update

compression_app.py

@@ -28,7 +28,7 @@ from compression_util import get_compression_leaderboard, common_corpuses
 docs = """## 📖 What is a good tokenizer?
 
 From a compression perspective, a good tokenizer should be lossless,
-and keep high compression rate (fewer tokens for given text).
+and keep high compression rate (fewer tokens for given text). <br>
 The encoding and decoding process can be formulated as
 ```python
     token_ids = tokenizer.encode(input_text)    # compressed tokens
@@ -40,7 +40,7 @@ Lossless tokenization preserves the exact original text, i.e. `decoded_text = in
 
   - Most lossy tokenizers get many out-of-vocabulary tokens. 👉 Check the [oov of bert-base-uncased](https://huggingface.co/spaces/eson/tokenizer-arena/blob/main/stats/compression_rate/google-bert.bert-base-cased%20%40%20cc100.zh-Hans.diff.json).
   - Some other tokenizers have no oov, but still be lossy due to text normalization. For example qwen performs [unicode normalization](https://github.com/huggingface/transformers/blob/v4.42.3/src/transformers/models/qwen2/tokenization_qwen2.py#L338),
-    which may bring some [slight difference](https://huggingface.co/spaces/eson/tokenizer-arena/raw/main/stats/compression_rate/Qwen.Qwen1.5-1.8B%20@%20cc100.ja.diff.json) to the reconstructed text. 
+    which may bring some [slight differences](https://huggingface.co/spaces/eson/tokenizer-arena/raw/main/stats/compression_rate/Qwen.Qwen1.5-1.8B%20@%20cc100.ja.diff.json) to the reconstructed text. 
 
 - **Compression Rate** <br>
 There are mainly two types of metric to represent the `input_text`: