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deberta-v2-tiny-japanese

Inference Endpoints

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nobu-g commited on Jan 18, 2023

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@@ -60,7 +60,7 @@ We first segmented texts in the corpora into words using [Juman++](https://githu
 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 DeBERTa model using [transformers](https://github.com/huggingface/transformers) library.
-The training took three weeks using 8 NVIDIA A100-SXM4-40GB GPUs.
 The following hyperparameters were used during pre-training:

 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 DeBERTa model using [transformers](https://github.com/huggingface/transformers) library.
+The training took 33 hours using 8 NVIDIA A100-SXM4-40GB GPUs.
 The following hyperparameters were used during pre-training: