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version 2: trained on CC100 and Wikipedia

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Files changed (5) hide show
  1. README.md +2 -1
  2. config.json +5 -0
  3. pytorch_model.bin +2 -2
  4. tf_model.h5 +1 -1
  5. tokenizer_config.json +1 -1
README.md CHANGED
@@ -11,6 +11,7 @@ tags:
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  license: mit
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  datasets:
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  - cc100
 
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  ---
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  # japanese-gpt2-medium
@@ -36,7 +37,7 @@ model = AutoModelForCausalLM.from_pretrained("rinna/japanese-gpt2-medium")
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  A 24-layer, 1024-hidden-size transformer-based language model.
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  # Training
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- The model was trained on [Japanese CC-100](http://data.statmt.org/cc-100/ja.txt.xz) to optimize a traditional language modelling objective on 8\\*V100 GPUs for around 30 days. It reaches around 18 perplexity on a chosen validation set from the same data.
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  # Tokenization
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  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.
 
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  license: mit
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  datasets:
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  - cc100
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+ - wikipedia
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  ---
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  # japanese-gpt2-medium
 
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  A 24-layer, 1024-hidden-size transformer-based language model.
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  # Training
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+ The model was trained on [Japanese CC-100](http://data.statmt.org/cc-100/ja.txt.xz) and [Japanese Wikipedia](https://dumps.wikimedia.org/other/cirrussearch) to optimize a traditional language modelling objective on 8\\*V100 GPUs for around 30 days. It reaches around 18 perplexity on a chosen validation set from the same data.
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  # Tokenization
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  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.
config.json CHANGED
@@ -7,8 +7,10 @@
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  "bos_token_id": 1,
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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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  "n_ctx": 1024,
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  "n_embd": 1024,
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  "n_head": 16,
@@ -16,6 +18,7 @@
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  "n_layer": 24,
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  "n_positions": 1024,
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  "resid_pdrop": 0.1,
 
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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,
@@ -27,5 +30,7 @@
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  "max_length": 50
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  }
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  },
 
 
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  "vocab_size": 32000
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  }
 
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  "bos_token_id": 1,
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  "embd_pdrop": 0.1,
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  "eos_token_id": 2,
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+ "gradient_checkpointing": false,
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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_ctx": 1024,
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  "n_embd": 1024,
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  "n_head": 16,
 
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  "n_layer": 24,
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  "n_positions": 1024,
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  "resid_pdrop": 0.1,
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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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  "max_length": 50
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  }
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  },
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+ "transformers_version": "4.8.2",
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+ "use_cache": true,
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  "vocab_size": 32000
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  }
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tokenizer_config.json CHANGED
@@ -1 +1 @@
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- {"eos_token": "</s>", "unk_token": "<unk>", "pad_token": "[PAD]", "additional_special_tokens": [], "bos_token": "<s>", "cls_token": "[CLS]", "sep_token": "[SEP]", "mask_token": "[MASK]", "do_lower_case": true, "extra_ids": 0}
 
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+ {"eos_token": "</s>", "unk_token": "<unk>", "pad_token": "[PAD]", "extra_ids": 0, "additional_special_tokens": [], "sp_model_kwargs": {}, "bos_token": "<s>", "cls_token": "[CLS]", "sep_token": "[SEP]", "mask_token": "[MASK]", "do_lower_case": true, "tokenizer_class": "T5Tokenizer"}