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Model: nlp-waseda/gpt2-xl-japanese
Source: Original Platform

.gitattributes

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README.md

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+---
+language:
+- ja
+license: cc-by-sa-4.0
+datasets:
+- wikipedia
+- cc100
+widget:
+- text: "早稲田 大学 で 自然 言語 処理 を"
+---
+
+# nlp-waseda/gpt2-xl-japanese
+
+This is Japanese GPT2 with approximately　1.5B parameters pretrained on Japanese Wikipedia and CC-100
+The model architecture of the model are based on [Radford+ 2019](https://paperswithcode.com/paper/language-models-are-unsupervised-multitask).
+
+## Intended uses & limitations
+
+You can use the raw model for text generation or fine-tune it to a downstream task.
+
+Note that the texts should be segmented into words using [Juman++](https://github.com/ku-nlp/jumanpp) in advance.
+
+### How to use
+
+You can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we set a seed for reproducibility:
+
+```python
+from transformers import pipeline, set_seed
+generator = pipeline('text-generation', model='nlp-waseda/gpt2-xl-japanese')
+# If you use gpu.
+# generator = pipeline('text-generation', model='nlp-waseda/gpt2-xl-japanese', device=0)
+
+set_seed(42)
+generator("早稲田 大学 で 自然 言語 処理 を", max_length=30, do_sample=True, pad_token_id=2, num_return_sequences=5)
+[{'generated_text': '早稲田 大学 で 自然 言語 処理 を 勉強 して いる 大学生 です. 自然 言語 処理 や 音声 認識, 機械 学習 等 に 興味 が あり, 特に 画像'},
+ {'generated_text': '早稲田 大学 で 自然 言語 処理 を 学んで いる と ある 方 と お 会い して き ました. 今日 は お 話 する 時間 が 少なかった のです が,'},
+ {'generated_text': '早稲田 大学 で 自然 言語 処理 を 研究 して いる が 、 それ を 趣味 と は 思わず 、 会社 を 作る ため の 手段 と とらえて いる ようです 。'},
+ {'generated_text': '早稲田 大学 で 自然 言語 処理 を 専門 的に 学ぶ サークル です 。 日本 語 教育 センター で 日本 語 を 勉強 した 中国 の 人 たち と 交流 する'},
+ {'generated_text': '早稲田 大学 で 自然 言語 処理 を 専攻 した 時 に 、 数学 の 知識 ・ プログラミング 言語 の 知識 が 身 に ついて いた の は 、 とても 役'}]
+```
+
+```python
+from transformers import AutoTokenizer, GPT2Model
+tokenizer = AutoTokenizer.from_pretrained('nlp-waseda/gpt2-xl-japanese')
+model = GPT2Model.from_pretrained('nlp-waseda/gpt2-xl-japanese')
+text = "早稲田 大学 で 自然 言語 処理 を"
+encoded_input = tokenizer(text, return_tensors='pt')
+output = model(**encoded_input)
+```
+
+### Preprocessing
+
+The texts are normalized using [neologdn](https://github.com/ikegami-yukino/neologdn), segmented into words using [Juman++](https://github.com/ku-nlp/jumanpp), and tokenized by [BPE](https://huggingface.co/docs/tokenizers/api/models#tokenizers.models.BPE). Juman++ 2.0.0-rc3 was used for pretraining.
+
+The model was trained on 8 NVIDIA A100 GPUs.
+
+
+# Acknowledgments
+
+This work was supported by Joint Usage/Research Center for Interdisciplinary Large-scale Information Infrastructures (JHPCN) through General Collaboration Project no. jh221004, "Developing a Platform for Constructing and Sharing of Large-Scale Japanese Language Models".
+
+For training models, we used the [mdx](https://mdx.jp/): a platform for the data-driven future.

config.json

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+{
+  "_name_or_path": "output",
+  "activation_function": "gelu_new",
+  "architectures": [
+    "GPT2LMHeadModel"
+  ],
+  "attn_pdrop": 0.1,
+  "bos_token_id": 1,
+  "embd_pdrop": 0.1,
+  "eos_token_id": 2,
+  "initializer_range": 0.02,
+  "layer_norm_epsilon": 1e-05,
+  "model_type": "gpt2",
+  "n_embd": 1600,
+  "n_head": 20,
+  "n_inner": null,
+  "n_layer": 48,
+  "n_positions": 1024,
+  "reorder_and_upcast_attn": false,
+  "resid_pdrop": 0.1,
+  "scale_attn_by_inverse_layer_idx": false,
+  "scale_attn_weights": true,
+  "summary_activation": null,
+  "summary_first_dropout": 0.1,
+  "summary_proj_to_labels": true,
+  "summary_type": "cls_index",
+  "summary_use_proj": true,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.20.1",
+  "use_cache": true,
+  "vocab_size": 50000
+}

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special_tokens_map.json

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+{
+  "bos_token": "<s>",
+  "eos_token": "</s>",
+  "unk_token": "<unk>"
+}

tokenizer_config.json

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+{
+  "bos_token": "<s>",
+  "eos_token": "</s>",
+  "model_max_length": 1024,
+  "name_or_path": "juman-bpe-wiki-cc100-50000",
+  "padding_side": "right",
+  "special_tokens_map_file": "juman-bpe-wiki-cc100-50000/special_tokens_map.json",
+  "tokenizer_class": "PreTrainedTokenizerFast",
+  "truncation_side": "right",
+  "unk_token": "<unk>"
+}

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