Model Pipeline - Training Word2Vec with Gensim
This pipeline trains a Word2Vec model using Gensim to learn word meanings from sentences. It turns words into numbers that capture their relationships.
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Training Word2Vec with Gensim in NLP - Model Pipeline Trace
Data Flow - 3 Stages
1Raw Text Data
1000 sentences x variable length→Collect sentences as lists of words→1000 sentences x variable length
[['I', 'love', 'cats'], ['Cats', 'are', 'cute']]
↓
2Preprocessing
1000 sentences x variable length→Lowercase and tokenize words→1000 sentences x variable length
[['i', 'love', 'cats'], ['cats', 'are', 'cute']]
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3Word2Vec Training
1000 sentences x variable length→Train Word2Vec model with window=5, vector_size=100→Vocabulary size: 5000 words, Vector size: 100
Word vector for 'cats': [0.12, -0.05, ..., 0.33]
Training Trace - Epoch by Epoch
8.5 |*********
7.0 |******
5.5 |****
4.0 |**
3.5 |*
+----------------
1 2 3 4 5 Epochs
| Epoch | Loss ↓ | Accuracy ↑ | Observation |
|---|---|---|---|
| 1 | 8.5 | N/A | Initial training loss high as model starts learning word relations |
| 2 | 6.2 | N/A | Loss decreases as word vectors improve |
| 3 | 4.8 | N/A | Model captures better semantic relationships |
| 4 | 3.9 | N/A | Loss continues to decrease steadily |
| 5 | 3.5 | N/A | Training converges with stable loss |
Prediction Trace - 3 Layers
Layer 1: Input word
Layer 2: Embedding lookup
Layer 3: Similarity calculation
Model Quiz - 3 Questions
Test your understanding
What does the Word2Vec model learn during training?
Key Insight
Word2Vec transforms words into vectors that capture their meanings by learning from sentence contexts. Training reduces loss as the model better understands word relationships, enabling it to find similar words effectively.