Janome is a Japanese morphological analysis engine (or tokenizer, POS-tagger) written in pure Python, including a built-in dictionary and language model. It aims to be easy to install and provides concise, well-designed APIs for various Python applications. Janome uses mecab-ipadic-2.7.0-20070801 as its built-in dictionary. The current version is 0.5.0, released in July 2023, with a release cadence of approximately 6-18 months between major versions.
pip install janomeVerified import paths — ran on the pinned version, not inferred.
Initializes the Tokenizer and processes a Japanese sentence, printing each token with its morphological information. An example for 'wakati-gaki' (word segmentation) mode is also included, which returns only surface forms.
Ensure adequate RAM (e.g., 2GB or more) is available during installation. For 32-bit environments, newer versions (0.2.6+) are more optimized.
Be aware that code using `Analyzer` might require adjustments in subsequent major versions. Refer to release notes for API changes.
Upgrade to Janome 0.4.2 or later to ensure deterministic tokenization.
Upgrade to Janome 0.4.2 or later, which ensures the system dictionary is a singleton, preventing this resource exhaustion.
For memory optimization, use `t = Tokenizer(wakati=True)` if you exclusively need word segmentation. Otherwise, default to `Tokenizer()` and pass `wakati=True` to `tokenize()` method when needed.
Ensure your environment has at least 2GB of free RAM before running `pip install janome`. If on a resource-constrained system, consider increasing swap space or using a more powerful machine for installation.
First, verify installation with `pip show janome`. If not installed, run `pip install janome`. Ensure you are importing `Tokenizer` from `janome.tokenizer` as shown in the quickstart, not directly from `janome`.
If you need `Token` objects with full morphological details, do not pass `wakati=True` to the `tokenize()` method or the `Tokenizer` constructor. If you *do* want `wakati-gaki` (list of strings), process the output as strings. Example: `for word in t.tokenize(text, wakati=True): print(word)`.