1. FastText: FastText is a library for efficient learning of word representations and sentence classification. It uses a simple yet powerful approach of representing words as bags of character n-grams, allowing it to capture both morphological and semantic information of words.

2. Flair: Flair is a framework for state-of-the-art natural language processing. It focuses on contextual word embeddings, which means it captures the meaning of a word based on its surrounding words in a sentence. Flair can be used for a variety of NLP tasks, including named entity recognition, part-of-speech tagging, and text classification.

3. Training Approach: FastText uses a supervised approach for training, where it requires labeled data to learn word representations and classify sentences. On the other hand, Flair utilizes a self-supervised approach, meaning it can train on large amounts of unlabeled data to capture contextual information without the need for explicit labels.

4. Word Embeddings: While both FastText and Flair provide word embeddings, the way they generate these embeddings differ. FastText generates word embeddings based on character n-grams, allowing it to handle out-of-vocabulary words and capture subword information. Flair, on the other hand, creates contextual word embeddings by combining the embeddings of a word with its surrounding words in the sentence, capturing the semantic meaning of words in context.

5. Language Support: FastText provides built-in support for over 150 languages, making it suitable for multilingual applications. Flair, on the other hand, has pre-trained models for a limited number of languages, mainly focused on English and some other popular languages. However, it allows for training custom models for other languages.

6. Model Size and Speed: FastText models tend to be smaller in size compared to Flair models. This is because FastText uses bag of character n-grams, which results in more compact representations. Additionally, FastText is known for its fast training and inference speed, making it suitable for large-scale applications with limited computational resources.

In Summary, FastText and Flair differ in their training approach, word embedding generation, language support, and model size/speed. FastText uses character n-grams and a supervised approach, supports more languages, and has smaller models with faster speed. Flair focuses on contextual word embeddings, uses a self-supervised approach, has limited language support (primarily English), and may have larger models with slower training and inference speed.