ku-nlp
/

bart-large-japanese

@@ -43,7 +43,7 @@ We used the following corpora for pre-training:
 We first segmented texts in the corpora into words using [Juman++](https://github.com/ku-nlp/jumanpp).
 Then, we built a sentencepiece model with 32000 tokens including words ([JumanDIC](https://github.com/ku-nlp/JumanDIC)) and subwords induced by the unigram language model of [sentencepiece](https://github.com/google/sentencepiece).
-We tokenized the segmented corpora into subwords using the sentencepiece model and trained the Japanese BART model using [transformers](https://github.com/huggingface/transformers) library.
 The training took about 1 month using 4 Tesla V100 GPUs.
 The following hyperparameters were used during pre-training:

 We first segmented texts in the corpora into words using [Juman++](https://github.com/ku-nlp/jumanpp).
 Then, we built a sentencepiece model with 32000 tokens including words ([JumanDIC](https://github.com/ku-nlp/JumanDIC)) and subwords induced by the unigram language model of [sentencepiece](https://github.com/google/sentencepiece).
+We tokenized the segmented corpora into subwords using the sentencepiece model and trained the Japanese BART model using [fairseq](https://github.com/facebookresearch/fairseq) library.
 The training took about 1 month using 4 Tesla V100 GPUs.
 The following hyperparameters were used during pre-training: