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Model: nlp-waseda/gpt2-small-japanese Source: Original Platform
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README.md
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README.md
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---
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language:
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- ja
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license: cc-by-sa-4.0
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datasets:
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- wikipedia
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- cc100
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widget:
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- text: "早稲田 大学 で 自然 言語 処理 を"
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---
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# nlp-waseda/gpt2-small-japanese
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This model is Japanese GPT-2 pretrained on Japanese Wikipedia and CC-100.
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## Intended uses & limitations
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You can use the raw model for text generation or fine-tune it to a downstream task.
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Note that the texts should be segmented into words using Juman++ in advance.
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### How to use
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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:
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```python
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>>> from transformers import pipeline, set_seed
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>>> generator = pipeline('text-generation', model='nlp-waseda/gpt2-small-japanese')
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>>> set_seed(42)
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>>> 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)
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[{'generated_text': '早稲田 大学 で 自然 言語 処理 を 学び 、 帰国 後 、 早稲田 大学 理工 学部 に 入学 し ます 。 卒業 後 、 早稲田 大学 工学 研究 科 、'},
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{'generated_text': '早稲田 大学 で 自然 言語 処理 を 学び 、 アメリカ の 大学 で 学士 号 を 取得 、 修士 の 取得 で 博士 号 を 取得 。 2008 年'},
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{'generated_text': '早稲田 大学 で 自然 言語 処理 を 勉強 して い ます 。 学部 は 日本 語 学科 を 専攻 して い ます 。 英語 が 話せる と いう'},
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{'generated_text': '早稲田 大学 で 自然 言語 処理 を 専攻 して いた 。 2011 年 に 第 26 回 日本 化学 会 学生 委員 会 奨励 賞 ( 第 2 年次 審査'},
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{'generated_text': '早稲田 大学 で 自然 言語 処理 を 中心 と する 言語 学 研究 を 行って いる 。 東京 都 ・ 豊島 区 の お 見合い 相手 。'}]
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```
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Here is how to use this model to get the features of a given text in PyTorch:
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```python
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from transformers import ReformerTokenizer, GPT2Model
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tokenizer = ReformerTokenizer.from_pretrained('nlp-waseda/gpt2-small-japanese')
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model = GPT2Model.from_pretrained('nlp-waseda/gpt2-small-japanese')
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text = "早稲田 大学 で 自然 言語 処理 を"
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encoded_input = tokenizer(text, return_tensors='pt')
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output = model(**encoded_input)
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```
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## Training data
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The GPT-2 model was pretrained on Japanese Wikipedia, dumped on 2022-03-20, and the Japanese portion of CC-100.
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## Training procedure
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### Preprocessing
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The texts are normalized using zenhan, segmented into words using Juman++, and tokenized using SentencePiece. Juman++ 2.0.0-rc3 was used for pretraining.
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The model was trained on 8 NVIDIA A100 GPUs.
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config.json
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config.json
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{
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"activation_function": "gelu_new",
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"architectures": [
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"GPT2LMHeadModel"
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],
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"attn_pdrop": 0.1,
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"bos_token_id": 2,
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"embd_pdrop": 0.1,
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"eos_token_id": 2,
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"initializer_range": 0.02,
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"layer_norm_epsilon": 1e-05,
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"model_type": "gpt2",
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"n_embd": 768,
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"n_head": 12,
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"n_inner": null,
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"n_layer": 12,
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"n_positions": 1024,
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"reorder_and_upcast_attn": false,
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"resid_pdrop": 0.1,
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"scale_attn_by_inverse_layer_idx": false,
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"scale_attn_weights": true,
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"summary_activation": null,
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"summary_first_dropout": 0.1,
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"summary_proj_to_labels": true,
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"summary_type": "cls_index",
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"summary_use_proj": true,
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"torch_dtype": "float32",
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"transformers_version": "4.18.0.dev0",
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"use_cache": true,
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"vocab_size": 32000
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}
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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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special_tokens_map.json
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{"eos_token": "</s>", "unk_token": "<unk>"}
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spiece.model
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tokenizer.json
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{"eos_token": "</s>", "unk_token": "<unk>", "additional_special_tokens": [], "sp_model_kwargs": {}, "tokenizer_class": "ReformerTokenizer"}
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