GloVe and FastText Embeddings
Global co-occurrence statistics, subword FastText, loading pretrained embeddings.
GloVe and FastText Embeddings is a free Learn AI with Python lesson on CoddyKit — lesson 2 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the Learn AI with Python learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.
Beyond Word2Vec
Word2Vec learns from local context windows. Two other influential embeddings take different approaches: GloVe uses global statistics, and FastText uses character pieces.
What Is GloVe
GloVe (Global Vectors) is trained on a global co-occurrence matrix: how often each pair of words appears together across the whole corpus, not just local windows.
Why Global Statistics Help
By factorizing the full co-occurrence matrix, GloVe captures corpus-wide relationships and ratios between words, often producing high-quality vectors that rival or beat Word2Vec on analogy tasks.
Loading Pretrained Vectors
Training embeddings needs huge corpora, so we usually download pretrained ones. gensim.downloader fetches popular models like GloVe vectors with one line.
import gensim.downloader as api
glove = api.load("glove-wiki-gigaword-100")
print(glove["computer"].shape) # (100,)Using Pretrained GloVe
Once loaded, GloVe vectors support the same operations as Word2Vec: similarity and analogies.
import gensim.downloader as api
glove = api.load("glove-wiki-gigaword-100")
print(glove.most_similar("king", topn=3))
print(glove.most_similar(positive=["king", "woman"], negative=["man"], topn=1))The Out-of-Vocabulary Problem
Word2Vec and GloVe assign vectors only to words seen in training. A new or misspelled word (out-of-vocabulary, OOV) has no vector, a real limitation for messy text.
What Is FastText
FastText solves OOV by representing each word as a bag of character n-grams. The word "playing" includes pieces like "pla", "lay", "ing", so it shares structure with related words.
How Character N-grams Help
Because a word vector is built from its sub-pieces, FastText can construct a vector for an unseen word from its n-grams. It also captures morphology, helping with prefixes, suffixes, and rare words.
Training FastText
gensim provides FastText with an API like Word2Vec. The min_n and max_n parameters set the character n-gram range.
from gensim.models import FastText
model = FastText(
sentences,
vector_size=100,
window=5,
min_count=1,
min_n=3,
max_n=6,
)FastText Handles OOV
Even a word never seen in training returns a vector, assembled from its character n-grams. This is FastText signature advantage over Word2Vec and GloVe.
from gensim.models import FastText
model = FastText(sentences, vector_size=100, min_n=3, max_n=6, min_count=1)
# works even if "catlike" was never in training
vec = model.wv["catlike"]Choosing an Embedding
Use GloVe or Word2Vec for clean text with a fixed vocabulary. Choose FastText for morphologically rich languages, noisy text, or when OOV words are common. Pretrained vectors are a great starting point in all cases.
Quick Check
Test your embedding knowledge.
Recap
Recap: GloVe learns from global co-occurrence statistics; FastText uses character n-grams to handle out-of-vocabulary and morphology. Load pretrained vectors with gensim.downloader.load. Both support similarity and analogies. Choose FastText for noisy or morphologically rich text, GloVe/Word2Vec for clean fixed vocabularies.
Frequently asked questions
Is the “GloVe and FastText Embeddings” lesson free?
Yes — the full text of “GloVe and FastText Embeddings” is free to read here on the web, and the Learn AI with Python course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the Learn AI with Python course, upgrade to CoddyKit PRO.
What will I learn in “GloVe and FastText Embeddings”?
Global co-occurrence statistics, subword FastText, loading pretrained embeddings. You practise Learn AI with Python with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.
Do I need any experience to start Learn AI with Python?
No prior experience is required. Learn AI with Python on CoddyKit is structured for beginners through advanced learners; this is — lesson 2 of 4, so you can start here or from the beginning and move at your own pace.
How long does the “GloVe and FastText Embeddings” lesson take?
Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.
Can I write and run code in this Learn AI with Python lesson?
Yes. Every Learn AI with Python lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.
All lessons in this course
- Word2Vec: Skip-gram and CBOW
- GloVe and FastText Embeddings
- Text Classification with BERT
- Semantic Similarity and Sentence Embeddings