied                           tie the word embedding and softmax weights
-  --seed SEED                      random seed
-  --cuda                           use CUDA
-  --log-interval N                 report interval
-  --save SAVE                      path to save the final model
-  --onnx-export                    path to export the final model in onnx format
-  --transformer_head N             the number of heads in the encoder/decoder of the transformer model
-  --transformer_encoder_layers N   the number of layers in the encoder of the transformer model
-  --transformer_decoder_layers N   the number of layers in the decoder of the transformer model
-  --transformer_d_ff N             the number of nodes on the hidden layer in feed forward nn
+  -h, --help            show this help message and exit
+  --data DATA           location of the data corpus
+  --model MODEL         type of recurrent net (RNN_TANH, RNN_RELU, LSTM, GRU,
+                        Transformer)
+  --emsize EMSIZE       size of word embeddings
+  --nhid NHID           number of hidden units per layer
+  --nlayers NLAYERS     number of layers
+  --lr LR               initial learning rate
+  --clip CLIP           gradient clipping
+  --epochs EPOCHS       upper epoch limit
+  --batch_size N        batch size
+  --bptt BPTT           sequence length
+  --dropout DROPOUT     dropout applied to layers (0 = no dropout)
+  --tied                tie the word embedding and softmax weights
+  --seed SEED           random seed
+  --cuda                use CUDA
+  --log-interval N      report interval
+  --save SAVE           path to save the final model
+  --onnx-export ONNX_EXPORT
+                        path to export the final model in onnx format
+  --nhead NHEAD         the number of heads in the encoder/decoder of the
+                        transformer model
 ```
 
 With these arguments, a variety of models can be tested.