update README.md

README.md

@@ -1,2 +1,76 @@
 # nagisa_bert
-A BERT model for nagisa
+
+[![Python package](https://github.com/taishi-i/nagisa_bert/actions/workflows/python-package.yml/badge.svg)](https://github.com/taishi-i/nagisa_bert/actions/workflows/python-package.yml)
+[![PyPI version](https://badge.fury.io/py/nagisa_bert.svg)](https://badge.fury.io/py/nagisa_bert)
+
+This library provides a tokenizer to use [a Japanese BERT model](https://huggingface.co/taishi-i/nagisa_bert) for [nagisa](https://github.com/taishi-i/nagisa).
+The model is available in [Transformers](https://github.com/huggingface/transformers) 🤗.
+
+## Install
+
+Python 3.7+ on Linux or macOS is required.
+You can install *nagisa_bert* by using the *pip* command.
+
+
+```bash
+$ pip install nagisa_bert
+```
+
+## Usage
+
+This model is available in Transformer's pipeline method.
+
+```python
+>>> from transformers import pipeline
+>>> from nagisa_bert import NagisaBertTokenizer
+
+>>> text = "nagisaで[MASK]できるモデルです"
+>>> tokenizer = NagisaBertTokenizer.from_pretrained("taishi-i/nagisa_bert")
+>>> fill_mask = pipeline("fill-mask", model='taishi-i/nagisa_bert', tokenizer=tokenizer)
+>>> print(fill_mask(text))
+[{'score': 0.1385931372642517,
+  'sequence': 'nagisa で 使用 できる モデル です',
+  'token': 8092,
+  'token_str': '使 用'},
+ {'score': 0.11947669088840485,
+  'sequence': 'nagisa で 利用 できる モデル です',
+  'token': 8252,
+  'token_str': '利 用'},
+ {'score': 0.04910655692219734,
+  'sequence': 'nagisa で 作成 できる モデル です',
+  'token': 9559,
+  'token_str': '作 成'},
+ {'score': 0.03792576864361763,
+  'sequence': 'nagisa で 購入 できる モデル です',
+  'token': 9430,
+  'token_str': '購 入'},
+ {'score': 0.026893319562077522,
+  'sequence': 'nagisa で 入手 できる モデル です',
+  'token': 11273,
+  'token_str': '入 手'}]
+```
+
+Tokenization and vectorization.
+
+```python
+>>> from transformers import BertModel
+>>> from nagisa_bert import NagisaBertTokenizer
+
+>>> text = "nagisaで[MASK]できるモデルです"
+>>> tokenizer = NagisaBertTokenizer.from_pretrained("taishi-i/nagisa_bert")
+>>> tokens = tokenizer.tokenize(text)
+>>> print(tokens)
+['na', '##g', '##is', '##a', 'で', '[MASK]', 'できる', 'モデル', 'です']
+
+>>> model = BertModel.from_pretrained("taishi-i/nagisa_bert")
+>>> h = model(**tokenizer(text, return_tensors="pt")).last_hidden_state
+>>> print(h)
+tensor([[[-0.2912, -0.6818, -0.4097,  ...,  0.0262, -0.3845,  0.5816],
+         [ 0.2504,  0.2143,  0.5809,  ..., -0.5428,  1.1805,  1.8701],
+         [ 0.1890, -0.5816, -0.5469,  ..., -1.2081, -0.2341,  1.0215],
+         ...,
+         [-0.4360, -0.2546, -0.2824,  ...,  0.7420, -0.2904,  0.3070],
+         [-0.6598, -0.7607,  0.0034,  ...,  0.2982,  0.5126,  1.1403],
+         [-0.2505, -0.6574, -0.0523,  ...,  0.9082,  0.5851,  1.2625]]],
+       grad_fn=<NativeLayerNormBackward0>)
+```