FastText embeddings in NLP - Model Pipeline Trace

Model Pipeline - FastText embeddings

This pipeline shows how FastText creates word embeddings by learning from text data. It breaks words into smaller parts, learns their meanings, and combines them to understand words better, even if they are new or misspelled.

Data Flow - 6 Stages

1Raw Text Input

1000 sentences x variable length→Collect raw sentences from text data→1000 sentences x variable length

"I love machine learning", "FastText helps with embeddings"

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2Text Preprocessing

1000 sentences x variable length→Lowercase, remove punctuation, tokenize words→1000 sentences x variable length tokens

["i", "love", "machine", "learning"], ["fasttext", "helps", "with", "embeddings"]

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3Subword Extraction

1000 sentences x variable length tokens→Break each word into character n-grams (subwords)→1000 sentences x variable length tokens x 3-6 char n-grams

word 'learning' -> ['lea', 'ear', 'arn', 'rni', 'nin', 'ing']

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4Embedding Lookup

1000 sentences x tokens x n-grams→Map each n-gram to a vector embedding→1000 sentences x tokens x n-grams x 300 dimensions

n-gram 'lea' -> vector of length 300

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5Embedding Aggregation

1000 sentences x tokens x n-grams x 300 dimensions→Average n-gram vectors to get word embedding→1000 sentences x tokens x 300 dimensions

word 'learning' embedding = average of its n-gram vectors

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6Sentence Embedding

1000 sentences x tokens x 300 dimensions→Average word embeddings to get sentence vector→1000 sentences x 300 dimensions

sentence embedding for 'I love machine learning'

Training Trace - Epoch by Epoch

Loss
2.5 |****
2.0 |*** 
1.5 |**  
1.0 |*   
0.5 |    
    +------------
     1 2 3 4 5 Epochs

Epoch	Loss ↓	Accuracy ↑	Observation
1	2.3	0.15	Initial training with random embeddings, loss high, accuracy low
2	1.8	0.30	Model starts learning subword patterns, loss decreases
3	1.4	0.45	Embeddings improve, better word representation
4	1.1	0.60	Model captures more semantic meaning
5	0.9	0.70	Training converges, embeddings stabilize

Prediction Trace - 4 Layers

Layer 1: Input Word

Layer 2: Subword Extraction

Layer 3: Embedding Lookup

Layer 4: Embedding Aggregation

Model Quiz - 3 Questions

Why does FastText use subword (n-gram) embeddings?

A To reduce the size of the vocabulary

B To speed up training by ignoring word order

C To understand parts of words and handle unknown words

D To translate words into multiple languages

Key Insight

FastText improves word understanding by learning from smaller parts of words, which helps it handle new or misspelled words better than traditional word embeddings.