Merge branch 'main' of https://huggingface.co./rinna/japanese-gpt2-xsmall into main

README.md

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+---
+language: ja
+thumbnail: https://github.com/rinnakk/japanese-gpt2/blob/master/rinna.png
+tags:
+- ja
+- japanese
+- gpt2
+- text-generation
+- lm
+- nlp
+license: mit
+datasets:
+- cc100
+- wikipedia
+---
+
+# japanese-gpt2-xsmall
+
+![rinna-icon](./rinna.png)
+
+This repository provides an extra-small-sized Japanese GPT-2 model. The model is provided by [rinna](https://corp.rinna.co.jp/).
+
+# How to use the model
+
+*NOTE:* Use `T5Tokenizer` to initiate the tokenizer.
+
+~~~~
+from transformers import T5Tokenizer, GPT2LMHeadModel
+
+tokenizer = T5Tokenizer.from_pretrained("rinna/japanese-gpt2-small")
+tokenizer.do_lower_case = True  # due to some bug of tokenizer config loading
+
+model = GPT2LMHeadModel.from_pretrained("rinna/japanese-gpt2-small")
+~~~~
+
+# Model architecture
+A 6-layer, 512-hidden-size transformer-based language model.
+
+# Training
+The model was trained on [Japanese CC-100](http://data.statmt.org/cc-100/ja.txt.xz) and [Japanese Wikipedia](https://dumps.wikimedia.org/jawiki/) to optimize a traditional language modelling objective on 8\\*V100 GPUs for around 4 days. It reaches around 28 perplexity on a chosen validation set from CC-100.
+
+# Tokenization
+The model uses a [sentencepiece](https://github.com/google/sentencepiece)-based tokenizer, the vocabulary was trained on the Japanese Wikipedia using the official sentencepiece training script.
+
+# Licenese
+[The MIT license](https://opensource.org/licenses/MIT)