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README.md

@@ -1,3 +1,96 @@
 ---
-license: cc-by-3.0
+license: cc-by-sa-3.0
+language: ja
+inference: false
 ---
+
+# LayoutLM-wikipedia-ja Model
+
+This is a [LayoutLM](https://doi.org/10.1145/3394486.3403172) model pretrained on texts in the Japanese language.
+
+## Model Details
+
+### Model Description
+
+- **Developed by:** Advanced Technology Laboratory, The Japan Research Institute, Limited.
+- **Model type:** LayoutLM
+- **Language:** Japanese
+- **License:** [CC BY-SA 3.0](https://creativecommons.org/licenses/by-sa/3.0/)
+- **Finetuned from model:** [cl-tohoku/bert-base-japanese-v2](https://huggingface.co/cl-tohoku/bert-base-japanese-v2)
+
+## Uses
+
+The model is primarily aimed at being fine-tuned on a token classification task. You can use the raw model for masked language modeling, although it is not the primary use case. Refer to <https://github.com/nishiwakikazutaka/shinra2022-task2_jrird> for instructions on how to fine-tune the model. Note that the linked repository is written in Japanese.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+```python
+>>> from transformers import AutoTokenizer, AutoModel
+>>> import torch
+
+>>> tokenizer = AutoTokenizer.from_pretrained("jri-advtechlab/layoutlm-wikipedia-ja")
+>>> model = AutoModel.from_pretrained("jri-advtechlab/layoutlm-wikipedia-ja")
+
+>>> tokens = tokenizer.tokenize("こんにちは")  # ['こん', '##にち', '##は']
+>>> normalized_token_boxes = [[637, 773, 693, 782], [693, 773, 749, 782], [749, 773, 775, 782]]
+>>> # add bounding boxes of cls + sep tokens
+>>> bbox = [[0, 0, 0, 0]] + normalized_token_boxes + [[1000, 1000, 1000, 1000]]
+
+>>> input_ids = [tokenizer.cls_token_id] \
+                + tokenizer.convert_tokens_to_ids(tokens) \
+                + [tokenizer.sep_token_id]
+>>> attention_mask = [1] * len(input_ids)
+>>> token_type_ids = [0] * len(input_ids)
+>>> encoding = {
+    "input_ids": torch.tensor([input_ids]),
+    "attention_mask": torch.tensor([attention_mask]),
+    "token_type_ids": torch.tensor([token_type_ids]),
+    "bbox": torch.tensor([bbox]),
+    }
+
+>>> outputs = model(**encoding)
+```
+
+## Training Details
+
+### Training Data
+
+The model is trained on the Japanese version of Wikipedia. The training corpus is distributed as [training data of the SHINRA 2022 shared task](https://2022.shinra-project.info/data-download#subtask-common).
+
+### Tokenization and Localization
+
+We used the tokenizer of [cl-tohoku/bert-base-japanese-v2](https://huggingface.co/cl-tohoku/bert-base-japanese-v2) to split texts into tokens (subwords). Each token is wrapped in a `<span>` tag with the no-wrap value set for the white-space property and localized by obtaining `BoundingClientRect`. The localization process was conducted with Google Chrome (106.0.5249.119) headless mode on Ubuntu 20.04.5 LTS with a 1,280*854 window size.
+
+The vocabulary is the same as [cl-tohoku/bert-base-japanese-v2](https://huggingface.co/cl-tohoku/bert-base-japanese-v2).
+
+### Training Procedure
+
+The model was trained using Masked Visual-Language Model (MVLM), but it was not trained using Multi-label Document Classification (MDC). We made this decision because we did not identify significant visual differences, such as those between a contract and an invoice, between the different Wikipedia articles.
+
+#### Preprocessing
+
+All parameters except the 2-D Position Embedding were initialized with weights from [cl-tohoku/bert-base-japanese-v2](https://huggingface.co/cl-tohoku/bert-base-japanese-v2). We initialized the 2-D Position Embedding with random values.
+
+#### Training Hyperparameters
+
+The model was trained on 8 NVIDIA A100 SXM4 GPUs for 100,000 steps, with a batch size of 256 with a maximum sequence length of 512. The optimizer used is Adam with a learning rate of 5e-5, &beta;<sub>1</sub>=0.9, &beta;<sub>2</sub>=0.999, learning rate warmup for 1,000 steps, and linear decay of the learning rate after. Additionally, we utilized fp16 mixed precision during training. The training took about 5.3 hours to finish.
+
+## Evaluation
+
+Our fine-tuned model achieved a macro-f1 score of 55.1451 on the leaderboard for the SHINRA 2022 shared task. You can check the leaderboard at [https://2022.shinra-project.info/#leaderboard](https://2022.shinra-project.info/#leaderboard) for detailed information.
+
+## Citation
+
+**BibTeX:**
+
+```tex
+@inproceedings{nishiwaki2023layoutlm-wiki-ja,
+  title = {日本語情報抽出タスクのための{L}ayout{LM}モデルの評価},
+  author = {西脇一尊 and 大沼俊輔 and 門脇一真},
+  booktitle = {言語処理学会第29回年次大会(NLP2023)予稿集},
+  year = {2023},
+  pages = {522--527}
+}
+```

config.json

@@ -0,0 +1,27 @@
+{
+  "_name_or_path": "layoutlm-wikipedia-ja",
+  "architectures": [
+    "LayoutLMForMaskedLM"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-12,
+  "max_2d_position_embeddings": 1024,
+  "max_position_embeddings": 512,
+  "model_type": "layoutlm",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "output_past": true,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.20.1",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 32768
+}

model.safetensors

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+size 457434408

pytorch_model.bin

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special_tokens_map.json

@@ -0,0 +1,7 @@
+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

tokenizer_config.json

@@ -0,0 +1,14 @@
+{
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": false,
+  "mask_token": "[MASK]",
+  "name_or_path": "layoutlm-wikipedia-ja",
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": false,
+  "tokenize_chinese_chars": false,
+  "tokenizer_class": "LayoutLMTokenizer",
+  "unk_token": "[UNK]"
+}