Flair is an open-source framework for state-of-the-art Natural Language Processing (NLP) built on PyTorch. It provides a simple, unified interface for various NLP tasks like named entity recognition, sentiment analysis, part-of-speech tagging, and text classification, with robust support for multilingual models and embeddings. Currently at version 0.15.1, Flair maintains a regular release cadence, often monthly or bi-monthly, consistently adding new features and addressing bug fixes.
pip install flairVerified import paths — ran on the pinned version, not inferred.
This quickstart demonstrates how to perform Named Entity Recognition (NER) and sentiment analysis using Flair's pre-trained models. It involves creating a `Sentence` object, loading a `Classifier` for a specific task (e.g., 'ner' or 'sentiment'), and then calling `predict()` on the sentence.
Upgrade your Python environment to 3.9 or higher.
Adjust your training code: `trainer = ModelTrainer(model, corpus)` and then `trainer.train(..., optimizer=torch.optim.AdamW)`.
Upgrade Flair to the latest version (0.15.1 or newer) to ensure compatibility with recent PyTorch and SciPy releases. If you must use an older Flair version, pin your PyTorch and SciPy versions to known compatible ones (e.g., from the Flair requirements.txt of that version).
If you were using this module, it is no longer available. You may need to find an alternative clustering solution or adapt your code.
Ensure Flair is installed in your active environment: `pip install flair`.
Install PyTorch separately following the official instructions from [pytorch.org](https://pytorch.org/get-started/locally/) for your specific OS, CUDA version, and Python. Then, `pip install flair`.
Remove `optimizer` from `ModelTrainer` initialization. Pass it to the `train()` or `fine_tune()` method instead.
`trainer = ModelTrainer(model, corpus)`
`trainer.train('output_path', optimizer=torch.optim.AdamW, ...)`Ensure your target labels are cast to a floating-point type (e.g., `torch.float`) before passing them to the loss function during training, particularly if you are using binary cross-entropy or regression losses.